diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 52a52954..21606076 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,6 +1,13 @@ Change Log ========== +v.2.4.1 +---------- +- Speed up eigenvalue parsing by using the faster ``doped`` site-matching functions rather than ``MakeDefectStructureInfo`` from ``pydefect`` +- Minor efficiency & robustness updates. +- Minor docs & tutorials updates +- Minor tests updates + v.2.4.0 ---------- - Electronic structure analysis by @adair-nicolson & @kavanase: diff --git a/README.md b/README.md index ad1fe6a6..63419cf3 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,8 @@ All features and functionality are fully-customisable: ### Performance and Example Outputs ![https://github.com/openjournals/joss-reviews/issues/6433](docs/JOSS/doped_JOSS_figure.png) -**a.** Optimal supercell generation comparison. **b.** Charge state estimation comparison. Example **(c)** Kumagai-Oba (eFNV) finite-size correction plot, **(d)** defect formation energy diagram, **(e)** chemical potential / stability region, **(f)** Fermi level vs. annealing temperature, **(g)** defect/carrier concentrations vs. annealing temperature and **(h)** Fermi level / carrier concentration heatmap plots from `doped`. See the [JOSS paper](https://github.com/openjournals/joss-reviews/issues/6433) for more details. +**(a)** Optimal supercell generation comparison. **(b)** Charge state estimation comparison. Example **(c)** Kumagai-Oba (eFNV) finite-size correction plot, **(d)** defect formation energy diagram, **(e)** chemical potential / stability region, **(f)** Fermi level vs. annealing temperature, **(g)** defect/carrier concentrations vs. annealing temperature and **(h)** Fermi level / carrier concentration heatmap plots from `doped`. Automated plots of **(i,j)** single-particle eigenvalues and **(k)** site +displacements from DFT supercell calculations. See the [JOSS paper](https://github.com/openjournals/joss-reviews/issues/6433) for more details. ## Installation diff --git a/docs/Contributing.rst b/docs/Contributing.rst index 6e15b287..11696e79 100644 --- a/docs/Contributing.rst +++ b/docs/Contributing.rst @@ -23,6 +23,6 @@ run tests and add new tests for any new features whenever submitting pull reques .. NOTE:: If you run into any issues using ``doped`` that aren't addressed on the - [Troubleshooting](https://doped.readthedocs.io/en/latest/Troubleshooting.html) page, please contact + `Troubleshooting `_ page, please contact the developers through the ``GitHub`` `Issues `_ page, or by email. diff --git a/docs/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png b/docs/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png index 059f9c04..c4c06fdf 100644 Binary files a/docs/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png and b/docs/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png differ diff --git a/docs/Dev_ToDo.md b/docs/Dev_ToDo.md index 85bf63bd..654c5143 100644 --- a/docs/Dev_ToDo.md +++ b/docs/Dev_ToDo.md @@ -1,9 +1,9 @@ # `doped` Development To-Do List ## Chemical potential -- Check through chemical potential TO-DOs. Need to recheck validity of approximations used for extrinsic competing phases (and code for this). +- Check through chemical potential TO-DOs. Need to recheck validity of approximations used for extrinsic competing phases (and code for this). Proper `vasp_std` setup (with `NKRED` folders like for defect calcs) and `vasp_ncl` generation. - Efficient generation of competing phases for which there are many polymorphs? See SK notes from CdTe competing phases. - Update chemical potential tools to work with new Materials Project API. Currently, supplying an API key for the new Materials Project API returns entries which do not have `e_above_hull` as a property, and so crashes. Ideally would be good to be compatible with both the legacy and new API, which should be fairly straightforward (try importing MPRester from mp_api client except ImportError import from pmg then will need to make a whole separate query/search because `band_gap` and `total_magnetisation` no longer accessible from `get_entries`). See https://docs.materialsproject.org/downloading-data/using-the-api -- Publication ready chemical potential diagram plotting tool as in Adam Jackson's `plot-cplap-ternary` (3D) and Sungyhun's `cplapy` (4D) (see `doped_chempot_plotting_example.ipynb`; code there, just needs to be implemented in module functions). `ChemicalPotentialGrid` in `py-sc-fermi` interface could be quite useful for this? (Worth moving that part of code out of `interface` subpackage?) +- Publication ready chemical potential diagram plotting tool as in Adam Jackson's `plot-cplap-ternary` (3D) and Sungyhun's `cplapy` (4D) (see `doped_chempot_plotting_example.ipynb`; code there, just needs to be implemented in module functions). `ChemicalPotentialGrid` in `py-sc-fermi` interface could be quite useful for this? (Worth moving that part of code out of `interface` subpackage?) Also can see https://github.com/materialsproject/pymatgen-analysis-defects/blob/main/pymatgen/analysis/defects/plotting/phases.py for 2D chempot plotting. - Also see `Cs2SnTiI6` notebooks for template code for this. - Note in tutorial that LaTeX table generator website can also be used with the `to_csv()` function to generate LaTeX tables for the competing phases. @@ -14,8 +14,7 @@ - Should have `ncols` as an optional parameter for the function, and auto-set this to 2 if the legend height exceeds that of the plot - Don't show transition levels outside of the bandgap (or within a certain range of the band edge, possibly using `pydefect` delocalisation analysis?), as these are shallow and not calculable with the standard supercell approach. - Option for degeneracy-weighted ('reduced') formation energy diagrams, similar to reduced energies in SOD. See Slack discussion and CdTe pyscfermi notebooks. Would be easy to implement if auto degeneracy handling implemented. - - Could also add an optional right-hand-side y-axis for defect concentration (for a chosen anneal temp) to our TLD plotting (e.g. `concentration_T = None`) as done for thesis, noting in docstring that this obvs doesn't account for degeneracy! - - Also carrier concentration vs Fermi level plots as done in the Kumagai PRX paper? With this, should also mention that DOS calcs should be well converged wrt kpoints for accurate Fermi level prediction. + - Could also add an optional right-hand-side y-axis for defect concentration (for a chosen anneal temp) to our TLD plotting (e.g. `concentration_T = None`) as done for thesis, noting in docstring that this obvs doesn't account for degeneracy! - Also see Fig. 6a of the `AiiDA-defects` preprint, want plotting tools like this - Can we add an option to give the `pydefect` defect-structure-info output (shown here https://kumagai-group.github.io/pydefect/tutorial.html#check-defect-structures) – seems quite useful tbf @@ -38,9 +37,8 @@ and/or if it's known from other work for your chosen functional etc.) - Show our workflow for calculating interstitials (see docs Tips page, i.e. `vasp_gam` relaxations first (can point to defects tutorial for this)) -> Need to mention this in the defects tutorial, and point to discussion in Tips docs page. - Add mini-example of calculating the dielectric constant (plus convergence testing with `vaspup2.0`) to docs/examples, and link this when `dielectric` used in parsing examples. Should also note that the dielectric should be in the same xyz Cartesian basis as the supercell calculations (likely but not necessarily the same as the raw output of a VASP dielectric calculation if an oddly-defined primitive cell is used) - - Note about cost of `vasp_ncl` chemical potential calculations for metals, use `ISMEAR = -5`, - possibly `NKRED` etc. (make a function to generate `vasp_ncl` calculation files with `ISMEAR = -5`, with option to set different kpoints) - if `ISMEAR = 0` - converged kpoints still prohibitively large, use vasp_converge_files again to check for quicker convergence with ISMEAR = -5. - - Use `NKRED = 2` for `vasp_ncl` chempot calcs, if even kpoints and over 4. Often can't use `NKRED` with `vasp_std`, because we don't know beforehand the kpts in the IBZ (because symmetry on for `vasp_std` chempot calcs)(same goes for `EVENONLY = True`). + - `vasp_ncl` chemical potential calculations for metals, use `ISMEAR = -5`, possibly `NKRED` etc. (make a function to generate `vasp_ncl` calculation files with `ISMEAR = -5`, with option to set different kpoints) - if `ISMEAR = 0` - converged kpoints still prohibitively large, use vasp_converge_files again to check for quicker convergence with ISMEAR = -5. + - Often can't use `NKRED` with `vasp_std`, because we don't know beforehand the kpts in the IBZ (because symmetry on for `vasp_std` chempot calcs)(same goes for `EVENONLY = True`). - Readily-usable in conjunction with `atomate`, `AiiDA`(-defects), `vise`, `CarrierCapture`, and give some quick examples? Add as optional dependencies. - Workflow diagram with: https://twitter.com/Andrew_S_Rosen/status/1678115044348039168?s=20 @@ -57,6 +55,8 @@ 85.115104) suggests that the SOC effects on total energy cancel out for chemical potential calculations, but only the case when the occupation of the SOC-affected orbitals is constant (typically not the case)) Better to do consistently (link Emily SOC work and/or thesis). + - But, can generally use non-SOC energies to reliably determine relative energies of polymorphs of the same composition (oxidation states), to good accuracy. + - Also, can use symmetry with SOC total energy calculations, have tested this. - Link to Irea review, saying that while spin and configurational degeneracies are accounted for automatically in `doped`, excited-state degeneracy (e.g. with bipolarons/dimers with single and triplet states) are not, so the user should manually account for this if present. Also note that diff --git a/docs/Future_ToDo.md b/docs/Future_ToDo.md index b6741c9b..875579b6 100644 --- a/docs/Future_ToDo.md +++ b/docs/Future_ToDo.md @@ -31,6 +31,7 @@ workflow which ppl often mess up. Can use modified code from `config-coord-plots` (but actually to scale and automatically/sensibly parsed etc.)(also see `CarrierCapture` functionalities) - Dielectric/kpoint-sampling weighted supercell generation? (essentially just a vectorised cost function implemented in the generation loop). Would natively optimise e.g. layered materials quite well. +- `doped`/`SnB`/`easyunfold` (virtual) workshop? Just noting as a possibility, could be MCC-supported. ## Chemical Potentials - Overhaul chemical potentials code, dealing with all `TODO`s in that module. diff --git a/docs/Installation.rst b/docs/Installation.rst index eb9dac43..52ca59b7 100644 --- a/docs/Installation.rst +++ b/docs/Installation.rst @@ -108,4 +108,5 @@ the ``doped`` GitHub repository: Requirements ------------- -``doped`` requires ``pymatgen>=2022.10.22`` and its dependencies. \ No newline at end of file +The ``doped`` dependencies are listed in the ``pyproject.toml`` file on +`the GitHub repository `__. \ No newline at end of file diff --git a/docs/JOSS/doped_JOSS_figure.png b/docs/JOSS/doped_JOSS_figure.png index d677a2e1..dc44b967 100644 Binary files a/docs/JOSS/doped_JOSS_figure.png and b/docs/JOSS/doped_JOSS_figure.png differ diff --git a/docs/JOSS/paper.bib b/docs/JOSS/paper.bib index de4c527b..3956a28c 100644 --- a/docs/JOSS/paper.bib +++ b/docs/JOSS/paper.bib @@ -1,5 +1,5 @@ @article{cen_cation_2023, - title = {Cation Disorder Dominates the Defect Chemistry of High-Voltage {{LiMn}} 1.5 {{Ni}} 0.5 {{O}} 4 ({{LMNO}}) Spinel Cathodes}, + title = {Cation Disorder Dominates the Defect Chemistry of High-Voltage {{LiMn}}{\textsubscript{1.5}}{{Ni}}{\textsubscript{0.5}}{{O}}{\textsubscript{4}}({{LMNO}}) Spinel Cathodes}, author = {Cen, Jiayi and Zhu, Bonan and R.~Kavanagh, Se{\'a}n and G.~Squires, Alexander and O.~Scanlon, David}, year = {2023}, journal = {Journal of Materials Chemistry A}, @@ -29,12 +29,12 @@ @article{choi_intrinsic_2023 file = {/Users/kavanase/Zotero/storage/DIGWGHAD/Choi et al_2023_Intrinsic Defects and Their Role in the Phase Transition of Na-Ion Anode.pdf} } -@misc{dou_giant_2024, - title = {Giant {{Band Degeneracy}} via {{Orbital Engineering Enhances Thermoelectric Performance}} from {{Sb2Si2Te6}} to {{Sc2Si2Te6}}}, +@article{dou_giant_2024, + title = {Giant {{Band Degeneracy}} via {{Orbital Engineering Enhances Thermoelectric Performance}} from {{Sb{\textsubscript{2}}Si{\textsubscript{2}}Te{\textsubscript{6}}}} to {{Sc{\textsubscript{2}}Si{\textsubscript{2}}Te{\textsubscript{6}}}}}, author = {Dou, Wenzhen and Spooner, Kieran and Kavanagh, Se{\'a}n and Zhou, Miao and Scanlon, David O.}, year = {2024}, month = jan, - publisher = {{ChemRxiv}}, + journal = {{ChemRxiv}}, doi = {10.26434/chemrxiv-2024-hm6vh}, urldate = {2024-01-18}, abstract = {The complex interrelationships among thermoelectric parameters mean that a priori design of high-performing materials is difficult. However, band engineering can allow the power factor to be optimized through enhancement of the Seebeck coefficient. Herein, using layered Sb2Si2Te6 and Sc2Si2Te6 as model systems, we comprehensively investigate and compare their thermoelectric properties by employing density functional theory combined with semiclassical Boltzmann transport theory. Our simulations reveal that Sb2Si2Te6 exhibits superior electrical conductivity compared to Sc2Si2Te6 due to lower scattering rates and more pronounced band dispersion. Remarkably, despite Sb2Si2Te6 exhibiting a lower lattice thermal conductivity, the introduction of Sc-d orbitals dramatically increases conduction band degeneracy in Sc2Si2Te6, yielding a significantly improved Seebeck coefficient relative to Sb2Si2Te6. As a result, Sc2Si2Te6 is predicted to achieve an extraordinary dimensionless figure of merit (ZT) of 3.51 at 1000 K, which significantly surpasses the predicted maximum ZT of 2.76 for Sb2Si2Te6 at 900 K. This work suggests that engineering band degeneracy through compositional variation is an effective strategy for improving the thermoelectric performance of layered materials.}, @@ -82,7 +82,7 @@ @article{hyde_lithium_2023 } @article{kavanagh_frenkel_2022, - title = {Frenkel {{Excitons}} in {{Vacancy-Ordered Titanium Halide Perovskites}} ({{Cs2TiX6}})}, + title = {Frenkel {{Excitons}} in {{Vacancy-Ordered Titanium Halide Perovskites}} ({{Cs{\textsubscript{2}}TiX{\textsubscript{6}}}})}, author = {Kavanagh, Se{\'a}n R. and Savory, Christopher N. and Liga, Shanti M. and Konstantatos, Gerasimos and Walsh, Aron and Scanlon, David O.}, year = {2022}, month = dec, @@ -151,7 +151,7 @@ @article{kim_carriercapturejl_2020 } @article{krajewska_enhanced_2021, - title = {Enhanced Visible Light Absorption in Layered {{Cs}}{\textsubscript{3}}{{Bi}}{\textsubscript{2}}{{Br}}{\textsubscript{9}} through Mixed-Valence {{Sn}}({\textsc{Ii}})/{{Sn}}({\textsc{Iv}}) Doping}, + title = {Enhanced Visible Light Absorption in Layered {{Cs}}{\textsubscript{3}}{{Bi}}{\textsubscript{2}}{{Br}}{\textsubscript{9}} through Mixed-Valence {{Sn}}({\textsc{II}})/{{Sn}}({\textsc{IV}}) Doping}, shorttitle = {Enhanced Visible Light Absorption in Layered {{Cs}} {\textsubscript{3}} {{Bi}} {\textsubscript{2}} {{Br}} {\textsubscript{9}} through Mixed-Valence {{Sn}}(}, author = {Krajewska, Chantalle J. and Kavanagh, Se{\'a}n R. and Zhang, Lina and Kubicki, Dominik J. and Dey, Krishanu and Ga{\l}kowski, Krzysztof and Grey, Clare P. and Stranks, Samuel D. and Walsh, Aron and Scanlon, David O. and Palgrave, Robert G.}, year = {2021}, @@ -203,24 +203,26 @@ @article{larsen_atomic_2017 file = {/Users/kavanase/Zotero/storage/NG4MIPJX/Larsen et al_2017_The atomic simulation environment—a Python library for working with atoms.pdf} } -@misc{li_computational_2023, - title = {Computational {{Prediction}} of an {{Antimony-based}} n-Type {{Transparent Conducting Oxide}}: {{F-doped Sb2O5}}}, - shorttitle = {Computational {{Prediction}} of an {{Antimony-based}} n-Type {{Transparent Conducting Oxide}}}, +@article{li_computational_2024, + title = {Computational {{Prediction}} of an {{Antimony-Based}} n-{{Type Transparent Conducting Oxide}}: {{F-Doped Sb{\textsubscript{2}}O{\textsubscript{5}}}}}, + shorttitle = {Computational {{Prediction}} of an {{Antimony-Based}} n-{{Type Transparent Conducting Oxide}}}, author = {Li, Ke and Willis, Joe and Kavanagh, Se{\'a}n R. and Scanlon, David O.}, - year = {2023}, - month = dec, - publisher = {{ChemRxiv}}, - doi = {10.26434/chemrxiv-2023-8l7pb}, - urldate = {2024-01-18}, - abstract = {Transparent conducting oxides (TCOs) possess a unique combination of optical transparency and electrical conductivity, making them indispensable in optoelectronic applications. However, the heavy dependence on a small number of established materials limits the range of devices they can support. The discovery and development of additional wide bandgap oxides that can be doped to display metallic-like conductivity is therefore necessary. In this work, we use hybrid density functional theory to identify a binary Sb(V) system, Sb2O5, as a promising TCO with high conductivity and transparency when doped with fluorine. We have conducted a full point defect analysis, finding F-doped Sb2O5 to exhibit degenerate n-type transparent conducting behavior. The inherently large electron affinity found in antimony oxides also widens its application in organic solar cells. Following our previous work on zinc antimonate, this work provides additional support for designing Sb(V)-based oxides as cost-effective transparent conducting oxides for a broader range of applications.}, - archiveprefix = {ChemRxiv}, - langid = {english}, - keywords = {Optoelectronics,Point defects,Transparent Conducting Oxides}, - file = {/Users/kavanase/Zotero/storage/GE7QAA9E/Li et al. - 2023 - Computational Prediction of an Antimony-based n-ty.pdf} + year = {2024}, + month = mar, + journal = {Chemistry of Materials}, + volume = {36}, + number = {6}, + pages = {2907--2916}, + publisher = {American Chemical Society}, + issn = {0897-4756}, + doi = {10.1021/acs.chemmater.3c03257}, + urldate = {2024-04-10}, + abstract = {Transparent conducting oxides (TCOs) possess a unique combination of optical transparency and electrical conductivity, making them indispensable in optoelectronic applications. However, their heavy dependence on a small number of established materials limits the range of devices that they can support. The discovery and development of additional wide bandgap oxides that can be doped to exhibit metallic-like conductivity are therefore necessary. In this work, we use hybrid density functional theory to identify a binary Sb(V) system, Sb2O5, as a promising TCO with high conductivity and transparency when doped with fluorine. We conducted a full point defect analysis, finding F-doped Sb2O5 to exhibit degenerate n-type transparent conducting behavior. The inherently large electron affinity found in antimony oxides also widens their application in organic solar cells. Following our previous work on zinc antimonate, this work provides additional support for designing Sb(V)-based oxides as cost-effective TCOs for a broader range of applications.}, + file = {/Users/kavanase/Zotero/storage/9Q32H4UP/Li et al_2024_Computational Prediction of an Antimony-Based n-Type Transparent Conducting.pdf} } @article{liga_mixed-cation_2023, - title = {Mixed-{{Cation Vacancy-Ordered Perovskites}} ({{Cs2Ti1}}{\textendash}{{xSnxX6}}; {{X}} = {{I}} or {{Br}}): {{Low-Temperature Miscibility}}, {{Additivity}}, and {{Tunable Stability}}}, + title = {Mixed-{{Cation Vacancy-Ordered Perovskites}} ({{Cs{\textsubscript{2}}Ti{\textsubscript{1-x}}Sn{\textsubscript{x}}X{\textsubscript{6}}}}; {{X}} = {{I}} or {{Br}}): {{Low-Temperature Miscibility}}, {{Additivity}}, and {{Tunable Stability}}}, shorttitle = {Mixed-{{Cation Vacancy-Ordered Perovskites}} ({{Cs2Ti1}}{\textendash}{{xSnxX6}}; {{X}} = {{I}} or {{Br}})}, author = {Liga, Shanti M. and Kavanagh, Se{\'a}n R. and Walsh, Aron and Scanlon, David O. and Konstantatos, Gerasimos}, year = {2023}, @@ -276,7 +278,7 @@ @article{mosquera-lois_imperfections_2023 file = {/Users/kavanase/Zotero/storage/HXUPFXNP/Mosquera-Lois et al_2023_Imperfections are not 0 K.pdf} } -@misc{mosquera-lois_machine-learning_2024, +@article{mosquera-lois_machine-learning_2024, title = {Machine-Learning Structural Reconstructions for Accelerated Point Defect Calculations}, author = {{Mosquera-Lois}, Irea and Kavanagh, Se{\'a}n R. and Ganose, Alex M. and Walsh, Aron}, year = {2024}, @@ -284,12 +286,13 @@ @misc{mosquera-lois_machine-learning_2024 number = {arXiv:2401.12127}, eprint = {2401.12127}, primaryclass = {cond-mat, physics:physics}, - publisher = {{arXiv}}, + journal = {{arXiv}}, urldate = {2024-01-23}, abstract = {Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the defect structural landscape. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90\% of cases, thereby reducing the number of first-principles calculations by 73\%. Using CdSe\$\_x\$Te\$\_\{1-x\}\$ alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurational complex systems.}, archiveprefix = {arxiv}, keywords = {Condensed Matter - Materials Science,Physics - Chemical Physics}, - file = {/Users/kavanase/Zotero/storage/EBQE6RWG/Mosquera-Lois et al_2024_Machine-learning structural reconstructions for accelerated point defect.pdf;/Users/kavanase/Zotero/storage/QXBJ2AI6/2401.html} + file = {/Users/kavanase/Zotero/storage/EBQE6RWG/Mosquera-Lois et al_2024_Machine-learning structural reconstructions for accelerated point defect.pdf;/Users/kavanase/Zotero/storage/QXBJ2AI6/2401.html}, + doi = {10.48550/arXiv.2401.12127}, } @article{mosquera-lois_search_2021, @@ -381,7 +384,7 @@ @article{neilson_defap_2022 } @article{nicolson_cu2sise3_2023, - title = {{{Cu2SiSe3}} as a Promising Solar Absorber: Harnessing Cation Dissimilarity to Avoid Killer Antisites}, + title = {{{Cu{\textsubscript{2}}SiSe{\textsubscript{3}}}} as a Promising Solar Absorber: Harnessing Cation Dissimilarity to Avoid Killer Antisites}, shorttitle = {{{Cu2SiSe3}} as a Promising Solar Absorber}, author = {Nicolson, Adair and Kavanagh, Se{\'a}n R. and Savory, Christopher N. and Watson, Graeme W. and Scanlon, David O.}, year = {2023}, @@ -435,8 +438,8 @@ @article{squires_py-sc-fermi_2023 file = {/Users/kavanase/Zotero/storage/E2EBAUM5/Squires et al. - 2023 - py-sc-fermi self-consistent Fermi energies and de.pdf} } -@misc{togo_textttspglib_2018, - title = {\${\textbackslash}texttt\{\vphantom\}{{Spglib}}\vphantom\{\}\$: A Software Library for Crystal Symmetry Search}, +@article{togo_textttspglib_2018, + title = {{\texttt{spglib}}: A Software Library for Crystal Symmetry Search}, shorttitle = {\${\textbackslash}texttt\{\vphantom\}{{Spglib}}\vphantom\{\}\$}, author = {Togo, Atsushi and Tanaka, Isao}, year = {2018}, @@ -444,7 +447,7 @@ @misc{togo_textttspglib_2018 number = {arXiv:1808.01590}, eprint = {1808.01590}, primaryclass = {cond-mat}, - publisher = {{arXiv}}, + journal = {{arXiv}}, doi = {10.48550/arXiv.1808.01590}, urldate = {2023-09-27}, abstract = {A computer algorithm to search crystal symmetries of crystal structures has been implemented in software \${\textbackslash}texttt\{spglib\}\$. An iterative algorithm is employed to find a set of space group operations that belongs to any one of space group types by accepting certain amount of distortion for input unit cell structures. The source code is distributed under the BSD 3-Clause License that is a permissive free software licence. Although \${\textbackslash}texttt\{spglib\}\$ is a small code, the iteration loops made the source code complicated. The aim of this text is to provide the algorithm details to those people who are interested in inside-\${\textbackslash}texttt\{spglib\}\$. This text is written for \${\textbackslash}texttt\{spglib\}\$ v1.10.4.}, @@ -472,7 +475,7 @@ @article{turiansky_nonrad_2021 } @article{wang_four-electron_2023-1, - title = {Four-Electron Negative-\${{U}}\$ Vacancy Defects in Antimony Selenide}, + title = {Four-Electron Negative-{\textit{U}} Vacancy Defects in Antimony Selenide}, author = {Wang, Xinwei and Kavanagh, Se{\'a}n R. and Scanlon, David O. and Walsh, Aron}, year = {2023}, month = oct, @@ -491,14 +494,17 @@ @article{willis_limits_2023 title = {Limits to {{Hole Mobility}} and {{Doping}} in {{Copper Iodide}}}, author = {Willis, Joe and Claes, Romain and Zhou, Qi and Giantomassi, Matteo and Rignanese, Gian-Marco and Hautier, Geoffroy and Scanlon, David O.}, year = {2023}, - month = oct, + month = nov, journal = {Chemistry of Materials}, - publisher = {{American Chemical Society}}, + volume = {35}, + number = {21}, + pages = {8995--9006}, + publisher = {American Chemical Society}, issn = {0897-4756}, doi = {10.1021/acs.chemmater.3c01628}, - urldate = {2023-10-25}, - abstract = {Over one hundred years have passed since the discovery of the p-type transparent conducting material copper iodide, predating the concept of the ``electron{\textendash}hole'' itself. Supercentenarian status notwithstanding, little is understood about the charge transport mechanisms in CuI. Herein, a variety of modeling techniques are used to investigate the charge transport properties of CuI, and limitations to the hole mobility over experimentally achievable carrier concentrations are discussed. Poor dielectric response is responsible for extensive scattering from ionized impurities at degenerately doped carrier concentrations, while phonon scattering is found to dominate at lower carrier concentrations. A phonon-limited hole mobility of 162 cm2 V{\textendash}1 s{\textendash}1 is predicted at room temperature. The simulated charge transport properties for CuI are compared to existing experimental data, and the implications for future device performance are discussed. In addition to charge transport calculations, the defect chemistry of CuI is investigated with hybrid functionals, revealing that reasonably localized holes from the copper vacancy are the predominant source of charge carriers. The chalcogens S and Se are investigated as extrinsic dopants, where it is found that despite relatively low defect formation energies, they are unlikely to act as efficient electron acceptors due to the strong localization of holes and subsequent deep transition levels.}, - file = {/Users/kavanase/Zotero/storage/Y9EHVRSY/acs.chemmater.html} + urldate = {2024-04-10}, + abstract = {Over one hundred years have passed since the discovery of the p-type transparent conducting material copper iodide, predating the concept of the ``electron--hole'' itself. Supercentenarian status notwithstanding, little is understood about the charge transport mechanisms in CuI. Herein, a variety of modeling techniques are used to investigate the charge transport properties of CuI, and limitations to the hole mobility over experimentally achievable carrier concentrations are discussed. Poor dielectric response is responsible for extensive scattering from ionized impurities at degenerately doped carrier concentrations, while phonon scattering is found to dominate at lower carrier concentrations. A phonon-limited hole mobility of 162 cm2 V--1 s--1 is predicted at room temperature. The simulated charge transport properties for CuI are compared to existing experimental data, and the implications for future device performance are discussed. In addition to charge transport calculations, the defect chemistry of CuI is investigated with hybrid functionals, revealing that reasonably localized holes from the copper vacancy are the predominant source of charge carriers. The chalcogens S and Se are investigated as extrinsic dopants, where it is found that despite relatively low defect formation energies, they are unlikely to act as efficient electron acceptors due to the strong localization of holes and subsequent deep transition levels.}, + file = {/Users/kavanase/Zotero/storage/IXIEWT7W/Willis et al_2023_Limits to Hole Mobility and Doping in Copper Iodide.pdf} } @article{willis_possibility_2023, @@ -809,15 +815,15 @@ @article{cp2k URL = {https://doi.org/10.1063/5.0007045}, eprint = {https://doi.org/10.1063/5.0007045} } -@misc{wang_upper_2024, - title = {Upper Efficiency Limit of {{Sb2Se3}} Solar Cells}, +@article{wang_upper_2024, + title = {Upper Efficiency Limit of {{Sb{\textsubscript{2}}Se{\textsubscript{3}}}} Solar Cells}, author = {Wang, Xinwei and Kavanagh, Se{\'a}n R. and Scanlon, David O. and Walsh, Aron}, year = {2024}, month = feb, number = {arXiv:2402.04434}, eprint = {2402.04434}, primaryclass = {cond-mat}, - publisher = {{arXiv}}, + journal = {{arXiv}}, doi = {10.48550/arXiv.2402.04434}, urldate = {2024-02-22}, abstract = {Antimony selenide (Sb2Se3) is at the forefront of an emerging class of sustainable photovoltaic materials. Despite notable developments over the past decade, the light-to-electricity conversion efficiency of Sb2Se3 has reached a plateau of {\textasciitilde}10\%. Is this an intrinsic limitation of the material or is there scope to rival the success of metal halide perovskite solar cells? Here we assess the trap-limited conversion efficiency of Sb2Se3. First-principles defect analysis of the hole and electron capture rates for point defects demonstrates the critical role of vacancies as active recombination centres. We predict an upper limit of 25\% efficiency in Sb2Se3 grown under optimal equilibrium conditions where the concentrations of charged vacancies are minimised. We further reveal how the detrimental effect of Se vacancies can be reduced by extrinsic oxygen passivation, highlighting a pathway to achieve high-performance metal selenide solar cells close to the thermodynamic limit.}, @@ -827,7 +833,7 @@ @misc{wang_upper_2024 } @article{yuan_discovery_2024, - title = {Discovery of the {{Zintl-phosphide BaCd2P2}} as a Long Carrier Lifetime and Stable Solar Absorber}, + title = {Discovery of the {{Zintl-phosphide BaCd{\textsubscript{2}}P{\textsubscript{2}}}} as a Long Carrier Lifetime and Stable Solar Absorber}, author = {Yuan, Zhenkun and Dahliah, Diana and Hasan, Muhammad Rubaiat and Kassa, Gideon and Pike, Andrew and Quadir, Shaham and Claes, Romain and Chandler, Cierra and Xiong, Yihuang and Kyveryga, Victoria and Yox, Philip and Rignanese, Gian-Marco and Dabo, Ismaila and Zakutayev, Andriy and Fenning, David P. and Reid, Obadiah G. and Bauers, Sage and Liu, Jifeng and Kovnir, Kirill and Hautier, Geoffroy}, year = {2024}, month = mar, @@ -840,7 +846,7 @@ @article{yuan_discovery_2024 } @article{broberg_high-throughput_2023, - title = {High-Throughput Calculations of Charged Point Defect Properties with Semi-Local Density Functional Theory---Performance Benchmarks for Materials Screening Applications}, + title = {High-Throughput Calculations of Charged Point Defect Properties with Semi-Local Density Functional Theory – Performance Benchmarks for Materials Screening Applications}, author = {Broberg, Danny and Bystrom, Kyle and Srivastava, Shivani and Dahliah, Diana and Williamson, Benjamin A. D. and Weston, Leigh and Scanlon, David O. and Rignanese, Gian-Marco and Dwaraknath, Shyam and Varley, Joel and Persson, Kristin A. and Asta, Mark and Hautier, Geoffroy}, year = {2023}, month = may, diff --git a/docs/JOSS/paper.md b/docs/JOSS/paper.md index 84c5d24b..69debd25 100644 --- a/docs/JOSS/paper.md +++ b/docs/JOSS/paper.md @@ -62,8 +62,8 @@ bibliography: paper.bib Defects are a universal feature of crystalline solids, dictating the key properties and performance of many functional materials. Given their crucial importance yet inherent difficulty in measuring experimentally, computational methods (such as DFT and ML/classical force-fields) are widely used to predict defect behaviour at the atomic level and the resultant impact on macroscopic properties. -Here we report ``doped``, a Python package for the generation, pre-/post-processing and analysis of defect supercell calculations. -``doped`` has been built to implement the defect simulation workflow in an efficient, user-friendly yet powerful and fully-flexible manner, with the goal of providing a robust general-purpose platform for conducting reproducible calculations of solid-state defect properties. +Here we report ``doped``, a Python package for the generation, pre-/post-processing, and analysis of defect supercell calculations. +``doped`` has been built to implement the defect simulation workflow in an efficient and user-friendly – yet powerful and fully-flexible – manner, with the goal of providing a robust general-purpose platform for conducting reproducible calculations of solid-state defect properties. [//]: # (such as conductivity, carrier recombination, catalytic activity etc) [//]: # (The typically dilute concentration of defects, despite their major impact on macroscopic properties, renders their experimental characterisation extremely challenging however. ) @@ -77,12 +77,12 @@ Here we report ``doped``, a Python package for the generation, pre-/post-process The materials science sub-field of computational defect modelling has seen considerable growth in recent years, driven by the crucial importance of these species in functional materials and the major advances in computational methodologies and resources facilitating their accurate simulation. Software which enables researchers to efficiently and accurately perform these calculations, while allowing for in-depth target analyses of the resultant data, is thus of significant value to the community. Indeed there are many critical stages in the computational workflow for defects, which when performed manually not only consume significant researcher time and effort but also leave room for human error – particularly for newcomers to the field. -Moreover, there are growing efforts to perform high-throughput investigations of defects in solids [@xiong_high-throughput_2023,@yuan_discovery_2024,@broberg_high-throughput_2023], necessitating robust, user-friendly and efficient software implementing this calculation workflow. +Moreover, there are growing efforts to perform high-throughput investigations of defects in solids [@xiong_high-throughput_2023; @yuan_discovery_2024; @broberg_high-throughput_2023], necessitating robust, user-friendly, and efficient software implementing this calculation workflow. [//]: # (By expediting the defect simulation workflow and providing efficient analysis tools, ``doped`` aims to... facilitate the investigation of defects in solids, and to enable the efficient and reproducible calculation of solid-state defect properties.) Given this importance of defect simulations and the complexity of the workflow, a number of software packages have been developed with the goal of managing pre- and post-processing of defect calculations, including work on the `HADES`/`METADISE` codes from the 1970s [@parker_hades_2004], to more recent work from @Kumagai2021, @Broberg2018, @Shen2024, @neilson_defap_2022, @Arrigoni2021, @Goyal2017, @Huang2022, @pean_presentation_2017 and @naik_coffee_2018.[^1] -While each of these codes have their strengths, they do not include the full suite of functionality provided by `doped` – some of which is discussed below – nor adopt the same focus on user-friendliness (along with sanity-checking warnings & error catching) and efficiency with full flexibility and wide-ranging functionality, targeting expert-level users and newcomers to the field alike. +While each of these codes have their strengths, they do not include the full suite of functionality provided by `doped` – some of which is discussed below – nor adopt the same focus on user-friendliness (along with sanity-checking warnings and error catching) and efficiency with full flexibility and wide-ranging functionality, targeting expert-level users and newcomers to the field alike. [^1]: Some of these packages are no longer maintained, not compatible with high-throughput architectures, and/or are closed-source/commercial packages. @@ -96,11 +96,11 @@ While each of these codes have their strengths, they do not include the full sui # doped -`doped` is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, as depicted in \autoref{fig_workflow}. -The design philosophy of `doped` has been to implement the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner, combining reasonable defaults with full user control for each parameter in the workflow. -As depicted in \autoref{fig_workflow}, the core functionality of `doped` is the generation of defect supercells and competing phases, writing calculation input files, parsing calculation outputs and analysing/plotting defect-related properties. This functionality and recommended usage of `doped` is demonstrated in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html) on the [documentation website](https://doped.readthedocs.io/en/latest/). +`doped` is a Python package for the generation, pre-/post-processing, and analysis of defect supercell calculations, as depicted in \autoref{fig_workflow}. +The design philosophy of `doped` has been to implement the defect simulation workflow in an efficient, reproducible, and user-friendly – yet powerful and fully-customisable – manner, combining reasonable defaults with full user control for each parameter in the workflow. +As depicted in \autoref{fig_workflow}, the core functionality of `doped` is the generation of defect supercells and competing phases, writing calculation input files, parsing calculation outputs, and analysing/plotting defect-related properties. This functionality and recommended usage of `doped` is demonstrated in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html) on the [documentation website](https://doped.readthedocs.io/en/latest/). -![**a.** Average minimum periodic image distance, normalised by the ideal image distance at that volume (i.e. for a perfect close-packed face-centred cubic (FCC) cell), versus the number of primitive unit cells, for the supercell generation algorithms of `doped`, `ASE` and `pymatgen`. "SC" = simple cubic and "HCP" = hexagonal close-packed. **b.** Average performance of various charge state estimation routines, in terms of false positives/negatives. "ICSD probabilities" refers to a model based on the probabilities of oxidation states, as given by their occurrence in the ICSD database. Asterisk indicates that `pyCDT` "false _negatives_" are underestimated as the majority of this test set used the estimated charge state ranges from `pyCDT`. "Ox. state" = oxidation state. Example **(c)** Kumagai-Oba (extended Freysoldt-Neugebauer-Van-de-Walle; "eFNV") finite-size correction plot, **(d)** defect formation energy diagram, **(e)** chemical potential / stability region, **(f)** Fermi level vs. annealing temperature, **(g)** defect/carrier concentrations vs. annealing temperature and **(h)** Fermi level / carrier concentration heatmap plots from `doped`. Automated plots of single-particle eigenvalues from DFT supercell calculations for **(i)** $V_{Cu}^{0}$ in $Cu_2SiSe_3$ and **(j)** $V_{Cd}^{-1}$ in CdTe. **(k)** Automated site displacement analysis, plotting atomic displacements with respect to the defect site against distance to the defect site, for $V_{Cd}^{-1}$ in CdTe. Data and code to reproduce these plots is provided in the [`docs/JOSS`](https://github.com/SMTG-Bham/doped/blob/main/docs/JOSS) subfolder of the `doped` GitHub repository. \label{fig1}](doped_JOSS_figure.png) +![Performance and example outputs from `doped`. **(a)** Average minimum periodic image distance, normalised by the ideal image distance (i.e. for a close-packed face-centred cubic (FCC) cell), vs. number of unit cells for supercell generation algorithms in `doped`, `ASE`, and `pymatgen`. "SC" = simple cubic and "HCP" = hexagonal close-packed. **(b)** Average performance of various charge state estimation routines. "ICSD probabilities" refers to a model based oxidation state probabilities, as given by their occurrence in the ICSD database. Asterisk indicates that `pyCDT` "false _negatives_" are underestimated as the majority of this test set used the `pyCDT` charge state ranges. "Ox. state" = oxidation state. Example **(c)** Kumagai-Oba (eFNV) finite-size correction plot, **(d)** defect formation energy diagram, **(e)** chemical potential / stability region, **(f)** Fermi level vs. annealing temperature, **(g)** defect/carrier concentrations vs. annealing temperature and **(h)** Fermi level / carrier concentration heatmap plots from `doped`. Automated plots of single-particle eigenvalues from DFT supercell calculations for **(i)** $V_{Cu}^{0}$ in Cu$_2$SiSe$_3$ and **(j)** $V_{Cd}^{-1}$ in CdTe. **(k)** Automated site displacement analysis, plotting atomic displacements with respect to the defect site against distance to the defect site, for $V_{Cd}^{-1}$ in CdTe. Data and code to reproduce these plots is provided in the [`docs/JOSS`](https://github.com/SMTG-Bham/doped/blob/main/docs/JOSS) folder of the `doped` GitHub repository. \label{fig1}](doped_JOSS_figure.png) Some key advances of `doped` include: @@ -108,7 +108,8 @@ Some key advances of `doped` include: - Over a test set of simple cubic, trigonal, orthorhombic, monoclinic and face-centred cubic unit cells, the `doped` algorithm is found to give mean improvements of 35.2%, 9.1% and 6.7% in the minimum image distance for a given (maximum) number of unit cells as compared to the `pymatgen` cubic supercell algorithm, the `ASE` optimal cell shape algorithm with simple-cubic target shape, and `ASE` with FCC target shape respectively – in the range of 2-20 unit cells. For 2-50 unit cells (for which the mean values across this test set are plotted in \autoref{fig1}a), this becomes 36.0%, 9.3% and 5.6% respectively. Given the approximately cubic scaling of DFT computational cost with the number of atoms, these correspond to significant reductions in cost (~20-150%). - As always, the user has full control over supercell generation in `doped`, with the ability to specify/adjust constraints on the minimum image distance, number of atoms or transformation matrix, or to simply provide a pre-generated supercell if desired. - **Charge-state Estimation:** Defects in solids can adopt various electronic charge states. However, the set of stable charge states for a given defect is typically not known _a priori_, so one must choose a set of _possible_ defect charge states to calculate – usually relying on some form of chemical intuition. In this regard, extremal defect charge states that are calculated but do not end up being stable can be considered 'false positives' or 'wasted' calculations,[^2] while charge states which are stable but were not calculated can be considered 'false negatives' or 'missed' calculations. `doped` builds on other routines which use known elemental oxidation states to additionally account for oxidation state _probabilities_, the electronic state of the host crystal and charge state magnitudes. Implementing these features in a simple cost function, we find a significant improvement in terms of both efficiency (reduced false positives) and completeness (reduced false negatives) for this charge state estimation, as shown in \autoref{fig1}b.[^3] - - Again, this step is fully-customisable. The user can tune the probability threshold at which to include charge states or manually specify defect charge states. All probability factors computed are available to the user and saved to the defect `JSON` files for full reproducibility. + + Again, this step is fully-customisable. The user can tune the probability threshold at which to include charge states or manually specify defect charge states. All probability factors computed are available to the user and saved to the defect `JSON` files for full reproducibility. [^2]: Note that _unstable_ defect charge states which are intermediate between _stable_ charge states (e.g. $X^0$ for a defect $X$ with a (+1/-1) negative-U level) should still be calculated and are _not_ considered false positives. @@ -121,33 +122,33 @@ Some key advances of `doped` include: `doped` aims to improve the efficiency of this step by querying the [Materials Project](https://materialsproject.org) database (containing both experimentally-measured and theoretically-predicted crystal structures), and pulling only compounds which _could border the host material_ within a user-specified error tolerance for the semi-local DFT database energies (0.1 eV/atom by default), along with the elemental reference phases. The necessary _k_-point convergence step for these compounds is also implemented in a semi-automated fashion to expedite this process. - With the parsed chemical potentials in `doped`, the user can easily select various X-poor/rich chemical conditions, or scan over a range of chemical potentials (growth conditions) as shown in \autoref{fig1}e,h. -- **Automated Symmetry & Degeneracy Handling:** `doped` automatically determines the point symmetry of both initial (un-relaxed) and final (relaxed) defect configurations, and computes the corresponding orientational (and spin) degeneracy factors. This is a key pre-factor in the defect concentration equation: +- **Automated Symmetry & Degeneracy Handling:** `doped` automatically determines the point symmetry of both initial (un-relaxed) and final (relaxed) defect configurations, and computes the corresponding orientational (and spin) degeneracy factors. This functionality is also offered in the form of [standalone functions](https://doped.readthedocs.io/en/latest/advanced_analysis_tutorial.html#point-symmetry-analysis) which do not require the defect calculations to have been generated/parsed with `doped`. This is a key pre-factor in the defect concentration equation: \begin{equation} N_D = gN_s \exp(-E_f/k_BT) \end{equation} - where $g$ is the product of all degeneracy factors, $N_s$ is the concentration of lattice sites for that defect, $E_f$ is the defect formation energy and $N_D$ is the defect concentration. $g$ can affect predicted defect/carrier concentrations by up to 2/3 orders of magnitude [@mosquera-lois_imperfections_2023; @kavanagh_impact_2022], and is often overlooked in defect calculations, partly due to the (previous) requirement of significant manual effort and knowledge of group theory. + where $g$ is the product of all degeneracy factors, $N_s$ is the concentration of lattice sites for that defect, $E_f$ is the defect formation energy and $N_D$ is the defect concentration. $g$ can affect predicted defect/carrier concentrations by up to two or three orders of magnitude [@mosquera-lois_imperfections_2023; @kavanagh_impact_2022], and is often overlooked in defect calculations, partly due to the (previous) requirement of significant manual effort and knowledge of group theory. - **Automated Compatibility Checking:** When parsing defect calculations, `doped` automatically checks that calculation parameters which could affect the defect formation energy (e.g. _k_-point grid, energy cutoff, pseudopotential choice, exchange fraction, Hubbard U etc.) are consistent between the defect and reference calculations. This is a common source of accidental error in defect calculations, and `doped` provides informative warnings if any inconsistencies are detected. -- **Thermodynamic Analysis:** `doped` provides a suite of flexible tools for the analysis of defect thermodynamics, including formation energy diagrams (\autoref{fig1}d), equilibrium & non-equilibrium Fermi level solving (\autoref{fig1}f), doping analysis (\autoref{fig1}g,h), Brouwer-type diagrams etc. These include physically-motivated (but tunable) grouping of defect sites, full inclusion of metastable states, support for complex system constraints, optimisation over high-dimensional chemical & temperature space and highly-customisable plotting. In-depth examples are provided in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html). +- **Thermodynamic Analysis:** `doped` provides a suite of flexible tools for the analysis of defect thermodynamics, including formation energy diagrams (\autoref{fig1}d), equilibrium & non-equilibrium Fermi level solving (\autoref{fig1}f), doping analysis (\autoref{fig1}g,h), Brouwer-type diagrams etc. These include physically-motivated (but tunable) grouping of defect sites, full inclusion of metastable states, support for complex system constraints, optimisation over high-dimensional chemical & temperature space and highly customisable plotting. In-depth examples are provided in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html). -- **Finite-Size Corrections:** Both the isotropic Freysoldt (FNV) [@Freysoldt2009] and anisotropic Kumagai (eFNV) [@kumagai_electrostatics-based_2014] image charge corrections are implemented automatically in `doped`, with tunable sampling radii / sites (which may be desirable for e.g. layered materials), automated correction plotting (to visualise/analyse convergence; \autoref{fig1}c) and automatic sampling error estimation. +- **Finite-Size Corrections:** Both the isotropic Freysoldt (FNV) [@Freysoldt2009] and anisotropic Kumagai (eFNV) [@kumagai_electrostatics-based_2014] image charge corrections are implemented automatically in `doped`, with tunable sampling radii / sites (which may be desirable for e.g. layered materials), automated correction plotting (to visualise/analyse convergence; \autoref{fig1}c), and automatic sampling error estimation. -- **Reproducibility & Tabulation:** `doped` has been built to support and encourage reproducibility, with all input parameters and calculation results saved to lightweight `JSON` files. This allows for easy sharing of calculation inputs/outputs and reproducible analysis. Several tabulation functions are also provided to facilitate the quick summarising of key quantities as exemplified in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html) (including defect formation energy contributions, charge transition levels (with/without metastable states), symmetry, degeneracy and multiplicity factors, defect/carrier concentrations, chemical potential limits, dopability limits, doping windows...) to aid transparency, reproducibility, comparisons with other works and general analysis. The use of these tabulated outputs in supporting information of publications is encouraged. +- **Reproducibility & Tabulation:** `doped` has been built to support and encourage reproducibility, with all input parameters and calculation results saved to lightweight `JSON` files. This allows for easy sharing of calculation inputs/outputs and reproducible analysis. Several tabulation functions are also provided to facilitate the quick summarising of key quantities as exemplified in the [tutorials](https://doped.readthedocs.io/en/latest/Tutorials.html) (including defect formation energy contributions, charge transition levels (with/without metastable states), symmetry, degeneracy and multiplicity factors, defect/carrier concentrations, chemical potential limits, dopability limits, doping windows...) to aid transparency, reproducibility, comparisons with other works, and general analysis. The use of these tabulated outputs in supporting information of publications is encouraged. - **High-Throughput Compatibility:** `doped` is built to be compatible with high-throughput architectures such as [atomate(2)](https://github.com/materialsproject/atomate2) [@atomate] or [AiiDA](https://aiida.net) [@AiiDA], aided by its object-oriented Python framework, JSON-serializable classes and sub-classed `pymatgen` objects. Examples are provided on the [documentation website](https://doped.readthedocs.io/en/latest/). - **[`ShakeNBreak`](https://shakenbreak.readthedocs.io):** `doped` is natively interfaced with our defect structure-searching code `ShakeNBreak` [@mosquera-lois_shakenbreak_2022], seamlessly incorporating this phase in the defect calculation workflow. This step can optionally be skipped or an alternative structure-searching approach readily implemented. -Some additional features of `doped` include directional-dependent site displacement (local strain) analysis, deterministic & informative defect naming, molecule generation for gaseous competing phases, multiprocessing for expedited generation & parsing, shallow defect analysis (via `pydefect` [@Kumagai2021]), Wyckoff site analysis (including arbitrary/interstitial sites), controllable defect site placement to aid visualisation and more. +Some additional features of `doped` include directional-dependent site displacement (local strain) analysis, deterministic & informative defect naming, molecule generation for gaseous competing phases, multiprocessing for expedited generation & parsing, shallow defect analysis (via `pydefect` [@Kumagai2021]), Wyckoff site analysis (including _arbitrary/interstitial_ sites), controllable defect site placement to aid visualisation and more. The defect generation and thermodynamic analysis components of `doped` are agnostic to the underlying software used for the defect supercell calculations. Direct calculation I/O is fully-supported for `VASP` [@vasp], while input defect structure files can be generated for several widely-used DFT codes, including `FHI-aims` [@fhi_aims], `CP2K` [@cp2k], `Quantum Espresso` [@espresso] and `CASTEP` [@castep] via the `pymatgen` `Structure` object. Full support for calculation I/O with other DFT codes may be added in the future if there is sufficient demand. Moreover, `doped` is built to be readily compatible with other computational toolkits for advanced defect characterisation, such as `ShakeNBreak` for defect structure-searching, `py-sc-fermi` for advanced thermodynamic analysis under complex constraints [@squires_py-sc-fermi_2023], `easyunfold` for analysing defect/dopant-induced electronic structure changes [@zhu_easyunfold_2024] or `CarrierCapture.jl`/`nonrad` for non-radiative recombination calculations [@kim_carriercapturejl_2020; @turiansky_nonrad_2021]. -`doped` has been used to manage the defect simulation workflow in a number of publications thus far, including @wang_upper_2024, @cen_cation_2023, @nicolson_cu2sise3_2023, @li_computational_2023, @kumagai_alkali_2023, @woo_inhomogeneous_2023, @wang_four-electron_2023-1, @mosquera-lois_search_2021, @mosquera-lois_identifying_2023, @mosquera-lois_machine-learning_2024, @huang_strong_2022, @dou_giant_2024, @liga_mixed-cation_2023, @willis_possibility_2023, @willis_limits_2023, @krajewska_enhanced_2021, @kavanagh_rapid_2021, @kavanagh_frenkel_2022. +`doped` has been used to manage the defect simulation workflow in a number of publications thus far, including @wang_upper_2024, @cen_cation_2023, @nicolson_cu2sise3_2023, @li_computational_2024, @kumagai_alkali_2023, @woo_inhomogeneous_2023, @wang_four-electron_2023-1, @mosquera-lois_search_2021, @mosquera-lois_identifying_2023, @mosquera-lois_machine-learning_2024, @huang_strong_2022, @dou_giant_2024, @liga_mixed-cation_2023, @willis_possibility_2023, @willis_limits_2023, @krajewska_enhanced_2021, @kavanagh_rapid_2021, @kavanagh_frenkel_2022. # CRediT Author Contributions **Seán R. Kavanagh:** Conceptualisation, Methodology, Software, Writing, Project Administration. **Alex G. Squires:** Code for complex doping analysis. **Adair Nicolson:** Code for shallow defect analysis. **Irea Mosquera-Lois:** Code for local strain analysis. **Katarina Brlec:** Competing phases code refactoring. **Aron Walsh & David Scanlon:** Funding Acquisition, Management, Ideas & Discussion. **All authors:** Feedback, Code Contributions, Writing – Review & Editing. diff --git a/docs/Tips.rst b/docs/Tips.rst index 9b0728be..9c579d6a 100644 --- a/docs/Tips.rst +++ b/docs/Tips.rst @@ -89,7 +89,7 @@ underlying calculation and/or extreme forces. `this part `_ of the ``SnB`` docs. - - **Alternatively (if you have already performed SnB structure-searching), convergence of the forces can be aided by:** + - `Alternatively (if you have already performed SnB structure-searching), convergence of the forces can be aided by:` - Switching the ionic relaxation algorithm back and forth (i.e. change :code:`IBRION` to :code:`1` or :code:`3` and back). - Reducing the ionic step width (e.g. change :code:`POTIM` to :code:`0.02` in the :code:`INCAR`) @@ -115,8 +115,10 @@ For tips on the ``ShakeNBreak`` part of the defect calculation workflow, please Layered / Low Dimensional Materials -------------------------------------- -Layered and low-dimensional materials introduce complications for defect analysis. One point is that typically such lower-symmetry materials exhibit higher rates of energy-lowering defect reconstructions (e.g. -`4-electron negative-U centres in Sb₂Se₃ `_), as a result of +Layered and low-dimensional materials introduce complications for defect analysis. One point is that +typically such lower-symmetry materials exhibit higher rates of energy-lowering defect reconstructions +(e.g. `4-electron negative-U centres in Sb₂Se₃ `_, +`vacancies in low-dimensional chalcogenides `_ etc), as a result of having more complex energy landscapes. Another is that often the application of charge correction schemes to supercell calculations with layered @@ -133,6 +135,13 @@ when parsing the intrinsic defects, the -3 charge antimony vacancy (``v_Sb-3``) with the supercell approach). If this error is not acceptable, you may need to use a larger supercell for more accurate energies. +.. note:: + + Charge correction errors are estimated by computing the standard error of the mean of the electrostatic + potential difference between the bulk and defect supercells, in the sampling region (far from the + defect site), and multiplying by the defect charge. This gives a lower bound estimate of the true + error in the charge correction for a given supercell. + Following the advice in the warning, we use ``defect_entry.get_kumagai_correction(plot=True)`` to plot the site potential differences for the defect supercell (which is used to obtain the eFNV (Kumagai-Oba) anisotropic charge correction): @@ -275,6 +284,11 @@ supercells and plotting the charge density. Important terms include: 3. ``vbm has acceptor phs``/``cbm has donor phs``: Whether a PHS has been automatically identified. Depends on how VBM-like/CBM-like the defect states are and the occupancy of the state. ``(X vs. 0.2)`` refers to the hole/electron occupancy at the band edge vs the default threshold of 0.2 for flagging as a PHS (but you should use your own judgement of course). 4. ``Localized Orbital(s)``: Information about localised defect states, if present. +Additionally, ``Index`` refers to the band/eigenvalue index in the DFT calculation, ``Energy`` is its +eigenvalue energy at the given ``K-point coords``, ``Orbitals`` lists the projected orbital contributions +to that state, and ``OrbDiff`` is the normalised difference in projected orbital contributions to the +VBM/CBM states between the bulk and defect supercells. + .. code-block:: python bulk = "Cu2SiSe3/bulk/vasp_std" @@ -314,7 +328,7 @@ PHS on the transition level diagram with a clear circle is shown on the right. .. image:: Cu2SiSe3_v_Cu_0_eigenvalue_plot.png :width: 325px :align: left -.. image:: Cu2SISe3_TLD.png +.. image:: Cu2SiSe3_TLD.png :width: 320px :align: left diff --git a/docs/conf.py b/docs/conf.py index 64700bee..8f7a8278 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,7 +25,7 @@ author = 'Seán R. Kavanagh' # The full version, including alpha/beta/rc tags -release = '2.4.0' +release = '2.4.1' # -- General configuration --------------------------------------------------- diff --git a/docs/index.rst b/docs/index.rst index 7b42b94a..adc870c1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,11 +50,12 @@ Performance and Example Outputs .. image:: JOSS/doped_JOSS_figure.png :target: https://arxiv.org/abs/2403.08012 -**a.** Optimal supercell generation comparison. **b.** Charge state estimation comparison. +**(a)** Optimal supercell generation comparison. **(b)** Charge state estimation comparison. Example **(c)** Kumagai-Oba (eFNV) finite-size correction plot, **(d)** defect formation energy diagram, **(e)** chemical potential / stability region, **(f)** Fermi level vs. annealing temperature, **(g)** defect/carrier concentrations vs. annealing temperature and **(h)** Fermi level / carrier concentration -heatmap plots from ``doped``. See the +heatmap plots from ``doped``. Automated plots of **(i,j)** single-particle eigenvalues and **(k)** site +displacements from DFT supercell calculations. See the `JOSS paper `__ for more details. .. Update all JOSS paper links when ready! diff --git a/doped/VASP_sets/PBEsol_ConvergenceSet.yaml b/doped/VASP_sets/PBEsol_ConvergenceSet.yaml index c0b3d92e..b0fcdb27 100644 --- a/doped/VASP_sets/PBEsol_ConvergenceSet.yaml +++ b/doped/VASP_sets/PBEsol_ConvergenceSet.yaml @@ -1,5 +1,5 @@ INCAR: - ALGO: "Normal # Change to All if ZHEGV, FEXCP/F or ZBRENT errors encountered" + ALGO: "Normal # Change to All if ZHEGV, FEXCP/F or ZBRENT errors encountered, or poor electronic convergence" EDIFF_PER_ATOM: 2.0e-07 # capped at a max EDIFF of 1e-4 for large structures (N(atoms) > 500) ENCUT: 350 GGA: PS diff --git a/doped/VASP_sets/RelaxSet.yaml b/doped/VASP_sets/RelaxSet.yaml index 843ed692..72752df7 100644 --- a/doped/VASP_sets/RelaxSet.yaml +++ b/doped/VASP_sets/RelaxSet.yaml @@ -1,6 +1,6 @@ INCAR: '# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM': 'Typical variable parameters' - ALGO: "Normal # Change to All if ZHEGV, FEXCP/F or ZBRENT errors encountered" + ALGO: "Normal # Change to All if ZHEGV, FEXCP/F or ZBRENT errors encountered, or poor electronic convergence" EDIFF_PER_ATOM: 2.0e-07 # capped at a max EDIFF of 1e-4 for large supercells (N(atoms) > 500) EDIFFG: -0.01 ENCUT: 300 diff --git a/doped/analysis.py b/doped/analysis.py index f2919e58..057d1daf 100644 --- a/doped/analysis.py +++ b/doped/analysis.py @@ -403,8 +403,9 @@ def defect_entry_from_paths( corrections to the defect energy (not recommended in most cases). Default = False. error_tolerance (float): - If the estimated error in the defect charge correction is greater - than this value (in eV), then a warning is raised. (default: 0.05 eV) + If the estimated error in the defect charge correction, based on the + variance of the potential in the sampling region is greater than this + value (in eV), then a warning is raised. (default: 0.05 eV) bulk_band_gap_path (str): Path to bulk OUTCAR file for determining the band gap. If the VBM/CBM occur at reciprocal space points not included in the bulk supercell @@ -511,7 +512,8 @@ def __init__( corrections to the defect energies (not recommended in most cases). Default = False. error_tolerance (float): - If the estimated error in any charge correction is greater than + If the estimated error in any charge correction, based on the + variance of the potential in the sampling region, is greater than this value (in eV), then a warning is raised. (default: 0.05 eV) bulk_band_gap_path (str): Path to bulk OUTCAR file for determining the band gap. If the @@ -587,6 +589,27 @@ def __init__( and dir not in (self.bulk_path.split("/") if self.bulk_path else []) ] + if not possible_defect_folders: # user may have specified the defect folder directly, so check + # if we can dynamically determine the defect folder: + possible_defect_folders = [ + dir + for dir in os.listdir(os.path.join(self.output_path, os.pardir)) + if any( + "vasprun" in file and ".xml" in file + for file_list in [ + tup[2] for tup in os.walk(os.path.join(self.output_path, os.pardir, dir)) + ] + for file in file_list + ) + and ( + os.path.basename(self.output_path) in dir # only that defect directory + or "bulk" in str(dir).lower() # or a bulk directory, for later + ) + and dir not in (self.bulk_path.split("/") if self.bulk_path else []) + ] + if possible_defect_folders: # update output path (otherwise will crash with informative error) + self.output_path = os.path.join(self.output_path, os.pardir) + if self.subfolder is None: # determine subfolder to use vasp_subfolders = [ subdir @@ -666,36 +689,16 @@ def __init__( dir_type="bulk", ) - self.bulk_procar = None - bulk_vr_exc = None - - try: - self.bulk_vr = get_vasprun( - bulk_vr_path, parse_projected_eigen=parse_projected_eigen is not False - ) - except Exception as exc: - bulk_vr_exc = exc # don't throw unless PROCAR also fails - self.bulk_vr = get_vasprun(bulk_vr_path, parse_projected_eigen=False) - - if parse_projected_eigen is not False and self.bulk_vr.projected_eigenvalues is None: - # Checking if PROCAR available: - bulk_procar_path, multiple = _get_output_files_and_check_if_multiple("PROCAR", self.bulk_path) - if "PROCAR" in bulk_procar_path: - try: - self.bulk_procar = get_procar(bulk_procar_path) - - except Exception as procar_exc: - if parse_projected_eigen is not None: # otherwise no warning - warning_message = ( - f"Could not parse vasprun.xml.gz files in bulk folder at {self.bulk_path}, " - f"got error:\n{bulk_vr_exc}\nThen got " - if bulk_vr_exc - else "Got " - ) - warnings.warn( - f"{warning_message}the following error when attempting to parse projected " - f"eigenvalues from the bulk PROCAR(.gz):\n{procar_exc}" - ) + self.bulk_vr, self.bulk_procar = _parse_vr_and_poss_procar( + bulk_vr_path, + parse_projected_eigen=self.parse_projected_eigen, + output_path=self.bulk_path, + label="bulk", + parse_procar=True, + ) + self.parse_projected_eigen = ( + self.bulk_vr.projected_eigenvalues is not None or self.bulk_procar is not None + ) # try parsing the bulk oxidation states first, for later assigning defect "oxi_state"s (i.e. # fully ionised charge states): @@ -1017,12 +1020,20 @@ def _mention_bulk_path_subfolder_for_correction_warnings(warning: str) -> str: def _call_multiple_corrections_tolerance_warning(correction_errors, type="FNV"): long_name = "Freysoldt" if type == "FNV" else "Kumagai" - correction_errors_string = "\n".join( - f"{name}: {error:.3f} eV" for name, error in correction_errors - ) + if error_tolerance >= 0.01: # if greater than 10 meV, round energy values to meV: + error_tol_string = f"{error_tolerance:.3f}" + correction_errors_string = "\n".join( + f"{name}: {error:.3f} eV" for name, error in correction_errors + ) + else: # else give in scientific notation: + error_tol_string = f"{error_tolerance:.2e}" + correction_errors_string = "\n".join( + f"{name}: {error:.2e} eV" for name, error in correction_errors + ) + warnings.warn( f"Estimated error in the {long_name} ({type}) charge correction for certain " - f"defects is greater than the `error_tolerance` (= {error_tolerance:.3f} eV):" + f"defects is greater than the `error_tolerance` (= {error_tol_string} eV):" f"\n{correction_errors_string}\n" f"You may want to check the accuracy of the corrections by plotting the site " f"potential differences (using `defect_entry.get_{long_name.lower()}_correction()`" @@ -1328,6 +1339,48 @@ def __repr__(self): ) +def _parse_vr_and_poss_procar( + vr_path: str, + parse_projected_eigen: Optional[bool] = None, + output_path: Optional[str] = None, + label: str = "bulk", + parse_procar: bool = True, +): + procar = None + + failed_eig_parsing_warning_message = ( + f"Could not parse eigenvalue data from vasprun.xml.gz files in {label} folder at {output_path}" + ) + + try: + vr = get_vasprun( + vr_path, + parse_projected_eigen=parse_projected_eigen is not False, + parse_eigen=(parse_projected_eigen is not False or label == "bulk"), + ) # vr.eigenvalues not needed for defects except for vr-only eigenvalue analysis + except Exception as vr_exc: + vr = get_vasprun(vr_path, parse_projected_eigen=False, parse_eigen=label == "bulk") + failed_eig_parsing_warning_message += f", got error:\n{vr_exc}" + + if parse_procar: + procar_path, multiple = _get_output_files_and_check_if_multiple("PROCAR", output_path) + if "PROCAR" in procar_path and parse_projected_eigen is not False: + try: + procar = get_procar(procar_path) + + except Exception as procar_exc: + failed_eig_parsing_warning_message += ( + f"\nThen got the following error when attempting to parse projected eigenvalues " + f"from the defect PROCAR(.gz):\n{procar_exc}" + ) + + if vr.projected_eigenvalues is None and procar is None and parse_projected_eigen is True: + # only warn if parse_projected_eigen is set to True (not None) + warnings.warn(failed_eig_parsing_warning_message) + + return vr, procar if parse_procar else vr + + class DefectParser: def __init__( self, @@ -1336,30 +1389,42 @@ def __init__( bulk_vr: Optional[Vasprun] = None, skip_corrections: bool = False, error_tolerance: float = 0.05, + parse_projected_eigen: Optional[bool] = None, **kwargs, ): """ - Create a DefectParser object, which has methods for parsing the results - of defect supercell calculations. + Create a ``DefectParser`` object, which has methods for parsing the + results of defect supercell calculations. - Direct initiation with DefectParser() is typically not recommended. Rather - DefectParser.from_paths() or defect_entry_from_paths() are preferred as + Direct initiation with ``DefectParser()`` is typically not recommended. Rather + ``DefectParser.from_paths()`` or ``defect_entry_from_paths()`` are preferred as shown in the doped parsing tutorials. Args: defect_entry (DefectEntry): - doped DefectEntry + doped ``DefectEntry`` defect_vr (Vasprun): - pymatgen Vasprun object for the defect supercell calculation + ``pymatgen`` ``Vasprun`` object for the defect supercell calculation bulk_vr (Vasprun): - pymatgen Vasprun object for the reference bulk supercell calculation + ``pymatgen`` ``Vasprun`` object for the reference bulk supercell calculation skip_corrections (bool): Whether to skip calculation and application of finite-size charge corrections to the defect energy (not recommended in most cases). Default = False. error_tolerance (float): - If the estimated error in the defect charge correction is greater - than this value (in eV), then a warning is raised. (default: 0.05 eV) + If the estimated error in the defect charge correction, based on the + variance of the potential in the sampling region is greater than this + value (in eV), then a warning is raised. (default: 0.05 eV) + parse_projected_eigen (bool): + Whether to parse the projected eigenvalues & orbitals from the bulk and + defect calculations (so ``DefectEntry.get_eigenvalue_analysis()`` can + then be used with no further parsing). Will initially try to load orbital + projections from ``vasprun.xml(.gz)`` files (slightly slower but more + accurate), or failing that from ``PROCAR(.gz)`` files if present in the + bulk/defect directories. Parsing this data can increase total parsing time + by anywhere from ~5-25%, so set to ``False`` if parsing speed is crucial. + Default is ``None``, which will attempt to load this data but with no + warning if it fails (otherwise if ``True`` a warning will be printed). **kwargs: Keyword arguments to pass to ``DefectParser()`` methods (``load_FNV_data()``, ``load_eFNV_data()``, ``load_bulk_gap_data()``) @@ -1375,6 +1440,7 @@ def __init__( self.skip_corrections = skip_corrections self.error_tolerance = error_tolerance self.kwargs = kwargs or {} + self.parse_projected_eigen = parse_projected_eigen @classmethod def from_paths( @@ -1435,8 +1501,9 @@ def from_paths( corrections to the defect energy (not recommended in most cases). Default = ``False``. error_tolerance (float): - If the estimated error in the defect charge correction is greater - than this value (in eV), then a warning is raised. (default: 0.05 eV) + If the estimated error in the defect charge correction, based on the + variance of the potential in the sampling region, is greater than this + value (in eV), then a warning is raised. (default: 0.05 eV) bulk_band_gap_path (str): Path to bulk ``OUTCAR`` file for determining the band gap. If the VBM/CBM occur at reciprocal space points not included in the bulk @@ -1485,7 +1552,16 @@ def from_paths( bulk_vr_path, dir_type="bulk", ) - bulk_vr = get_vasprun(bulk_vr_path) + bulk_vr, reparsed_bulk_procar = _parse_vr_and_poss_procar( + bulk_vr_path, + parse_projected_eigen, + bulk_path, + label="bulk", + parse_procar=bulk_procar is None, + ) + if bulk_procar is None and reparsed_bulk_procar is not None: + bulk_procar = reparsed_bulk_procar + parse_projected_eigen = bulk_vr.projected_eigenvalues is not None or bulk_procar is not None elif bulk_vr is None: raise ValueError("Either `bulk_path` or `bulk_vr` must be provided!") @@ -1504,30 +1580,10 @@ def from_paths( dir_type="defect", ) - defect_procar = None - try: - defect_vr = get_vasprun( - defect_vr_path, - parse_projected_eigen=parse_projected_eigen is not False, - parse_eigen=parse_projected_eigen is not False, - ) # vr.eigenvalues not needed for defects except for vr-only eigenvalue analysis - except Exception as defect_vr_exc: - defect_vr = get_vasprun(defect_vr_path, parse_projected_eigen=False, parse_eigen=False) - defect_procar_path, multiple = _get_output_files_and_check_if_multiple("PROCAR", defect_path) - if "PROCAR" in defect_procar_path and parse_projected_eigen is not False: - try: - defect_procar = get_procar(defect_procar_path) - - except Exception as procar_exc: - if parse_projected_eigen is not None: # otherwise no warning - warning_message = ( - f"Could not parse vasprun.xml.gz files in defect folder at {defect_path}, " - f"got error:\n{defect_vr_exc}\nThen got " - ) - warnings.warn( - f"{warning_message}the following error when attempting to parse projected " - f"eigenvalues from the defect PROCAR(.gz):\n{procar_exc}" - ) + defect_vr, defect_procar = _parse_vr_and_poss_procar( + defect_vr_path, parse_projected_eigen, defect_path, label="defect" + ) + parse_projected_eigen = defect_procar is not None or defect_vr.projected_eigenvalues is not None possible_defect_name = os.path.basename( defect_path.rstrip("/.").rstrip("/") # remove any trailing slashes to ensure correct name @@ -1538,7 +1594,7 @@ def from_paths( try: parsed_charge_state: int = _defect_charge_from_vasprun(bulk_vr, defect_vr, charge_state) except RuntimeError as orig_exc: # auto charge guessing failed and charge_state not provided, - # try determine from folder name - must have "-" or "+" at end of name for this + # try to determine from folder name - must have "-" or "+" at end of name for this try: charge_state_suffix = possible_defect_name.rsplit("_", 1)[-1] if charge_state_suffix[0] not in ["-", "+"]: @@ -1750,19 +1806,20 @@ def _read_bulk_voronoi_node_dict(bulk_path): bulk_vr=bulk_vr, skip_corrections=skip_corrections, error_tolerance=error_tolerance, + parse_projected_eigen=parse_projected_eigen, **kwargs, ) if parse_projected_eigen is not False: try: dp.defect_entry._load_and_parse_eigenvalue_data( - bulk_vr, - bulk_procar, + bulk_vr=bulk_vr, + bulk_procar=bulk_procar, defect_vr=defect_vr, defect_procar=defect_procar, ) except Exception as exc: - if parse_projected_eigen is not None: # otherwise no warning + if parse_projected_eigen is True: # otherwise no warning warnings.warn(f"Projected eigenvalues/orbitals parsing failed with error: {exc!r}") defect_vr.projected_eigenvalues = None # no longer needed, delete to reduce memory demand @@ -2066,7 +2123,12 @@ def load_and_check_calculation_metadata(self): bulk_vr_path, dir_type="bulk", ) - self.bulk_vr = get_vasprun(bulk_vr_path) + self.bulk_vr = _parse_vr_and_poss_procar( + bulk_vr_path, + parse_projected_eigen=False, # not needed for DefectEntry metadata + label="bulk", + parse_procar=False, + ) if not self.defect_vr: defect_vr_path, multiple = _get_output_files_and_check_if_multiple( @@ -2079,7 +2141,29 @@ def load_and_check_calculation_metadata(self): defect_vr_path, dir_type="defect", ) - self.defect_vr = get_vasprun(defect_vr_path) + self.defect_vr = _parse_vr_and_poss_procar( + defect_vr_path, + parse_projected_eigen=False, # not needed for DefectEntry metadata + label="defect", + parse_procar=False, + ) + + def _get_vr_dict_without_proj_eigenvalues(vr): + proj_eigen = vr.projected_eigenvalues + vr.projected_eigenvalues = None + vr_dict = vr.as_dict() # only call once + vr_dict_wout_proj = { # projected eigenvalue data might be present, but not needed (v slow + # and data-heavy) + **{k: v for k, v in vr_dict.items() if k != "output"}, + "output": { + k: v + for k, v in vr_dict["output"].items() + if k != "projected_eigenvalues" # reduce memory demand + }, + } + vr_dict_wout_proj["output"]["projected_eigenvalues"] = None + vr.projected_eigenvalues = proj_eigen # reset to original value + return vr_dict_wout_proj run_metadata = { # incars need to be as dict without module keys otherwise not JSONable: @@ -2091,19 +2175,9 @@ def load_and_check_calculation_metadata(self): "bulk_actual_kpoints": self.bulk_vr.actual_kpoints, "defect_potcar_symbols": self.defect_vr.potcar_spec, "bulk_potcar_symbols": self.bulk_vr.potcar_spec, - "defect_vasprun_dict": self.defect_vr.as_dict(), # projected_eigenvalues already removed - "bulk_vasprun_dict": { - **{ # projected_eigenvalues may not yet be removed - k: v for k, v in self.bulk_vr.as_dict().items() if k != "output" - }, - "output": { - k: v - for k, v in self.bulk_vr.as_dict()["output"].items() - if k != "projected_eigenvalues" - }, - }, + "defect_vasprun_dict": _get_vr_dict_without_proj_eigenvalues(self.defect_vr), + "bulk_vasprun_dict": _get_vr_dict_without_proj_eigenvalues(self.bulk_vr), } - run_metadata["bulk_vasprun_dict"]["output"]["projected_eigenvalues"] = None # reduce memory demand self.defect_entry.calculation_metadata["mismatching_INCAR_tags"] = _compare_incar_tags( run_metadata["bulk_incar"], run_metadata["defect_incar"] @@ -2122,8 +2196,8 @@ def load_and_check_calculation_metadata(self): def load_bulk_gap_data(self, bulk_band_gap_path=None, use_MP=False, mpid=None, api_key=None): """ - Get bulk band gap data from bulk OUTCAR file, or OUTCAR located at - ``actual_bulk_path``. + Get bulk band gap data from a bulk ``vasprun.xml(.gz)`` file located + in/at ``bulk_band_gap_path``. Alternatively, one can specify query the Materials Project (MP) database for the bulk gap data, using ``use_MP = True``, in which case the MP entry @@ -2134,17 +2208,18 @@ def load_bulk_gap_data(self, bulk_band_gap_path=None, use_MP=False, mpid=None, a Args: bulk_band_gap_path (str): - Path to bulk OUTCAR file for determining the band gap. If the VBM/CBM - occur at reciprocal space points not included in the bulk supercell - calculation, you should use this tag to point to a bulk bandstructure - calculation instead. If None, will use - self.defect_entry.calculation_metadata["bulk_path"]. + Path to bulk ``vasprun.xml(.gz)`` file for determining the band gap. + If the VBM/CBM occur at reciprocal space points not included in the bulk + supercell calculation, you should use this tag to point to a bulk + band-structure calculation instead. If None, will use + ``self.defect_entry.calculation_metadata["bulk_path"]``. use_MP (bool): If True, will query the Materials Project database for the bulk gap data. mpid (str): If provided, will query the Materials Project database for the bulk gap data, using this Materials Project ID. - api_key (str): Materials API key to access database. + api_key (str): + Materials API key to access database. """ if not self.bulk_vr: bulk_vr_path, multiple = _get_output_files_and_check_if_multiple( @@ -2156,7 +2231,12 @@ def load_bulk_gap_data(self, bulk_band_gap_path=None, use_MP=False, mpid=None, a f"{self.defect_entry.calculation_metadata['bulk_path']}. Using " f"{os.path.basename(bulk_vr_path)} to {_vasp_file_parsing_action_dict['vasprun.xml']}." ) - self.bulk_vr = get_vasprun(bulk_vr_path) + self.bulk_vr = _parse_vr_and_poss_procar( + bulk_vr_path, + parse_projected_eigen=self.parse_projected_eigen, + label="bulk", + parse_procar=False, + ) bulk_sc_structure = self.bulk_vr.initial_structure @@ -2233,18 +2313,17 @@ def load_bulk_gap_data(self, bulk_band_gap_path=None, use_MP=False, mpid=None, a gap_calculation_metadata["MP_gga_BScalc_data"] = None # to signal no MP BS is used if bulk_band_gap_path: - print(f"Using actual bulk path: {bulk_band_gap_path}") - actual_bulk_vr_path, multiple = _get_output_files_and_check_if_multiple( + bulk_gap_vr_path, multiple = _get_output_files_and_check_if_multiple( "vasprun.xml", bulk_band_gap_path ) if multiple: warnings.warn( f"Multiple `vasprun.xml` files found in specified directory: " - f"{bulk_band_gap_path}. Using {os.path.basename(actual_bulk_vr_path)} to " + f"{bulk_band_gap_path}. Using {os.path.basename(bulk_gap_vr_path)} to " f"{_vasp_file_parsing_action_dict['vasprun.xml']}." ) - actual_bulk_vr = get_vasprun(actual_bulk_vr_path) - band_gap, cbm, vbm, _ = actual_bulk_vr.eigenvalue_band_properties + bulk_gap_vr = get_vasprun(bulk_gap_vr_path, parse_projected_eigen=False) + band_gap, cbm, vbm, _ = bulk_gap_vr.eigenvalue_band_properties gap_calculation_metadata = { "mpid": mpid, diff --git a/doped/chemical_potentials.py b/doped/chemical_potentials.py index f91fb8cb..9aa5ea19 100644 --- a/doped/chemical_potentials.py +++ b/doped/chemical_potentials.py @@ -176,47 +176,50 @@ def _calculate_formation_energies(data: list, elemental: dict): """ for d in data: for el in elemental: - d[el] = Composition(d["formula"]).as_dict().get(el, 0) + d[el] = Composition(d["Formula"]).as_dict().get(el, 0) formation_energy_df = pd.DataFrame(data) - formation_energy_df["num_atoms_in_fu"] = formation_energy_df["formula"].apply( + formation_energy_df["num_atoms_in_fu"] = formation_energy_df["Formula"].apply( lambda x: Composition(x).num_atoms ) - formation_energy_df["num_species"] = formation_energy_df["formula"].apply( + formation_energy_df["num_species"] = formation_energy_df["Formula"].apply( lambda x: len(Composition(x).as_dict()) ) # get energy per fu then subtract elemental energies later, to get formation energies - if "energy_per_fu" in formation_energy_df.columns: - formation_energy_df["formation_energy_calc"] = formation_energy_df["energy_per_fu"] - if "energy_per_atom" not in formation_energy_df.columns: - formation_energy_df["energy_per_atom"] = formation_energy_df["energy_per_fu"] / ( + if "DFT Energy (eV/fu)" in formation_energy_df.columns: + formation_energy_df["formation_energy_calc"] = formation_energy_df["DFT Energy (eV/fu)"] + if "DFT Energy (eV/atom)" not in formation_energy_df.columns: + formation_energy_df["DFT Energy (eV/atom)"] = formation_energy_df["DFT Energy (eV/fu)"] / ( formation_energy_df["num_atoms_in_fu"] ) - elif "energy_per_atom" in formation_energy_df.columns: + elif "DFT Energy (eV/atom)" in formation_energy_df.columns: formation_energy_df["formation_energy_calc"] = ( - formation_energy_df["energy_per_atom"] * formation_energy_df["num_atoms_in_fu"] + formation_energy_df["DFT Energy (eV/atom)"] * formation_energy_df["num_atoms_in_fu"] ) - formation_energy_df["energy_per_fu"] = formation_energy_df["energy_per_atom"] * ( + formation_energy_df["DFT Energy (eV/fu)"] = formation_energy_df["DFT Energy (eV/atom)"] * ( formation_energy_df["num_atoms_in_fu"] ) else: raise ValueError( - "No energy data (energy_per_atom or energy_per_fu) found in input data to calculate " - "formation energies!" + "No energy data (DFT Energy (eV/atom) or per Formula Unit (eV/fu)) found in input " + "data to calculate formation energies!" ) for k, v in elemental.items(): formation_energy_df["formation_energy_calc"] -= formation_energy_df[k] * v - formation_energy_df["formation_energy"] = formation_energy_df["formation_energy_calc"] + formation_energy_df["Formation Energy (eV/fu)"] = formation_energy_df["formation_energy_calc"] + formation_energy_df["Formation Energy (eV/atom)"] = ( + formation_energy_df["formation_energy_calc"] / formation_energy_df["num_atoms_in_fu"] + ) formation_energy_df = formation_energy_df.drop(columns=["formation_energy_calc"]) # sort by num_species, then alphabetically, then by num_atoms_in_fu, then by formation_energy formation_energy_df = formation_energy_df.sort_values( - by=["num_species", "formula", "num_atoms_in_fu", "formation_energy"], + by=["num_species", "Formula", "num_atoms_in_fu", "Formation Energy (eV/fu)"], ) # drop num_atoms_in_fu and num_species return formation_energy_df.drop(columns=["num_atoms_in_fu", "num_species"]) @@ -551,7 +554,8 @@ def convergence_setup( + ",".join(str(k) for k in dict_set.kpoints.kpts[0]) ) fname = f"competing_phases/{self._competing_phase_name(e)}/kpoint_converge/{kname}" - # TODO: competing_phases folder name should be an optional parameter + # TODO: competing_phases folder name should be an optional parameter, and rename default + # to something that isn't so ugly? CompetingPhases? # TODO: Naming should be done in __init__ to ensure consistency and efficiency. Watch # out for cases where rounding can give same name (e.g. Te!) - should use # {formula}_MP_{mpid}_EaH_{round(e_above_hull,4)} as naming convention, to prevent any @@ -1264,17 +1268,18 @@ def from_vaspruns(self, path="competing_phases", folder="vasp_std", csv_path=Non self.elemental_energies[el] = v["output"]["final_energy_per_atom"] d = { - "formula": v["pretty_formula"], - "kpoints": kpoints, - "energy_per_fu": final_energy / formulas_per_unit, - "energy_per_atom": v["output"]["final_energy_per_atom"], - "energy": final_energy, + "Formula": v["pretty_formula"], + "k-points": kpoints, + "DFT Energy (eV/fu)": final_energy / formulas_per_unit, + "DFT Energy (eV/atom)": v["output"]["final_energy_per_atom"], + "DFT Energy (eV)": final_energy, } temp_data.append(d) formation_energy_df = _calculate_formation_energies(temp_data, self.elemental_energies) self.data = formation_energy_df.to_dict(orient="records") self.formation_energy_df = pd.DataFrame(self._get_and_sort_formation_energy_data()) # sort data + self.formation_energy_df.set_index("Formula") if csv_path is not None: self.to_csv(csv_path) @@ -1284,29 +1289,36 @@ def _get_and_sort_formation_energy_data(self, sort_by_energy=False, prune_polymo if prune_polymorphs: # only keep the lowest energy polymorphs formation_energy_df = _calculate_formation_energies(data, self.elemental_energies) - indices = formation_energy_df.groupby("formula")["energy_per_atom"].idxmin() + indices = formation_energy_df.groupby("Formula")["DFT Energy (eV/atom)"].idxmin() pruned_df = formation_energy_df.loc[indices] data = pruned_df.to_dict(orient="records") if sort_by_energy: - data = sorted(data, key=lambda x: x["formation_energy"], reverse=True) + data = sorted(data, key=lambda x: x["Formation Energy (eV/fu)"], reverse=True) # moves the bulk composition to the top of the list - _move_dict_to_start(data, "formula", self.bulk_composition.reduced_formula) + _move_dict_to_start(data, "Formula", self.bulk_composition.reduced_formula) # for each dict in data list, sort the keys as formula, formation_energy, energy_per_atom, # energy_per_fu, energy, kpoints, then by order of appearance in bulk_composition dict, # then alphabetically for any remaining: - data = [ + copied_data = copy.deepcopy(data) + formation_energy_data = [ { - "formula": d["formula"], - "formation_energy": d["formation_energy"], - "energy_per_atom": d["energy_per_atom"], - "energy_per_fu": d["energy_per_fu"], - "energy": d.get("energy"), - "kpoints": d.get("kpoints"), + **{ + k: d.pop(k, None) + for k in [ + "Formula", + "Formation Energy (eV/fu)", + "Formation Energy (eV/atom)", + "DFT Energy (eV/atom)", + "DFT Energy (eV/fu)", + "DFT Energy (eV)", + "k-points", + ] + }, **{ # num elts columns, sorted by order of occurrence in bulk composition: - str(elt): d.get(str(elt)) + str(elt): d.pop(str(elt), None) for elt in sorted( self.bulk_composition.elements, key=lambda x: self.bulk_composition.reduced_formula.index(str(x)), @@ -1315,22 +1327,24 @@ def _get_and_sort_formation_energy_data(self, sort_by_energy=False, prune_polymo **{ k: v for k, v in d.items() - if k - not in [ - "formula", - "formation_energy", - "energy_per_atom", - "energy_per_fu", - "energy", - "kpoints", - ] + if not any( + i in k + for i in [ + "Formula", + "Formation Energy", + "DFT Energy", + "k-points", + ] + ) }, } - for d in data + for d in copied_data ] # if all values are None for a certain key, remove that key from all dicts in list: - keys_to_remove = [k for k in data[0] if all(d[k] is None for d in data)] - return [{k: v for k, v in d.items() if k not in keys_to_remove} for d in data] + keys_to_remove = [ + k for k in formation_energy_data[0] if all(d[k] is None for d in formation_energy_data) + ] + return [{k: v for k, v in d.items() if k not in keys_to_remove} for d in formation_energy_data] def to_csv(self, csv_path, sort_by_energy=False, prune_polymorphs=False): """ @@ -1349,48 +1363,53 @@ def to_csv(self, csv_path, sort_by_energy=False, prune_polymorphs=False): Default is False. """ formation_energy_data = self._get_and_sort_formation_energy_data(sort_by_energy, prune_polymorphs) - pd.DataFrame(formation_energy_data).to_csv(csv_path, index=False) - print(f"Competing phase formation energies have been saved to {csv_path}.") + pd.DataFrame(formation_energy_data).set_index("Formula").to_csv(csv_path) + print(f"Competing phase formation energies have been saved to {csv_path}") def from_csv(self, csv_path): """ - Read in data from csv. + Read in data from a previously parsed formation energies csv file. Args: - csv_path (str): Path to csv file. Must have columns 'formula', - 'energy_per_fu', 'energy' and 'formation_energy' + csv_path (str): Path to csv file. Must have columns 'Formula', + and 'DFT Energy per Formula Unit (ev/fu)' or + 'DFT Energy per Atom (ev/atom)' Returns: - None, sets self.data and self.elemental_energies. + None, sets ``self.data`` and ``self.elemental_energies``. """ formation_energy_df = pd.read_csv(csv_path) - if "formula" not in list(formation_energy_df.columns) or all( - x not in list(formation_energy_df.columns) for x in ["energy_per_fu", "energy_per_atom"] + if "Formula" not in list(formation_energy_df.columns) or all( + x not in list(formation_energy_df.columns) + for x in [ + "DFT Energy (eV/fu)", + "DFT Energy (eV/atom)", + ] ): raise ValueError( - "Supplied csv does not contain the minimal columns required ('formula', " - "and 'energy_per_fu' or 'energy_per_atom'!" + "Supplied csv does not contain the minimal columns required ('Formula', and " + "'DFT Energy (eV/fu)' or 'DFT Energy (eV/atom)')" ) self.data = formation_energy_df.to_dict(orient="records") - self.elemental_energies = {} for i in self.data: - c = Composition(i["formula"]) + c = Composition(i["Formula"]) if len(c.elements) == 1: el = c.chemical_system - if "energy_per_atom" in list(formation_energy_df.columns): - el_energy_per_atom = i["energy_per_atom"] + if "DFT Energy (eV/atom)" in list(formation_energy_df.columns): + el_energy_per_atom = i["DFT Energy (eV/atom)"] else: - el_energy_per_atom = i["energy_per_fu"] / c.num_atoms + el_energy_per_atom = i["DFT Energy (eV/fu)"] / c.num_atoms if el not in self.elemental_energies or el_energy_per_atom < self.elemental_energies[el]: self.elemental_energies[el] = el_energy_per_atom - if "formation_energy" not in list(formation_energy_df.columns): + if "Formation Energy (eV/fu)" not in list(formation_energy_df.columns): formation_energy_df = _calculate_formation_energies(self.data, self.elemental_energies) self.data = formation_energy_df.to_dict(orient="records") self.formation_energy_df = pd.DataFrame(self._get_and_sort_formation_energy_data()) # sort data + self.formation_energy_df.set_index("Formula") def calculate_chempots(self, csv_path=None, verbose=True, sort_by=None): """ @@ -1412,15 +1431,15 @@ def calculate_chempots(self, csv_path=None, verbose=True, sort_by=None): extrinsic_formation_energies = [] bulk_pde_list = [] for d in self.data: - e = PDEntry(d["formula"], d["energy_per_fu"]) + e = PDEntry(d["Formula"], d["DFT Energy (eV/fu)"]) # checks if the phase is intrinsic - if set(Composition(d["formula"]).elements).issubset(self.bulk_composition.elements): + if set(Composition(d["Formula"]).elements).issubset(self.bulk_composition.elements): intrinsic_phase_diagram_entries.append(e) if e.composition == self.bulk_composition: # bulk phase bulk_pde_list.append(e) else: extrinsic_formation_energies.append( - {"formula": d["formula"], "formation_energy": d["formation_energy"]} + {k: v for k, v in d.items() if k in ["Formula", "Formation Energy (eV/fu)"]} ) if not bulk_pde_list: @@ -1483,11 +1502,16 @@ def calculate_chempots(self, csv_path=None, verbose=True, sort_by=None): phase_name_columns = [] for k, v in chempot_dict.items(): phase_name_columns.append(str(k)) - phase_energy_list.append(v) + phase_energy_list.append(round(v, 4)) chemical_potentials.append(phase_energy_list) # make df, will need it in next step - chempots_df = pd.DataFrame(chemical_potentials, columns=phase_name_columns) + chempots_df = pd.DataFrame( + chemical_potentials, + index=list(self._intrinsic_chempots["limits_wrt_el_refs"].keys()), + columns=phase_name_columns, + ) + chempots_df.index.name = "Limit" if self.extrinsic_species is not None: self._calculate_extrinsic_chempot_lims( # updates self._chempots @@ -1500,12 +1524,12 @@ def calculate_chempots(self, csv_path=None, verbose=True, sort_by=None): # save and print if csv_path is not None: - chempots_df.to_csv(csv_path, index=False) + chempots_df.to_csv(csv_path) if verbose: print("Saved chemical potential limits to csv file: ", csv_path) if verbose: - print("Calculated chemical potential limits: \n") + print("Calculated chemical potential limits (in eV wrt elemental reference phases): \n") print(chempots_df) return chempots_df @@ -1517,7 +1541,7 @@ def _calculate_extrinsic_chempot_lims(self, extrinsic_formation_energies, chempo for el in self.elemental: # TODO: This code (in all this module) should be rewritten to # be more readable (re-used and uninformative variable names, missing informative # comments...) - e[el] = Composition(e["formula"]).as_dict().get(el, 0) + e[el] = Composition(e["Formula"]).as_dict().get(el, 0) # gets the df into a slightly more convenient dict cpd = chempots_df.to_dict(orient="records") @@ -1527,16 +1551,16 @@ def _calculate_extrinsic_chempot_lims(self, extrinsic_formation_energies, chempo # print(f"df3: {df3}") # debugging for i, c in enumerate(cpd): name = f"mu_{self.extrinsic_species}_{i}" - df3[name] = df3["formation_energy"] + df3[name] = df3["Formation Energy (eV/fu)"] for k, v in c.items(): df3[name] -= df3[k] * v df3[name] /= df3[self.extrinsic_species] # find min at that chempot mins.append(df3[name].min()) - mins_formulas.append(df3.iloc[df3[name].idxmin()]["formula"]) + mins_formulas.append(df3.iloc[df3[name].idxmin()]["Formula"]) chempots_df[self.extrinsic_species] = mins - col_name = f"{self.extrinsic_species}_limiting_phase" + col_name = f"{self.extrinsic_species}-Limiting Phase" chempots_df[col_name] = mins_formulas # 1. work out the formation energies of all dopant competing @@ -1621,13 +1645,16 @@ def cplap_input(self, dependent_variable=None, filename="input.dat"): sub_dict for sub_dict in self.data if self.bulk_composition.reduced_composition - == Composition(sub_dict["formula"]).reduced_composition + == Composition(sub_dict["Formula"]).reduced_composition ] - bulk_entry = min(bulk_entries, key=lambda x: x["formation_energy"]) + bulk_entry = min(bulk_entries, key=lambda x: x["Formation Energy (eV/fu)"]) print(f"{len(self.bulk_composition.as_dict())} # number of elements in bulk") for k, v in self.bulk_composition.as_dict().items(): print(int(v), k, end=" ") - print(f"{bulk_entry['formation_energy']} # num_atoms, element, formation_energy (bulk)") + print( + f"{bulk_entry['Formation Energy (eV/fu)']} # number of atoms, element, formation " + f"energy (bulk)" + ) if dependent_variable is not None: print(f"{dependent_variable} # dependent variable (element)") @@ -1640,26 +1667,27 @@ def cplap_input(self, dependent_variable=None, filename="input.dat"): entries_for_cplap = [ entry_dict for entry_dict in self.data - if entry_dict["formula"] in bordering_phases - and Composition(entry_dict["formula"]).reduced_composition + if entry_dict["Formula"] in bordering_phases + and Composition(entry_dict["Formula"]).reduced_composition != self.bulk_composition.reduced_composition ] # cull to only the lowest energy entries of each composition culled_cplap_entries = {} for entry in entries_for_cplap: - reduced_comp = Composition(entry["formula"]).reduced_composition + reduced_comp = Composition(entry["Formula"]).reduced_composition if ( reduced_comp not in culled_cplap_entries - or entry["formation_energy"] < culled_cplap_entries[reduced_comp]["formation_energy"] + or entry["Formation Energy (eV/fu)"] + < culled_cplap_entries[reduced_comp]["Formation Energy (eV/fu)"] ): culled_cplap_entries[reduced_comp] = entry print(f"{len(culled_cplap_entries)} # number of bordering phases") for i in culled_cplap_entries.values(): - print(f"{len(Composition(i['formula']).as_dict())} # number of elements in phase:") - for k, v in Composition(i["formula"]).as_dict().items(): + print(f"{len(Composition(i['Formula']).as_dict())} # number of elements in phase:") + for k, v in Composition(i["Formula"]).as_dict().items(): print(int(v), k, end=" ") - print(f"{i['formation_energy']} # num_atoms, element, formation_energy") + print(f"{i['Formation Energy (eV/fu)']} # number of atoms, element, formation energy") def to_LaTeX_table(self, splits=1, sort_by_energy=False, prune_polymorphs=True): """ @@ -1691,67 +1719,70 @@ def to_LaTeX_table(self, splits=1, sort_by_energy=False, prune_polymorphs=True): # done in the pyscfermi report style formation_energy_data = self._get_and_sort_formation_energy_data(sort_by_energy, prune_polymorphs) - if any("kpoints" not in item for item in formation_energy_data): # TODO: this is bad, - # should just not have the kpoints column if it's not present, and warn user - raise ( - ValueError( - "kpoints need to be present in data; run CompetingPhasesAnalyzer.from_vaspruns " - "instead of from_csv" - ) - ) + kpoints_col = any("k-points" in item for item in formation_energy_data) string = "\\begin{table}[h]\n\\centering\n" string += ( - "\\caption{Formation energies ($\\Delta E_f$) per formula unit of \\ce{" + "\\caption{Formation energies per formula unit ($\\Delta E_f$) of \\ce{" + self.bulk_composition.reduced_formula - + "} and all competing phases, with k-meshes used in calculations." - + ("}\n" if not prune_polymorphs else " Only the lowest energy polymorphs are included}\n") + + "} and all competing phases" + + (", with k-meshes used in calculations." if kpoints_col else ".") + + (" Only the lowest energy polymorphs are included}\n" if prune_polymorphs else "}\n") ) string += "\\label{tab:competing_phase_formation_energies}\n" + column_names_string = "Formula" + (" & k-mesh" if kpoints_col else "") + " & $\\Delta E_f$ (eV/fu)" + if splits == 1: - string += "\\begin{tabular}{ccc}\n" + string += "\\begin{tabular}" + ("{ccc}" if kpoints_col else "{cc}") + "\n" string += "\\hline\n" - string += "Formula & k-mesh & $\\Delta E_f$ (eV) \\\\ \\hline \n" + string += column_names_string + " \\\\ \\hline \n" for i in formation_energy_data: - kpoints = i["kpoints"].split("x") - fe = i["formation_energy"] + kpoints = i.get("k-points", "0x0x0").split("x") + fe = i["Formation Energy (eV/fu)"] string += ( "\\ce{" - + i["formula"] - + "} & " - + f"{kpoints[0]}$\\times${kpoints[1]}$\\times${kpoints[2]}" - " & " + f"{fe:.3f} \\\\ \n" + + i["Formula"] + + "}" + + (f" & {kpoints[0]}$\\times${kpoints[1]}$\\times${kpoints[2]}" if kpoints_col else "") + + " & " + + f"{fe:.3f} \\\\ \n" ) elif splits == 2: - string += "\\begin{tabular}{ccc|ccc}\n" + string += "\\begin{tabular}" + ("{ccc|ccc}" if kpoints_col else "{cc|cc}") + "\n" string += "\\hline\n" - string += ( - "Formula & k-mesh & $\\Delta E_f$ (eV) & Formula & k-mesh & $\\Delta E_f$ (eV)\\\\ " - "\\hline \n" - ) + string += column_names_string + " & " + column_names_string + " \\\\ \\hline \n" mid = len(formation_energy_data) // 2 first_half = formation_energy_data[:mid] last_half = formation_energy_data[mid:] for i, j in zip(first_half, last_half): - kpoints = i["kpoints"].split("x") - fe = i["formation_energy"] - kpoints2 = j["kpoints"].split("x") - fe2 = j["formation_energy"] + kpoints1 = i.get("k-points", "0x0x0").split("x") + fe1 = i["Formation Energy (eV/fu)"] + kpoints2 = j.get("k-points", "0x0x0").split("x") + fe2 = j["Formation Energy (eV/fu)"] string += ( "\\ce{" - + i["formula"] - + "} & " - + f"{kpoints[0]}$\\times${kpoints[1]}$\\times${kpoints[2]}" - " & " - + f"{fe:.3f} & " + + i["Formula"] + + "}" + + ( + f" & {kpoints1[0]}$\\times${kpoints1[1]}$\\times${kpoints1[2]}" + if kpoints_col + else "" + ) + + " & " + + f"{fe1:.3f} & " + "\\ce{" - + j["formula"] - + "} & " - + f"{kpoints2[0]}$\\times${kpoints2[1]}$\\times${kpoints2[2]}" - " & " + f"{fe2:.3f} \\\\ \n" + + j["Formula"] + + "}" + + ( + f" & {kpoints2[0]}$\\times${kpoints2[1]}$\\times${kpoints2[2]}" + if kpoints_col + else "" + ) + + " & " + + f"{fe2:.3f} \\\\ \n" ) string += "\\hline\n" diff --git a/doped/core.py b/doped/core.py index 4a2d5d48..561d41ca 100644 --- a/doped/core.py +++ b/doped/core.py @@ -251,13 +251,15 @@ def from_dict(cls, d: dict): Returns: ``DefectEntry`` object """ - from doped.utils.parsing import _reset_warnings, suppress_logging + orig_simplefilter = warnings.simplefilter + warnings.simplefilter = lambda *args, **kwargs: None # avoid vise warning suppression + + from doped.utils.parsing import suppress_logging with suppress_logging(): obj = super().from_dict(d) - _reset_warnings() # vise suppresses ``UserWarning``s (and this initialises ``vise`` - # ``BandEdgeStates`` objects) so need to reset + warnings.simplefilter = orig_simplefilter # reset to original return obj @@ -277,10 +279,17 @@ def _check_correction_error_and_return_output( if ( correction_error > error_tolerance ): # greater than 50 meV error in charge correction, warn the user + if error_tolerance >= 0.01: # if greater than 10 meV, round energy values to meV: + error_val_string = f"{correction_error:.3f}" + error_tol_string = f"{error_tolerance:.3f}" + else: # else give in scientific notation: + error_val_string = f"{correction_error:.2e}" + error_tol_string = f"{error_tolerance:.2e}" + warnings.warn( f"Estimated error in the {'Freysoldt (FNV)' if type == 'FNV' else 'Kumagai (eFNV)'} " - f"charge correction for defect {self.name} is {correction_error:.3f} eV (i.e. which is " - f"greater than the `error_tolerance`: {error_tolerance:.3f} eV). You may want to check " + f"charge correction for defect {self.name} is {error_val_string} eV (i.e. which is " + f"greater than the `error_tolerance`: {error_tol_string} eV). You may want to check " f"the accuracy of the correction by plotting the site potential differences (using " f"`defect_entry.get_{'freysoldt' if type == 'FNV' else 'kumagai'}_correction()` with " f"`plot=True`). Large errors are often due to unstable or shallow defect charge states (" @@ -314,6 +323,11 @@ def get_freysoldt_correction( If this correction is used, please cite Freysoldt's original paper; 10.1103/PhysRevLett.102.016402. + The charge correction error is estimated by computing the average + standard deviation of the planar-averaged potential difference in the + sampling region, and multiplying by the defect charge. This is expected + to be a lower bound estimate of the true charge correction error. + Args: dielectric (float or int or 3x1 matrix or 3x3 matrix): Total dielectric constant of the host compound (including both @@ -352,7 +366,8 @@ def get_freysoldt_correction( (which gives an estimate of the error range of the correction energy). Default is False. error_tolerance (float): - If the estimated error in the charge correction is greater than + If the estimated error in the charge correction, based on the + variance of the potential in the sampling region, is greater than this value (in eV), then a warning is raised. (default: 0.05 eV) style_file (str): Path to a ``.mplstyle`` file to use for the plot. If ``None`` @@ -447,7 +462,12 @@ def get_kumagai_correction( For example, with layered materials, the defect charge is often localised to one layer, so we may want to adjust ``defect_region_radius`` and/or ``excluded_indices`` to ensure that only sites in other layers are used for - the sampling region (plateau) - see example on doped docs Tips page. + the sampling region (plateau) - see example on doped docs ``Tips`` page. + + The correction error is estimated by computing the standard error of the mean + of the sampled site potential differences, multiplied by the defect charge. + This is expected to be a lower bound estimate of the true charge correction + error. Args: dielectric (float or int or 3x1 matrix or 3x3 matrix): @@ -487,7 +507,8 @@ def get_kumagai_correction( (which gives an estimate of the error range of the correction energy). Default is False. error_tolerance (float): - If the estimated error in the charge correction is greater than + If the estimated error in the charge correction, based on the + variance of the potential in the sampling region, is greater than this value (in eV), then a warning is raised. (default: 0.05 eV) style_file (str): Path to a ``.mplstyle`` file to use for the plot. If ``None`` @@ -603,13 +624,10 @@ def _load_and_parse_eigenvalue_data( from doped.utils.parsing import ( _get_output_files_and_check_if_multiple, _multiple_files_warning, - _reset_warnings, get_procar, get_vasprun, ) - _reset_warnings() # vise suppresses `UserWarning`s, so need to reset - parsed_vr_procar_dict = {} for vr, procar, label in [(bulk_vr, bulk_procar, "bulk"), (defect_vr, defect_procar, "defect")]: path = self.calculation_metadata.get(f"{label}_path") @@ -680,6 +698,7 @@ def _load_and_parse_eigenvalue_data( defect_vr=defect_vr, bulk_procar=bulk_procar, # may be None, in which case Vasprun.projected_eigenvalues used defect_procar=defect_procar, # may be None, in which case Vasprun.projected_eigenvalues used + defect_supercell_site=self.defect_supercell_site, ) self.calculation_metadata["eigenvalue_data"] = { @@ -790,9 +809,6 @@ def get_eigenvalue_analysis( ``Figure`` object (if ``plot=True``). """ from doped.utils.eigenvalues import get_eigenvalue_analysis - from doped.utils.parsing import _reset_warnings - - _reset_warnings() # vise suppresses `UserWarning`s, so need to reset self._load_and_parse_eigenvalue_data( bulk_vr=bulk_vr, @@ -1739,6 +1755,36 @@ def from_json(cls, filename: str): """ return loadfn(filename) + def get_charge_states(self, padding: int = 1) -> list[int]: + """ + Refactored version of ``pymatgen-analysis-defects``'s + ``get_charge_states`` to not break when ``oxi_state`` is not set. + """ + if self.user_charges: + return self.user_charges + + if self.oxi_state is None or not isinstance(self.oxi_state, (int, float)): + self._set_oxi_state() # try guessing + + if self.oxi_state is None or not isinstance(self.oxi_state, (int, float)): # still not set + warnings.warn( + f"Defect oxidation state not set and couldn't be guessed, returning charge" + f"state range from -{padding} to +{padding}" + ) + return [*range(-padding, padding + 1)] + + if isinstance(self.oxi_state, int) or self.oxi_state.is_integer(): + oxi_state = int(self.oxi_state) + else: + raise ValueError("Oxidation state must be an integer") + + if oxi_state >= 0: + charges = [*range(-padding, oxi_state + padding + 1)] + else: + charges = [*range(oxi_state - padding, padding + 1)] + + return charges + def doped_defect_from_pmg_defect(defect: core.Defect, bulk_oxi_states=False, **doped_kwargs): """ diff --git a/doped/corrections.py b/doped/corrections.py index d807ad3e..94976d6a 100644 --- a/doped/corrections.py +++ b/doped/corrections.py @@ -36,6 +36,7 @@ """ import os +import warnings from typing import Optional, Union import matplotlib.pyplot as plt @@ -53,14 +54,11 @@ _get_bulk_supercell, _get_defect_supercell, _get_defect_supercell_bulk_site_coords, - _reset_warnings, get_locpot, get_outcar, ) from doped.utils.plotting import _get_backend, format_defect_name -_reset_warnings() # vise suppresses `UserWarning`s, so need to reset - def _monty_decode_nested_dicts(d): """ @@ -392,6 +390,9 @@ def get_kumagai_correction( CorrectionResults (summary of the corrections applied and metadata), and the matplotlib figure object if ``plot`` is True. """ + orig_simplefilter = warnings.simplefilter + warnings.simplefilter = lambda *args, **kwargs: None # monkey-patch to avoid vise warning suppression + # suppress pydefect INFO messages import logging @@ -414,7 +415,7 @@ def get_kumagai_correction( "You can do this by running `pip install pydefect`." ) from exc - _reset_warnings() # vise suppresses `UserWarning`s, so need to reset + warnings.simplefilter = orig_simplefilter # reset to original def doped_make_efnv_correction( charge: float, diff --git a/doped/thermodynamics.py b/doped/thermodynamics.py index 0f03f841..5b3dbc59 100644 --- a/doped/thermodynamics.py +++ b/doped/thermodynamics.py @@ -1310,7 +1310,7 @@ def _parse_fermi_dos(self, bulk_dos_vr: Union[str, Vasprun, FermiDos]): return bulk_dos_vr if isinstance(bulk_dos_vr, str): - bulk_dos_vr = get_vasprun(bulk_dos_vr) + bulk_dos_vr = get_vasprun(bulk_dos_vr, parse_dos=True) fermi_dos_band_gap, _cbm, fermi_dos_vbm, _ = bulk_dos_vr.eigenvalue_band_properties if abs(fermi_dos_vbm - self.vbm) > 0.1: @@ -2864,7 +2864,8 @@ def __repr__(self): def get_e_h_concs(fermi_dos: FermiDos, fermi_level: float, temperature: float) -> tuple[float, float]: """ Get the corresponding electron and hole concentrations (in cm^-3) for a - given Fermi level (in eV) and temperature (in K), for a FermiDos object. + given Fermi level (in eV) and temperature (in K), for a ``FermiDos`` + object. Note that the Fermi level here is NOT referenced to the VBM! So the Fermi level should be the corresponding eigenvalue within the calculation (or in @@ -2896,7 +2897,7 @@ def get_e_h_concs(fermi_dos: FermiDos, fermi_level: float, temperature: float) - return e_conc, h_conc -def scissor_dos(delta_gap: float, dos: Dos, tol=1e-8, verbose=True): +def scissor_dos(delta_gap: float, dos: Union[Dos, FermiDos], tol: float = 1e-8, verbose: bool = True): """ Given an input Dos/FermiDos object, rigidly shifts the valence and conduction bands of the DOS object to give a band gap that is now @@ -2989,4 +2990,6 @@ def scissor_dos(delta_gap: float, dos: Dos, tol=1e-8, verbose=True): if verbose: print(f"Orig gap: {dos.get_gap(tol=tol)}, new gap:{dos.get_gap(tol=tol) + delta_gap}") scissored_dos_dict["structure"] = dos.structure.as_dict() - return FermiDos.from_dict(scissored_dos_dict) + if isinstance(dos, FermiDos): + return FermiDos.from_dict(scissored_dos_dict) + return Dos.from_dict(scissored_dos_dict) diff --git a/doped/utils/eigenvalues.py b/doped/utils/eigenvalues.py index 4325e6e8..490c255c 100644 --- a/doped/utils/eigenvalues.py +++ b/doped/utils/eigenvalues.py @@ -16,24 +16,22 @@ import matplotlib.pyplot as plt import numpy as np -from pymatgen.core import Element, Species +from pymatgen.core.structure import PeriodicSite from pymatgen.electronic_structure.core import Spin from pymatgen.entries.computed_entries import ComputedStructureEntry from pymatgen.io.vasp.outputs import Procar, Vasprun from shakenbreak.plotting import _install_custom_font +from doped.analysis import defect_from_structures from doped.core import DefectEntry -from doped.utils.parsing import ( - _reset_warnings, - get_magnetization_from_vasprun, - get_nelect_from_vasprun, - get_procar, -) +from doped.utils.parsing import get_magnetization_from_vasprun, get_nelect_from_vasprun, get_procar from doped.utils.plotting import _get_backend +orig_simplefilter = warnings.simplefilter +warnings.simplefilter = lambda *args, **kwargs: None # monkey-patch to avoid vise warning suppression + if TYPE_CHECKING: from easyunfold.procar import Procar as EasyunfoldProcar - from pydefect.analyzer.make_defect_structure_info import DefectStructureInfo try: from vise import user_settings @@ -44,10 +42,8 @@ from pydefect.analyzer.band_edge_states import BandEdgeOrbitalInfos, OrbitalInfo, PerfectBandEdgeState from pydefect.analyzer.eigenvalue_plotter import EigenvalueMplPlotter from pydefect.analyzer.make_band_edge_states import make_band_edge_states - from pydefect.analyzer.make_defect_structure_info import MakeDefectStructureInfo from pydefect.cli.vasp.make_perfect_band_edge_state import get_edge_info from pydefect.defaults import defaults - from pydefect.util.structure_tools import Coordination, Distances from vise.analyzer.vasp.band_edge_properties import BandEdgeProperties, eigenvalues_from_vasprun except ImportError as exc: @@ -56,45 +52,7 @@ "You can do this by running `pip install pydefect`." ) from exc -_reset_warnings() # vise suppresses `UserWarning`s, so need to reset - - -def _coordination(self, include_on_site=True, cutoff_factor=None) -> "Coordination": - cutoff_factor = cutoff_factor or defaults.cutoff_distance_factor - cutoff = self.shortest_distance * cutoff_factor - elements = [element.specie.name for element in self.structure] - e_d = zip(elements, self.distances(remove_self=False)) - - unsorted_distances = defaultdict(list) - neighboring_atom_indices = [] - for i, (element, distance) in enumerate(e_d): - if distance < cutoff and include_on_site: - unsorted_distances[element].append(round(distance, 2)) - neighboring_atom_indices.append(i) - - distance_dict = {element: sorted(distances) for element, distances in unsorted_distances.items()} - return Coordination(distance_dict, round(cutoff, 3), neighboring_atom_indices) - - -def _distances(self, remove_self=True, specie=None) -> list[float]: - result = [] - lattice = self.structure.lattice - if isinstance(specie, Element): - el = specie.symbol - elif specie is None: - el = None - elif isinstance(specie, Species): - el = specie.element - for site in self.structure: - site_specie = site.specie.element if isinstance(site.specie, Species) else site.specie - if el and Element(el) != site_specie: - result.append(float("inf")) - continue - distance, _ = lattice.get_distance_and_image(site.frac_coords, self.coord) - if remove_self and distance < 1e-5: - continue - result.append(distance) - return result +warnings.simplefilter = orig_simplefilter # reset to original def band_edge_properties_from_vasprun( @@ -156,8 +114,13 @@ def make_perfect_band_edge_state_from_vasp( return PerfectBandEdgeState(vbm_info, cbm_info) -def _make_band_edge_orbital_infos_vr( - defect_vr: Vasprun, vbm: float, cbm: float, str_info: "DefectStructureInfo", eigval_shift: float = 0.0 +def make_band_edge_orbital_infos( + defect_vr: Vasprun, + vbm: float, + cbm: float, + eigval_shift: float = 0.0, + neighbor_indices: Optional[list[int]] = None, + defect_procar: Optional[Union["EasyunfoldProcar", Procar]] = None, ): """ Make ``BandEdgeOrbitalInfos`` from a ``Vasprun`` object. @@ -169,8 +132,15 @@ def _make_band_edge_orbital_infos_vr( defect_vr (Vasprun): Defect ``Vasprun`` object. vbm (float): VBM eigenvalue in eV. cbm (float): CBM eigenvalue in eV. - str_info (DefectStructureInfo): ``pydefect`` ``DefectStructureInfo``. - eigval_shift (float): Shift eigenvalues down by this value (to set VBM at 0 eV). + eigval_shift (float): + Shift eigenvalues down by this value (to set VBM at 0 eV). + Default is 0.0. + neighbor_indices (list[int]): + Indices of neighboring atoms to the defect site, for localisation analysis. + Default is ``None``. + defect_procar (EasyunfoldProcar, Procar): + ``EasyunfoldProcar`` or ``Procar`` object, for the defect supercell, + if projected eigenvalue/orbitals data is not provided in ``defect_vr``. Returns: ``BandEdgeOrbitalInfos `` object @@ -178,7 +148,6 @@ def _make_band_edge_orbital_infos_vr( eigval_range = defaults.eigval_range kpt_coords = [tuple(coord) for coord in defect_vr.actual_kpoints] max_energy_by_spin, min_energy_by_spin = [], [] - neighbors = str_info.neighbor_atom_indices for e in defect_vr.eigenvalues.values(): max_energy_by_spin.append(np.amax(e[:, :, 0], axis=0)) @@ -190,7 +159,8 @@ def _make_band_edge_orbital_infos_vr( lower_idx = np.argwhere(max_energy_by_band > vbm - eigval_range)[0][0] upper_idx = np.argwhere(min_energy_by_band < cbm + eigval_range)[-1][-1] - orbs, s = defect_vr.projected_eigenvalues, defect_vr.final_structure + orbs = defect_vr.projected_eigenvalues if defect_procar is None else defect_procar.data + s = defect_vr.final_structure orb_infos: list[Any] = [] for spin, eigvals in defect_vr.eigenvalues.items(): orb_infos.append([]) @@ -199,8 +169,8 @@ def _make_band_edge_orbital_infos_vr( for b_idx in range(lower_idx, upper_idx + 1): e, occ = eigvals[k_idx, b_idx, :] orbitals = make_bes.calc_orbital_character(orbs, s, spin, k_idx, b_idx) - if neighbors: - p_ratio = make_bes.calc_participation_ratio(orbs, spin, k_idx, b_idx, neighbors) + if neighbor_indices: + p_ratio = make_bes.calc_participation_ratio(orbs, spin, k_idx, b_idx, neighbor_indices) else: p_ratio = None orb_infos[-1][-1].append(OrbitalInfo(e, orbitals, occ, p_ratio)) @@ -215,7 +185,7 @@ def _make_band_edge_orbital_infos_vr( ) -def _parse_procar(procar: Union[str, "Path", "EasyunfoldProcar", Procar]): +def _parse_procar(procar: Optional[Union[str, Path, "EasyunfoldProcar", Procar]] = None): """ Parse a ``procar`` input to a ``Procar`` object in the correct format. @@ -226,14 +196,14 @@ def _parse_procar(procar: Union[str, "Path", "EasyunfoldProcar", Procar]): ``Procar`` object. """ if not hasattr(procar, "data"): # not a parsed Procar object - if hasattr(procar, "proj_data") and not isinstance(procar, (str, Path, Procar)): + if procar and hasattr(procar, "proj_data") and not isinstance(procar, (str, Path, Procar)): if procar._is_soc: procar.data = {Spin.up: procar.proj_data[0]} else: procar.data = {Spin.up: procar.proj_data[0], Spin.down: procar.proj_data[1]} del procar.proj_data - else: # path to PROCAR file + elif isinstance(procar, (str, Path)): # path to PROCAR file procar = get_procar(procar) return procar @@ -242,8 +212,10 @@ def _parse_procar(procar: Union[str, "Path", "EasyunfoldProcar", Procar]): def get_band_edge_info( bulk_vr: Vasprun, defect_vr: Vasprun, - bulk_procar: Optional[Union[str, "Path", "EasyunfoldProcar", Procar]] = None, - defect_procar: Optional[Union[str, "Path", "EasyunfoldProcar", Procar]] = None, + bulk_procar: Optional[Union[str, Path, "EasyunfoldProcar", Procar]] = None, + defect_procar: Optional[Union[str, Path, "EasyunfoldProcar", Procar]] = None, + defect_supercell_site: Optional[PeriodicSite] = None, + neighbor_cutoff_factor: float = 1.3, ): """ Generate metadata required for performing eigenvalue & orbital analysis, @@ -279,6 +251,19 @@ def get_band_edge_info( calculation. Not required if the supplied ``bulk_vr`` was parsed with ``parse_projected_eigen = True``. Default is ``None``. + defect_supercell_site (PeriodicSite): + ``PeriodicSite`` object of the defect site in the defect + supercell, from which the defect neighbours are determined + for localisation analysis. If ``None`` (default), then the + defect site is determined automatically from the defect + and bulk supercell structures. + neighbor_cutoff_factor (float): + Sites within ``min_distance * neighbor_cutoff_factor`` of + the defect site in the `relaxed` defect supercell are + considered neighbors for localisation analysis, where + ``min_distance`` is the minimum distance between sites in + the defect supercell. Default is 1.3 (matching the ``pydefect`` + default). Returns: ``pydefect`` ``BandEdgeOrbitalInfos``, and ``EdgeInfo`` objects @@ -290,23 +275,33 @@ def get_band_edge_info( bulk_procar = _parse_procar(bulk_procar) pbes = make_perfect_band_edge_state_from_vasp(vasprun=bulk_vr, procar=bulk_procar) - _orig_method_coor = Distances.coordination - Distances.coordination = _coordination - _orig_method_dist = Distances.distances - Distances.distances = _distances - - dsinfo = MakeDefectStructureInfo( # Using default values suggested by pydefect - bulk_vr.final_structure, - defect_vr.final_structure, - defect_vr.final_structure, - symprec=0.1, - dist_tol=1.0, - neighbor_cutoff_factor=1.3, # Neighbors are sites within min_dist * neighbor_cutoff_factor - ) + # get defect neighbour indices + sorted_distances = np.sort(defect_vr.final_structure.distance_matrix.flatten()) + min_distance = sorted_distances[sorted_distances > 0.5][0] + + if defect_supercell_site is None: + ( + _defect, + defect_site, # _relaxed_ defect site in supercell (if substitution/interstitial) + defect_site_in_bulk, # vacancy site + _defect_site_index, + _bulk_site_index, + _guessed_initial_defect_structure, + _unrelaxed_defect_structure, + _bulk_voronoi_node_dict, + ) = defect_from_structures( + bulk_vr.final_structure, + defect_vr.final_structure.copy(), + return_all_info=True, + oxi_state="Undetermined", + ) + defect_supercell_site = defect_site or defect_site_in_bulk - # Undo monkey patch in case used in other parts of the code: - Distances.coordination = _orig_method_coor - Distances.distances = _orig_method_dist + neighbor_indices = [ + i + for i, site in enumerate(defect_vr.final_structure.sites) + if defect_supercell_site.distance(site) <= min_distance * neighbor_cutoff_factor + ] from pydefect.analyzer.band_edge_states import logger @@ -319,25 +314,14 @@ def get_band_edge_info( vbm_info = get_edge_info(band_edge_prop.vbm_info, orbs, s, bulk_vr) cbm_info = get_edge_info(band_edge_prop.cbm_info, orbs, s, bulk_vr) - if defect_procar is not None: - defect_procar = _parse_procar(defect_procar) - band_orb = make_bes.make_band_edge_orbital_infos( - defect_procar, - defect_vr, - vbm_info.orbital_info.energy, - cbm_info.orbital_info.energy, - eigval_shift=-vbm_info.orbital_info.energy, - str_info=dsinfo.defect_structure_info, - ) - - else: - band_orb = _make_band_edge_orbital_infos_vr( - defect_vr, - vbm_info.orbital_info.energy, - cbm_info.orbital_info.energy, - eigval_shift=-vbm_info.orbital_info.energy, - str_info=dsinfo.defect_structure_info, - ) + band_orb = make_band_edge_orbital_infos( + defect_vr, + vbm_info.orbital_info.energy, + cbm_info.orbital_info.energy, + eigval_shift=-vbm_info.orbital_info.energy, + neighbor_indices=neighbor_indices, + defect_procar=_parse_procar(defect_procar), + ) return band_orb, vbm_info, cbm_info @@ -399,10 +383,10 @@ def get_eigenvalue_analysis( filename: Optional[str] = None, ks_labels: bool = False, style_file: Optional[str] = None, - bulk_vr: Optional[Union[str, "Path", Vasprun]] = None, - bulk_procar: Optional[Union[str, "Path", "EasyunfoldProcar", Procar]] = None, - defect_vr: Optional[Union[str, "Path", Vasprun]] = None, - defect_procar: Optional[Union[str, "Path", "EasyunfoldProcar", Procar]] = None, + bulk_vr: Optional[Union[str, Path, Vasprun]] = None, + bulk_procar: Optional[Union[str, Path, "EasyunfoldProcar", Procar]] = None, + defect_vr: Optional[Union[str, Path, Vasprun]] = None, + defect_procar: Optional[Union[str, Path, "EasyunfoldProcar", Procar]] = None, force_reparse: bool = False, ylims: Optional[tuple[float, float]] = None, legend_kwargs: Optional[dict] = None, @@ -647,8 +631,8 @@ def _orbital_diff(orbital_1: dict, orbital_2: dict) -> float: ) if len(emp.axs) > 1: - emp.axs[0].set_title("Spin Down") - emp.axs[1].set_title("Spin Up") + emp.axs[0].set_title("Spin Up") + emp.axs[1].set_title("Spin Down") else: emp.axs[0].set_title("KS levels") diff --git a/doped/utils/parsing.py b/doped/utils/parsing.py index bc0e805f..b1edd26b 100644 --- a/doped/utils/parsing.py +++ b/doped/utils/parsing.py @@ -21,7 +21,6 @@ from pymatgen.io.vasp.outputs import Locpot, Outcar, Procar, Vasprun, _parse_vasp_array from pymatgen.util.coord import pbc_diff -from doped import _ignore_pmg_warnings from doped.core import DefectEntry if TYPE_CHECKING: @@ -33,17 +32,6 @@ def _get_potcar_summary_stats() -> dict: return loadfn(POTCAR_STATS_PATH) -def _reset_warnings(): - """ - When importing ``vise``/``pydefect``, ``UserWarning``s are suppressed, so - we need to reset. - """ - warnings.simplefilter("default") - warnings.filterwarnings("ignore", message="`np.int` is a deprecated alias for the builtin `int`") - warnings.filterwarnings("ignore", message="Use get_magnetic_symmetry()") - _ignore_pmg_warnings() - - @contextlib.contextmanager def suppress_logging(level=logging.CRITICAL): """ @@ -541,6 +529,9 @@ def check_atom_mapping_far_from_defect(bulk, defect, defect_coords): warn the user if they are large (often indicates a mismatch between the bulk and defect supercell definitions). """ + orig_simplefilter = warnings.simplefilter + warnings.simplefilter = lambda *args, **kwargs: None # monkey-patch to avoid vise warning suppression + # suppress pydefect INFO messages import logging @@ -553,7 +544,7 @@ def check_atom_mapping_far_from_defect(bulk, defect, defect_coords): except ImportError: # can't check as vise/pydefect not installed. Not critical so just return return - _reset_warnings() # vise suppresses `UserWarning`s, so need to reset + warnings.simplefilter = orig_simplefilter # reset to original far_from_defect_disps = {site.specie.symbol: [] for site in bulk} diff --git a/doped/utils/plotting.py b/doped/utils/plotting.py index f531d8d5..697dafe4 100644 --- a/doped/utils/plotting.py +++ b/doped/utils/plotting.py @@ -627,7 +627,7 @@ def _get_legends_txt(for_legend, all_entries=False): while defect_name in legends_txt: i += 1 - defect_name = f"{defect_name}$_{{-{chr(96 + i)}}}$" # d, e, f etc + defect_name = f"{defect_name.rsplit('$_', 1)[0]}$_{{-{chr(96 + i)}}}$" # d, e, f etc legends_txt.append(defect_name) @@ -663,7 +663,7 @@ def _rename_key_and_dicts( while key in output_dict: i += 1 - key = f"{key}_{chr(96 + i)}" # d, e, f etc + key = f"{key.rsplit('_', 1)[0]}_{chr(96 + i)}" # d, e, f etc return key, output_dicts diff --git a/doped/vasp.py b/doped/vasp.py index 9cfa3980..7deb3013 100644 --- a/doped/vasp.py +++ b/doped/vasp.py @@ -353,7 +353,6 @@ def __init__( """ _ignore_pmg_warnings() self.charge_state = charge_state - self.potcars = self._check_user_potcars(unperturbed_poscar=True, snb=False) self.poscar_comment = ( poscar_comment or f"{structure.formula} {'+' if self.charge_state > 0 else ''}{self.charge_state}" @@ -517,11 +516,12 @@ def write_input( snb (bool): If input structures are from ShakeNBreak (so POSCARs aren't 'unperturbed'). (default: False) """ - if not potcar_spec: - potcars = self._check_user_potcars(unperturbed_poscar=unperturbed_poscar, snb=snb) - else: + if potcar_spec: potcars = True + else: + potcars = self._check_user_potcars(unperturbed_poscar=unperturbed_poscar, snb=snb) + if unperturbed_poscar and potcars: # write everything, use DictSet.write_input() try: super().write_input( @@ -532,12 +532,14 @@ def write_input( zip_output=zip_output, ) except ValueError as e: - if str(e).startswith("NELECT") and potcar_spec: - with zopen(os.path.join(output_path, "POTCAR.spec"), "wt") as pot_spec_file: - pot_spec_file.write("\n".join(self.potcar_symbols)) + if not str(e).startswith("NELECT") or not potcar_spec: + raise e - self.kpoints.write_file(f"{output_path}/KPOINTS") - self.poscar.write_file(f"{output_path}/POSCAR") + with zopen(os.path.join(output_path, "POTCAR.spec"), "wt") as pot_spec_file: + pot_spec_file.write("\n".join(self.potcar_symbols)) + + self.kpoints.write_file(f"{output_path}/KPOINTS") + self.poscar.write_file(f"{output_path}/POSCAR") else: # use `write_file()`s rather than `write_input()` to avoid writing POSCARs/POTCARs os.makedirs(output_path, exist_ok=True) @@ -1182,7 +1184,7 @@ def bulk_vasp_nkred_std(self) -> Optional[DefectDictSet]: if nkred_defect_dict_set is None: return None - if nkred_defect_dict_set._check_user_potcars(unperturbed_poscar=True, snb=False): + if any("VASP_PSP_DIR" in i for i in SETTINGS): # POTCARs available user_incar_settings = copy.deepcopy(self.user_incar_settings) user_incar_settings.update(singleshot_incar_settings) user_incar_settings.update( # add NKRED settings diff --git a/examples/Cu2SiSe3/Cu2SiSe3_int_band_edge_states.json b/examples/Cu2SiSe3/Cu2SiSe3_int_band_edge_states.json index 39c8853f..82f406e7 100644 --- a/examples/Cu2SiSe3/Cu2SiSe3_int_band_edge_states.json +++ b/examples/Cu2SiSe3/Cu2SiSe3_int_band_edge_states.json @@ -1 +1 @@ -{"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeStates", "@version": "0.9.1", "states": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeState", "@version": "0.9.1", "vbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.1", "band_idx": 352, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.1", "energy": 3.6647, "orbitals": {"Cu": [0.002, 0.021, 0.34, 0.0], "Se": [0.005, 0.321, 0.005, 0.0], "Si": [0.017, 0.016, 0.0, 0.0]}, "occupation": 1.0, "participation_ratio": 0.13585164835164834}}, "cbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.1", "band_idx": 355, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.1", "energy": 5.349, "orbitals": {"Cu": [0.09, 0.007, 0.021, 0.0], "Se": [0.185, 0.121, 0.008, 0.0], "Si": [0.132, 0.029, 0.0, 0.0]}, "occupation": 0.0, "participation_ratio": 0.04306704948488431}}, "vbm_orbital_diff": 0.12364130434782608, "cbm_orbital_diff": 0.09950248756218906, "localized_orbitals": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.1", "band_idx": 353, "ave_energy": 3.94005, "occupation": 1.0, "orbitals": {"Cu": [0.07300000000000001, 0.219, 1.96, 0.0], "Se": [0.14, 1.8729999999999998, 0.06, 0.0], "Si": [0.36499999999999994, 0.665, 0.0, 0.0]}, "participation_ratio": 0.5398908363578341, "radius": null, "center": null}, {"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.1", "band_idx": 354, "ave_energy": 4.5089749999999995, "occupation": 1.0, "orbitals": {"Cu": [0.15299999999999997, 0.33699999999999997, 1.377, 0.0], "Se": [0.135, 1.328, 0.104, 0.0], "Si": [0.106, 1.1549999999999998, 0.0, 0.0]}, "participation_ratio": 0.6583871298800835, "radius": null, "center": null}], "vbm_hole_occupation": 0.0, "cbm_electron_occupation": 0.0}, {"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeState", "@version": "0.9.1", "vbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.1", "band_idx": 352, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.1", "energy": 3.7174, "orbitals": {"Cu": [0.002, 0.021, 0.347, 0.0], "Se": [0.003, 0.342, 0.004, 0.0], "Si": [0.012, 0.005, 0.0, 0.0]}, "occupation": 1.0, "participation_ratio": 0.10526315789473682}}, "cbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.1", "band_idx": 354, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.1", "energy": 5.3524, "orbitals": {"Cu": [0.089, 0.007, 0.021, 0.0], "Se": [0.184, 0.122, 0.007, 0.0], "Si": [0.132, 0.029, 0.0, 0.0]}, "occupation": 0.0, "participation_ratio": 0.042854732579719924}}, "vbm_orbital_diff": 0.05978260869565217, "cbm_orbital_diff": 0.10281923714759536, "localized_orbitals": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.1", "band_idx": 353, "ave_energy": 4.083975000000001, "occupation": 1.0, "orbitals": {"Cu": [0.07600000000000001, 0.236, 2.036, 0.0], "Se": [0.16, 1.6980000000000002, 0.06899999999999999, 0.0], "Si": [0.439, 0.653, 0.0, 0.0]}, "participation_ratio": 0.6083461369016441, "radius": null, "center": null}], "vbm_hole_occupation": 0.0, "cbm_electron_occupation": 0.0}]} \ No newline at end of file +{"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeStates", "@version": "0.9.3", "states": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeState", "@version": "0.9.3", "vbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.3", "band_idx": 352, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.3", "energy": 3.6647, "orbitals": {"Cu": [0.002, 0.021, 0.34, 0.0], "Se": [0.005, 0.321, 0.005, 0.0], "Si": [0.017, 0.016, 0.0, 0.0]}, "occupation": 1.0, "participation_ratio": 0.11717032967032967}}, "cbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.3", "band_idx": 355, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.3", "energy": 5.349, "orbitals": {"Cu": [0.09, 0.007, 0.021, 0.0], "Se": [0.185, 0.121, 0.008, 0.0], "Si": [0.132, 0.029, 0.0, 0.0]}, "occupation": 0.0, "participation_ratio": 0.020942408376963352}}, "vbm_orbital_diff": 0.12364130434782608, "cbm_orbital_diff": 0.09950248756218906, "localized_orbitals": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.3", "band_idx": 353, "ave_energy": 3.94005, "occupation": 1.0, "orbitals": {"Cu": [0.07300000000000001, 0.219, 1.96, 0.0], "Se": [0.14, 1.8729999999999998, 0.06, 0.0], "Si": [0.36499999999999994, 0.665, 0.0, 0.0]}, "participation_ratio": 0.49546802087270597, "radius": null, "center": null}, {"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.3", "band_idx": 354, "ave_energy": 4.5089749999999995, "occupation": 1.0, "orbitals": {"Cu": [0.15299999999999997, 0.33699999999999997, 1.377, 0.0], "Se": [0.135, 1.328, 0.104, 0.0], "Si": [0.106, 1.1549999999999998, 0.0, 0.0]}, "participation_ratio": 0.5050764162866771, "radius": null, "center": null}], "vbm_hole_occupation": 0.0, "cbm_electron_occupation": 0.0}, {"@module": "pydefect.analyzer.band_edge_states", "@class": "BandEdgeState", "@version": "0.9.3", "vbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.3", "band_idx": 352, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.3", "energy": 3.7174, "orbitals": {"Cu": [0.002, 0.021, 0.347, 0.0], "Se": [0.003, 0.342, 0.004, 0.0], "Si": [0.012, 0.005, 0.0, 0.0]}, "occupation": 1.0, "participation_ratio": 0.07615939072487418}}, "cbm_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "EdgeInfo", "@version": "0.9.3", "band_idx": 354, "kpt_coord": [0.0, 0.0, 0.0], "orbital_info": {"@module": "pydefect.analyzer.band_edge_states", "@class": "OrbitalInfo", "@version": "0.9.3", "energy": 5.3524, "orbitals": {"Cu": [0.089, 0.007, 0.021, 0.0], "Se": [0.184, 0.122, 0.007, 0.0], "Si": [0.132, 0.029, 0.0, 0.0]}, "occupation": 0.0, "participation_ratio": 0.021258646870254767}}, "vbm_orbital_diff": 0.05978260869565217, "cbm_orbital_diff": 0.10281923714759536, "localized_orbitals": [{"@module": "pydefect.analyzer.band_edge_states", "@class": "LocalizedOrbital", "@version": "0.9.3", "band_idx": 353, "ave_energy": 4.083975000000001, "occupation": 1.0, "orbitals": {"Cu": [0.07600000000000001, 0.236, 2.036, 0.0], "Se": [0.16, 1.6980000000000002, 0.06899999999999999, 0.0], "Si": [0.439, 0.653, 0.0, 0.0]}, "participation_ratio": 0.5579097586972654, "radius": null, "center": null}], "vbm_hole_occupation": 0.0, "cbm_electron_occupation": 0.0}]} \ No newline at end of file diff --git a/examples/GGA_workflow_tutorial.ipynb b/examples/GGA_workflow_tutorial.ipynb index 765db2d8..43ea55fe 100644 --- a/examples/GGA_workflow_tutorial.ipynb +++ b/examples/GGA_workflow_tutorial.ipynb @@ -3192,9 +3192,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "> The current algorithm for how `doped` queries the Materials Project (MP) and determines relevant competing phases to be calculated, is that it first queries the MP for all phases with energies above hull less than `e_above_hull` (optional parameter in `CompetingPhases()`) in eV/atom in the chemical space of the host material. It then determines which of these phases border the host material in the phase diagram (i.e. which are competing phases and thus determine the chemical potentials), as well as which phases _would_ border the host material if their energies were downshifted by `e_above_hull`. The latter are included as well, and so `e_above_hull` acts as an uncertainty range for the MP-calculated formation energies, which may not be accurate due to functional choice (GGA vs hybrid DFT / GGA+U / RPA etc.), lack of vdW corrections etc. \n", + "```{note}\n", + "The current algorithm for how `doped` queries the Materials Project (MP) and determines relevant competing phases to be calculated, is that it first queries the MP for all phases with energies above hull less than `e_above_hull` (optional parameter in `CompetingPhases()`) in eV/atom in the chemical space of the host material. It then determines which of these phases border the host material in the phase diagram (i.e. which are competing phases and thus determine the chemical potentials), as well as which phases _would_ border the host material if their energies were downshifted by `e_above_hull`. The latter are included as well, and so `e_above_hull` acts as an uncertainty range for the MP-calculated formation energies, which may not be accurate due to functional choice (GGA vs hybrid DFT / GGA+U / RPA etc.), lack of vdW corrections etc. \n", "\n", - "> In this qualitative tutorial, since we're using GGA, we use a less strict `e_above_hull` than the default (0.01 vs 0.1 eV/atom). But in general, it's recommended to use the default `e_above_hull` of 0.1 eV/atom, which is a reasonable value." + "In this qualitative tutorial, since we're using GGA, we use a less strict `e_above_hull` than the default (0.01 vs 0.1 eV/atom). But in general, it's recommended to use the default `e_above_hull` of 0.1 eV/atom, which is a reasonable value.\n", + "```" ] }, { @@ -5203,7 +5205,7 @@ "the supplied `dielectric` constant and calculation outputs, and will\n", "warn you if any required outputs are missing. \n", "\n", - "Additionally, the `DefectsParser` class automatically checks the consistency and estimated error of the defect finite-size charge correction, and will warn you if the estimated error is above `error_tolerance` (50 meV by default). As shown later in the [Charge Corrections](#charge_corrections) section, we can directly visualise the \n", + "Additionally, the `DefectsParser` class automatically checks the consistency and estimated error of the defect finite-size charge correction (using the standard error of the mean potential difference in the sampling region, times the defect charge), and will warn you if the estimated error is above `error_tolerance` – 50 meV by default. As shown later in the [Charge Corrections](#charge_corrections) section, we can directly visualise the \n", "finite-size charge correction plots (showing how they are being computed) easily with `doped`, which is \n", "recommended if any of these warnings about the charge correction accuracy are printed.\n" ] @@ -5381,7 +5383,7 @@ "To calculate and plot defect formation energies, we need to know the chemical potentials of the elements\n", " in the system (see the [YouTube defects tutorial](https://youtu.be/FWz7nm9qoNg) for more details on\n", " this).\n", - "Since we have calculated the chemical potentials in the previous section [Chemical Potentials](#chemical_potentials), we can just load the results from the JSON file here:" + "Since we have calculated the chemical potentials in the previous ``Chemical Potentials`` section, we can just load the results from the JSON file here:" ] }, { @@ -5492,6 +5494,15 @@ ")" ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "```{tip}\n", + "As shown above, can specify the chemical potential limit at which to obtain and plot the defect formation energies using the ``limit`` parameter, which we can set to either ``\"X-rich\"/\"X-poor\"`` where X is an element in the system, in which case the most X-rich/poor limit will be used (e.g. \"Cd-rich\"), or a key in the ``chempots[\"limits\"]`` dictionary (e.g. ``\"Cd-CdTe\"`` from that shown above). Alternatively, one can also provide a single chemical potential limit in the form of a dictonary to the ``DefectThermodynamics`` methods – see docstrings for more details.\n", + "``` " + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/examples/advanced_analysis_tutorial.ipynb b/examples/advanced_analysis_tutorial.ipynb index 1b89fe0d..aed1084e 100644 --- a/examples/advanced_analysis_tutorial.ipynb +++ b/examples/advanced_analysis_tutorial.ipynb @@ -45,21 +45,21 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "38c970b5e311206c", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-28T20:48:12.780487Z", - "start_time": "2024-03-28T20:48:06.223535Z" - }, - "collapsed": false + "end_time": "2024-04-09T21:47:43.626323Z", + "start_time": "2024-04-09T21:47:35.381640Z" + } }, - "outputs": [], "source": [ "%matplotlib inline\n", "from monty.serialization import loadfn\n", "CdTe_defects_thermo = loadfn(\"CdTe/CdTe_thermo_wout_meta.json\") # load our DefectThermodynamics object" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", @@ -825,52 +825,52 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "48f67363f3e0869d", "metadata": { - "ExecuteTime": { - "end_time": "2024-03-28T17:32:14.612226Z", - "start_time": "2024-03-28T17:31:52.949931Z" - }, "collapsed": false, - "tags": [] + "tags": [], + "ExecuteTime": { + "end_time": "2024-04-09T21:48:12.383744Z", + "start_time": "2024-04-09T21:47:54.757810Z" + } }, - "outputs": [], "source": [ "from doped.analysis import DefectParser\n", "defect_entry = DefectParser.from_paths(defect_path = \"Cu2SiSe3/v_Cu_0/vasp_std\",\n", " bulk_path = \"Cu2SiSe3/bulk/vasp_std\",\n", " dielectric = [[8.73, 0, -0.48],[0., 7.78, 0],[-0.48, 0, 10.11]]\n", " ).defect_entry" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 2, "id": "79b2dbbc4aa3f194", "metadata": { - "ExecuteTime": { - "end_time": "2024-03-28T17:32:15.401814Z", - "start_time": "2024-03-28T17:32:14.613463Z" - }, "collapsed": false, - "tags": [] + "tags": [], + "ExecuteTime": { + "end_time": "2024-04-09T21:48:13.253485Z", + "start_time": "2024-04-09T21:48:12.385Z" + } }, + "source": [ + "bes, fig = defect_entry.get_eigenvalue_analysis()" + ], "outputs": [ { "data": { - "image/png": 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AAKA62P5fzQUAAACAMli+fHkcdthhMWXKlJRzatWqFY888kiceuqpOeys6vrggw/ihBNOSPmq1V133TXGjh0bL7/8cgwcOLDCT+KrU6dO/PCHP4x33nknRowYEQ0aNEg7f+3atXH00UfHsmXLKlQvnQ8++CAGDhwYa9euTTuvbdu28cwzz8SsWbPiyCOPjLy8vArVq1WrVpx44onx5ptvxqhRo0p9XfKyZcti4MCBZX71b3Vz1llnxTvvvLPNsX79+sW7774bI0aMiD322KPCNRo2bBgXX3xxvPfee9GvX7+0c7du3RpnnXVWbNy4scL1KLslS5bEgAEDori4OO28li1bxqOPPhqvv/56DB06NGrVqlWhevn5+dGvX7+YMmVKTJgwodTvq7vvvjueeOKJCtWKSPb5kuS9p/LrX/96m+ddrVq14sYbb4w33ngjTj311KhZM3sv7xs/fnz8/Oc/L3XeHnvsEVOnTo0ZM2bEYYcdVuHrUrdu3TjrrLPivffeizvvvDPlv/fvvfdenHjiibFly5YK1SmLzz//PH7729+mnXP11VfHnDlzok+fPhmpuccee8Rll10WixYtitGjR0enTp3+NdarV6849thjM1IHAACgOhAcBQAAACDrli1bFgcffHDaV0bXqVMnHn/88RgyZEgOO6u6SkpKYtiwYSlfJ3zCCSfEW2+9Fccdd1zGatasWTPOO++8eP3116NHjx5p53766adx9NFHp329b3kVFxdH//7944svvkg770c/+lG8+eabcdRRR2Wsdn5+fgwbNizmzZtX6rqvvfZanHTSSRmrXVU89dRT8cgjj3zr6zVq1Ii77rorJk2aFK1bt85YvebNm8fEiRPjpz/9adp5b7/9dvzhD3/IWF22bfXq1dGvX7/49NNP08474YQTYv78+TF06NAKh9e2ZeDAgTF37txSg1tnnXVWqWfEtiT5fEny3lP56KOPYsSIEd/6eosWLWLmzJlx2WWXVTgQXVbz58+PU045JbZu3Zp23vDhw+ONN96Iww8/PGO1a9asGRdccEG8+eab0bNnz23OmTx5ctx+++0Zq/nfRo4cmTYo/Mc//jGuueaaCv9STDr5+fkxePDgePPNN+Puu++OgoKCuPnmmzNeBwAAoCoTHAUAAAAgqxYsWBC9evVK+RTDiIh69erF+PHj4+ijj85hZ1Xb/fffH7Nnz97m2OWXXx6PPfZYNG3aNCu199prr5gxY0apAa433ngjrr/++ozVvfjii+Pdd99NO+eGG26IBx54IBo2bJixuv9u1113jUmTJsWZZ56Zdt7EiRPjvvvuy0oPlWVb4aVvAt3nn39+Vmrm5eXFn/70pxg+fHjaeX/84x9j/fr1WemBf7rkkktiwYIFaedcccUV8dhjj0WzZs2y0kNBQUGMHTs2zjvvvG2O5+fnxxlnnBEFBQXlXjvJ50uS957KG2+8EZs2bfqPr7Vs2TJmzJgRBx54YNbrb9myJc4888xYs2ZNyjl5eXlx8803x8iRI2OnnXbKSh977LFHTJs2LeUv7UycODErdSMiHnzwwZRjxx9/fPzsZz/LWu1v5Ofnx7nnnhsffvhh9OrVK+v1AAAAqhLBUQAAAACy5qWXXoqDDz447SvNd9ppp5g0aVJGn3C2I3j00Ue3+fWbb745brjhhqzXr1u3bjz++ONxyimnpJ130003xVtvvbXd9aZOnVpqWOjuu++Oyy+/fLtrlaZGjRpx//33l/okzEsuuSTt9/aO4P7778/Jq3vvvvvuKCwsTDn+xRdfxEMPPZT1PpLq2WefjXvvvTftnNtvvz2uu+66rPeSl5cXd91117cChnvssUfMnDkzbrnllqhTp0651kzy+ZLkvZdHnTp1Yvz48dGxY8ec1PvjH/8YL7/8cto5f/7zn+NXv/pV1nupU6dOPProo/HDH/4w67W+sXTp0vjggw9Sjuf66Z9NmjTJaT0AAICqQHAUAAAAgKyYPHlyHH744SlftR4R0ahRo3jmmWeid+/euWusGjvnnHNyEiL5Ro0aNeLBBx9M+xSuTZs2pXw6YFlt3LgxzjrrrLRzLrroojj33HO3q055/fGPf4z+/funHF+1alVccMEFOewoty6++OI49dRTc1KrVq1acf/996d99fnIkSNz0kvSbNy4MYYPHx4lJSUp5/zsZz+Ln//85znrKS8vL+655574/ve/HxERZ599drzxxhtx0EEHlXutJJ8vSd57ef3+97//1/dbtn322Wdx1VVXpZ3zq1/9Ks4+++yc9BPxzydvjhw5Mg499NCc1HvllVdSjh1wwAHx3e9+Nyd9AAAAJJngKAAAAAAZ98gjj8TAgQPTvoK1SZMmMXXq1OjZs2cOO6u+DjzwwLjzzjtzXrd27doxduzY2HnnnVPOef7552PmzJkVrvHggw/G4sWLU44ffvjhccstt1R4/YrKz8+Phx9+ONq2bZtyzvjx4+P111/PWU+5sscee8Rvf/vbnNYsLCxM+brkiH8Gjb788sscdpQMpd1/ffr0idtuuy13Df1/tWvXjsceeywmTZoU9957b4Vf1Z3k8yXJey+PHj16bPcvQJTH7bffHmvXrk05fsQRR8SNN96Ys36+UatWrXj88cfT/nufKZ9++mnKsU6dOmW9PgAAAIKjAAAAAGTYiBEj4tRTT41NmzalnNO8efOYPn16dO/ePYedVV+1a9eOv/zlL1GrVq1Kqd+8efNSQ6s33XRThdbevHlz2lfS1q9fP+67777Iz6+cH2U2atQo7r777rRzbrjhhhx1kzu333571KtXL+d1073CuqSkJJ555pkcdrPjK8v995e//KXS7r82bdpEv379Kvz5JJ8vSd57ed144405+3tYuXJl3HPPPSnHK/u6NGvWLO64446s1ykqKko51rJly6zXBwAAQHAUAAAAgAy65ppr4oILLoitW7emnLPzzjvHjBkzomvXrjnsrHq7+OKLY5999qnUHoYMGRJHH310yvGnn3465s+fX+51H3300fjwww9Tjl911VWxxx57lHvdTOrXr18MHjw45fjjjz8e77//fg47yq6OHTumvdbZdPDBB0ebNm1Sjs+aNSuH3ez4Srv/rrzyykq//7ZHks+XJO+9PL7//e9Hnz59slrj340cOTKKi4tTjl911VXRrl27nPWzLUOHDo2+fftmtUbt2rVTjqULlQIAAJA5gqMAAAAAbLetW7fGeeedF9dee23aebvttlvMnDmz0kOQ1UmDBg3ioosuquw2IuKfweB0HnvssXKv+b//+78px5o2bRoXXHBBudfMhiuuuCLl2NatWyu096rqpz/9aeTl5VVa/aOOOirl2DvvvJPDTnZ8pd1/6Z4AWx0k+XxJ8t7L48wzz8zq+v/tH//4R8qxqnRdfvOb32R1/YKCgpRjc+fOzWptAAAA/klwFAAAAIDtsnHjxjj55JPTvno1IqJt27Yxa9asaN++fY462zEMHz48mjZtWtltRETEfvvtF0ceeWTK8bFjx5ZrvaKiopg6dWrK8fPPPz8aNGhQrjWzpbCwMI444oiU42PGjMlhN9mTl5cXQ4YMqdQeevXqlXJMcDRzqtP9VxHVaX+ZPl+SvPfyyM/PT/vE00z75JNPYs6cOSnHq9J1Oeigg+KAAw7I2vrpnnb78ssv71BP8QYAAKiqBEcBAAAAqLDVq1fHwIEDS30i2F577RWzZs2q9NevVkfDhw+v7Bb+wznnnJNy7K233ooPPvigzGtNnDgxNm7cmHI810+CK80ZZ5yRcuzVV1+NpUuX5q6ZLOnWrVs0b968Unvo0qVLyrEvv/zSa4wzpLrdf+VV3faXyfMlyXsvj8LCwvjOd76TlbW3Zfz48VFSUpJyvDpdl+3VtWvXlGNbtmyJCy64ILZs2ZK1+gAAAAiOAgAAAFBBy5cvj8MOOyymTJmSdl7Hjh1j1qxZ0bp16xx1tuNo165ddO7cubLb+A99+/aNOnXqpBx//vnny7zWjBkzUo516dIl2rZtW47Osq9fv35Rs2bNlOPPPfdcDrvJjj59+lR2C6Ve95UrV+akjx1ddbv/yqu67S+T50uS914ePXv2zMq6qcyePTvlWFW8LgMHDsza2s2bN4/u3bunHH/mmWfi1FNPjXXr1mWtBwAAgKQTHAUAAACg3JYtWxYHH3xwvPLKK2nndenSJWbOnBm77LJLjjrbsWQztFFRO+20Uxx22GEpx996660yr/Xmm2+mHKuKe2/atGkceOCBKcfT7ae66NixY2W3EE2aNIm6deumHF+9enUOu9lxVbf7r7yq2/4yeb4kee/lse+++2Zl3VTmzZuXcqwqXpdWrVpFYWFh1tb/4Q9/mHb80UcfjcLCwlJ/QQkAAICKERwFAAAAoFwWLFgQvXr1infeeafUudOnT6/0115XZ926davsFrZp//33TzlW1uDo1q1b04Zo9ttvv3L3lQvpQjTlCc1WVR06dKjsFiLinwHlVARHt191vf/KqrruLxPnS5L3Xl7t27fPyrrbsmXLlliwYEHK8ep4XbbXWWedFe3atUs75913342jjjoqDjzwwHjwwQejqKgoa/0AAAAkTep3fwAAAADAf3nppZdiwIABsWLFijLNnzx5cpx00klZ7mrb5syZk5M6e++9dzRs2DAra1e119R/I11fb7/9dpnWWLJkSaxZs6ZCNSpTp06dUo7Nnz8/h51kx2677VbZLURERL169VKOrV+/Poed7Jiq6/1XVtV1f5k4X5K89/Ladddds7LutnzwwQexYcOGlOPV8bpsr9q1a8eoUaPisMMOi82bN6ed++KLL8aLL74YZ599dvTo0SOOPPLIOOyww2K//faLBg0aZK1HAACAHZngKAAAAABlMnny5Bg0aFDaQMp/Gz58eHTu3LlSAhHdu3fPSZ3p06dH7969s7J2VXn6439L9zrzsoaKv/zyy5RjNWrUiL322qvcfeXC3nvvnXJs+fLlOewkO9I96TOX8vNTvyyrpKQkh53smKrr/VdW1XV/mThfkrz38srlE9HT/dtYXa9LJhx88MFxzz33xNlnn12ms33z5s0xe/bsmD17dlxzzTWRn58fe++9d3Tr1i0KCwuja9eu0bVr1/jOd76T1b4BAAB2BIKjAAAAAJTqkUceiWHDhsWmTZvK9bk1a9bEoEGD4pVXXomCgoIsdbfjqqpP0WrSpEnKsbVr18bmzZujZs30P3pctWpVyrFGjRpFjRo1KtxfNjVu3Djl2Nq1a2PLli1VtveyqCrBUbKrut5/ZVVd95eJ8yXJey+vunXrZnS9dIqLi1OOVdfrkinDhw+P2rVrx9lnn532qazbsnXr1liwYEEsWLAgHnrooX99fdddd43vf//7ccABB8QBBxwQ3bt3j/r162e6dQAAgGot9a9tAwAAAEBEjBgxIk499dRyh0a/sXDhwhg2bJinBO5AGjVqlHY8XXCpLHMaNmxY7p5ypbTeyrL3qqxWrVqV3QI5UF3vv7KqrvvLxPmS5L2XV506dTK+Zio76nXJlGHDhsULL7wQhYWFGVnvk08+iXHjxsVll10WvXv3jqZNm8ZRRx0Vt99+e7z77rsZqQEAAFDdCY4CAAAAkNI111wTF1xwQWzdujXlnPr168fhhx+edp3x48fH9ddfn+n2qCSlPQl17dq1pa6Rbk5VfipYaXtfs2ZNjjqBiquu919ZVdf9ZeJ8SfLeyysvLy/ja6aybt26lGPV+bpk0n777Rdz5syJv/3tb9GtW7eMrr1hw4aYMmVK/PKXv4wOHTrEAQccEKNGjYr169dntA4AAEB1IjgKAAAAwLds3bo1zjvvvLj22mvTzisoKIhnnnkmpk6dGieeeGLauVdffXU888wzmWyTSlJagKcsQZN0c8oSPK0spe3dq96pDqrr/VdW1XV/mThfkrz3qixdOLQ6X5dMy8/Pj9NOOy3mzJkTzz33XJx22mnRrFmzjNd56aWX4owzzohWrVrFjTfeKEAKAAAkkuAoAAAAAN9y8sknxz333JN2TosWLWLGjBlx0EEHRUTE/fffH/vuu2/K+Vu3bo1TTjklFi9enMlWqQSlvTK4tFfZR/wzdFzR9StTut7y8vLKtHeobOnuv+Li4hx2kh1JPl+SvPeqLF3/1fW6ZNtBBx0Uf/vb3+KLL76IF154Ia666qo44ogjMhok/eqrr+LXv/51dOnSJebOnZuxdQEAAKqDmpXdAAAAAABVz2OPPZZ2vHXr1jFlypTYe++9//W1Bg0axNixY2P//fePlStXbvNzX331VQwaNCheeOGFqFu3biZb/paSkpKsrp8La9asyelrYsvq66+/TjlWv379qFGjRqlrpAvRFBcXx+bNm6Nmzar348tU39sR/7wH8vP9rj5VX2khti1btpTpPq6qkny+JHnvVdmOeF1yJT8/Pw488MA48MAD//W1ZcuWxWuvvfYff5YuXVrhGgsXLoxevXrFmDFjol+/fploGwAAoMqr3v9LGwAAAICca9++fTz//PP/ERr9xp577hn/+7//mzbg8dprr8VPfvKTbLa4w3jnnXcqu4VtWrBgQcqx5s2bl2mNnXfeOeXYli1bYuHCheXuKxfSXZOWLVvmsBOouNLuv0WLFuWwm8xL8vmS5L1XZS1atEg5Vl2vS2Vq3bp1HHPMMXH11VfHuHHjYsmSJbFixYqYOnVq/P73v49TTz012rRpU641169fH8cff3y8+OKLWeoaAACgahEcBQAAAKDMCgsL47nnnovdd9895Zx+/frFtddem3adUaNGxT333JPp9nY48+bNq+wWtildX/vss0+Z1thtt92icePGFapRmdL11blz5xx2AhVXXe+/sqqu+8vE+ZLkvVdlbdq0iZ122inleHW8LlVN06ZN4/DDD4+LLrooHnrooVi8eHF88MEH8cADD8Txxx9fpifdb9iwIYYMGRLFxcU56BgAAKByCY4CAAAAUCa9evWK6dOnp31q1jeuuOKKOO6449LO+fnPf+6pTqV49dVXK7uFbZozZ07KsX333bfM63Tp0qVCNSrT3LlzU46l2w9UNem+X6vq2VMeST5fkrz3qiovLy86deqUcrw6XpfqoF27dvGjH/0oxowZEx999FFcc8010aBBg7Sf+fjjj+Omm27KUYcAAACVR3AUAAAAgFL17ds3Jk+eHAUFBWWan5eXF3/961+jQ4cOKeds3LgxhgwZEl988UWm2tzhTJw4sbJb+JY1a9bE9OnTU46X58lwXbt2TTk2YcKEcvWVC19++WW89NJLKcd3hHATyZHu/nvyySdz2El2JPl8SfLeq7J0v1hRFa/L0qVL46233qrsNjKmWbNmcfXVV8fbb78dPXr0SDv33nvvjU2bNuWoMwAAgMohOAoAAABAWieccEKMHz8+6tevX67PNWzYMMaOHRuNGjVKOefjjz+OoUOHxubNm7e3zR3SBx98EG+//XZlt/EfnnnmmVi/fn3K8YMPPrjMax155JEpxxYsWBDvv/9+uXrLtkmTJsXWrVu3OVajRo3o06dPjjuCikt3/7355puxZMmSHHaTeUk+X5K896rs0EMPTTlWFa9LVQyzZsLuu+8eM2bMiF69eqWcs2LFirS/JAMAALAjEBwFAAAAIKXhw4fHI488ErVr167Q5zt06BB//etfIy8vL+WcmTNnxqWXXlrRFnd4999/f2W38B9GjhyZcqxr167Rtm3bMq911FFHxU477VShWpUh3bU45JBDolmzZjnsBrZPafffAw88kMNuMi/J50uS916VDRgwIGrWrJlyvDpdl+qubt268de//jXtf9/OmTMnhx0BAADknuAoAAAAANt08cUXx8iRIyM/f/t+hHTsscfGFVdckXbObbfdFo899th21dlRjRw5MoqKiiq7jYiIeOutt+Lpp59OOX7ccceVa706derEgAEDUo7fe++9sWrVqnKtmS0vv/xyPPfccynHjz/++Bx2A9uvtPvvrrvuijVr1uSwo8xK8vmS5L1XZU2aNIlDDjkk5XhVui7PPvtsvPbaa5XdRlbtsccecdRRR6Ucf+edd3LYDQAAQO4JjgIAAADwLdddd13ceuutGVvv2muvjf79+6edc+aZZ8b8+fMzVnNHUVxcHHfccUdltxEREb/97W/Tjg8dOrTcaw4bNizlWFFRUfzxj38s95rZcN1116Ucq1OnTpxwwgk57AYyI93999VXX8Wdd96Zw24yL8nnS5L3XpWddNJJKceq0nW5/vrrK7uFnOjZs2fKsZUrV+auEQAAgEogOAoAAADAt5T2hNDyys/Pj7///e+x5557ppyzZs2aGDRoUJV52lZVcuONN8aiRYsqtYennnoqRo8enXJ8wIABsc8++5R73f79+8f3vve9lOM33nhjLFy4sNzrZtK4ceNiwoQJKcfPOOOM2HnnnXPYEZmW7nXFq1evzmEnuVXa/fe73/2u0s+e7ZHk8yXJe6/KTj/99LR7qgrX5aGHHorp06dXag+50qxZs5Rj69aty2EnAAAAuSc4CgAAAEBONG7cOMaOHRsNGjRIOee9996LYcOGRUlJSQ47q/rWr18f55xzTmzdurVS6hcVFcW5556bds7ll19e4fV//etfpxxbv359DB8+PLZs2VLh9bfHypUr48ILL0w5XqNGjbj00ktz2BHZ0LRp05RjX331VQ47yb1099/atWtj+PDhlXb2fPHFF/HKK69s1xpJPl+SvPeqqm7duvGzn/0s5fj69evj7LPPrtR77pe//GWl1K4My5cvTznWuHHj3DUCAABQCQRHAQAAAMiZfffdNx544IG0c5544om48cYbc9RR9fHss89uVzizorZs2RInnnhiLFmyJOWc3r17R69evSpcY/DgwWmfjDdr1qy46KKLKrx+RX2z92XLlqWcc8YZZ0S7du1y2BXZkC44On/+/Bx2knul3X8zZsyIX/ziF7lr6P/bsmVLnHzyydGzZ8+45pprYvPmzRVaJ8nnS5L3XpWde+65aZ90OWPGjLj44otz2NE/bdy4MQYNGhRffvllzmtXlvfffz/lWIsWLXLYCQAAQO4JjgIAAACQU0OHDo1LLrkk7Zwrr7wypkyZkqOOqo9bbrklRo4cmbN6JSUl8dOf/jSeeeaZlHPq1KkT99xzz3bVyc/PjwceeCBq1qyZcs4dd9wRd95553bVKY9v9j558uSUc3beeee49dZbc9YT2ZMuIPT888/nsJPcK8v996c//Sluv/32HHYV8Ytf/CKmTZsWmzdvjmuvvTYOPPDAePfdd8u9TpLPlyTvvSorKCgo9X66/fbb4+67785RRxFbt26NH//4xzF79uyc1Xzuuefitddey1m9/7Zhw4YYN25cyvFu3brlrhkAAIBKIDgKAAAAQM7deOONcfjhh6cc37p1a5x88slpn3KZVOecc0784Q9/yHqdzZs3x49+9KNSQ6FXXnlldOjQYbvrFRYWxmWXXZZ2zoUXXhjXX3/9dtcqTVn3fvfdd0eTJk2y3g/Z171795Rj//d//1ehwGJ1UlhYWOoTjX/5y1/GFVdckZN+fvnLX34rzDhnzpwoLCyMO++8M0pKSsq1XpLPlyTvvSo7/fTTo2/fvmnnnH/++XHTTTdlvZcNGzbECSecEA899NC3xnbaaae0/71WUZs3b46zzz47evToEddcc02sW7cu4zVKc9ddd8XXX3+dcvzggw/OYTcAAAC5JzgKAAAAQM7VqFEjHn300WjTpk3KOStWrIjBgwfH+vXrc9hZ1XLyySd/62slJSVx8cUXxznnnBNr1qzJSt1PPvkk+vXrF6NGjUo7r0ePHnHppZdmrO6VV14ZBxxwQNo5v/nNb+JHP/pRFBcXZ6zuv/vkk0+if//+pe79xz/+cRx//PFZ6YHcO/TQQ1OOlZSUxO9+97scdlM5fvOb30TPnj3Tzrnhhhti6NChsWLFiqz0sGrVqjjhhBNSPo1x3bp18Zvf/CY++eSTcq+d5PMlyXuvyu677774zne+k3bO5ZdfHsOHD4/Vq1dnpYdFixZFnz59YsyYMdscv/baa2O33XbLeN177rkn3nnnndi0aVNce+210b59+3jggQdi06ZNGa+1La+++mraIPyBBx4Y3/3ud3PSCwAAQGURHAUAAACgUjRr1izGjh0b9erVSznn1VdfjfPOOy+HXVUtI0aMSBlcuO+++6JLly7x7LPPZqxeSUlJ/O1vf4vOnTvH1KlT085t27ZtPPHEE2lfgVxetWvXjvHjx8eee+6Zdt7//M//RJcuXWLKlCkZq/3vey9t3cMPP7zUJ+ZRvXTq1ClatmyZcvzvf/97/PWvf63Q2kVFRfHZZ59VtLWc+eb+a9++fdp5//jHP6JTp07xj3/8o9xP/kznqaeeim7dusXo0aPTzhsxYkS0atWq3Osn+XxJ8t6rst133z3Gjh0bderUSTvv/vvvj65du2b03/vNmzfHXXfdFV27do3/+7//2+acfv36xc9//vOM1fzG119/Hddcc81/fO2jjz6KH//4x7HnnnvGbbfdFl999VXG635j6tSpcdhhh8WGDRtSzrnwwguzVh8AAKCqEBwFAAAAoNIUFhbGvffem3bOgw8+WOqcHVWTJk1iwoQJ0bhx422Of/DBB3HEEUdE796949lnn61wiGvz5s3x2GOPxb777hvDhg1L++rWiIiCgoKYOHFi2qBdRTVv3jyeeuqpUp/Ctnjx4jjqqKPi0EMPzeneO3fuHI8//njUqlWrQvWomvLy8koNqZ955pnxpz/9qVzrTp8+Pbp27fqtkFRV1axZs3jqqaeiRYsWaed9/vnnMXTo0CgsLIzRo0fH5s2bK1SvpKQknn766TjyyCOjf//+8f7776ed/8Mf/jBOO+20CtWKSPb5kuS9V2UHHXRQPPDAA5GXl5d23jf/3vfp0yemT59e4Xrr16+P+++/P9q3bx8//elPUz65vH379vHwww9Hfn7m/2/E3/72tymDocuWLYuLLroodt111zjxxBNj3LhxsXbt2ozU/eijj+L888+Pvn37xqpVq1LO6927d5x44okZqQkAAFCV5ZVk8leCAQAAAKgWFi9eHO3atUs5nusfGV144YVx5513phyvXbt2zJo1K3r06JHDrrKvrNdh2rRpcfTRR5canthtt91iyJAh0bdv3ygsLEwb7Fy6dGm89tpr8eSTT8bYsWPL/Orpli1bxsSJE6Nbt25lml9R77zzTvTr1y8WL15cpvmtW7eOE044Ifr27Rv77bdf2nDU4sWL/7X3cePGlfnJZj179ozx48dHs2bNyjS/rHJ5P1a1ez+Vtm3bxpIlS7Y5Nn369Ojdu3fGa3799dfRpk2bUl/Vvf/++8cll1wSRx111DZD3Z999llMmzYt7rnnnnj++ecj4p9n2AcffFChJ2VWhoULF5YpyPmNZs2axbHHHhsDBw6MwsLCaNu2bcq5K1asiDlz5sTTTz8dTzzxRHz44YdlqnHMMcfE448/npGnHCfpfPlvSdp7dTnvIiJGjRoVZ511Vplf1b777rv/67oUFhamvC4lJSWxaNGiePXVV+OJJ56ICRMmlPra+1122SVmzJjxr6cPn3HGGTFq1KhvzWvTpk2Zv4++sXDhwujUqVO5Xklft27d6NOnTxx44IHx/e9/P7p06RI777xzqWHbtWvXxvz58+OFF16Ip556KqZOnRpbtmxJ+5mmTZvGSy+9FHvttVeZ+wMAAKiuBEcBAAAAEqiqhSk2b94chx9+eMyaNSvlnN122y3mzp0bzZs3z2Fn2VWe6/Diiy/GgAEDyvX61pYtW8bOO+8cBQUFUb9+/Vi9enUUFRXFxx9/XKHXwLZv3z6efvrptD1n0meffRYDBgyIuXPnlvuzu+66a7Ro0SIKCgqiXr16UVxcHEVFRfHRRx/FypUry73ekCFD4m9/+1vUrVu33J8tjeDot1VGcDQi4pZbbolLL720THPz8/Pju9/9bjRv3jwaNGgQX3/9dXz++ecp+77wwgvjjjvuyGS7WbV8+fI49thj44UXXij3Zxs3bhy77bZbFBQURMOGDWPDhg2xatWqWLZsWXzxxRflXq9///4xZsyYUl/pXR5JOV+2JSl7ry7n3TeeeeaZGDJkSKnBzm355ro0btw46tSp86/rsnTp0lLD8P9uzz33jClTpvzH31smg6NFRUXx4x//OB5//PFyfe6/1a1bN3bfffdo0qRJ1K9fP+rVqxdbtmyJ9evXx9dffx2fffZZuc+aunXrxtSpU6NXr17b1RsAAEB1ITgKAAAAkEBVMUzx+eefR7du3eLjjz9OOadPnz4xZcqUqFGjRg47y57yXocFCxbEcccdF++99162W/uWoUOHxj333BNNmzbNad21a9fGr371q7j77rsr5fuyXr16cd1118UvfvGLUp9uVlGCo99WWcHRkpKSGDx4cIwdOzbja9erVy8+/PDDtE8Crmo2bNgQV111VfzhD38o9Ul92ZCXlxeXX355/O53v8vKK7OTcL6kkoS9V5fz7t/NmzcvTj/99Hj99ddzXvuAAw6IsWPHxs477/wfX89kcPQbDzzwQFx22WXx5ZdfVujzmdaiRYsYM2aM0CgAAJAomf9JCwAAAABUQMuWLePxxx9P+0S56dOnx2WXXZbDrqqWjh07xuuvvx4XXnhhzkJGLVq0iNGjR8ejjz6a89BoRET9+vXjrrvuiunTp8eee+6Z09oHH3xwvPnmm/HLX/4y56EuKkdeXl6MGjUqDjjggIyvvW7duvjLX/6S8XWzqU6dOnHzzTfHiy++GPvuu29Oa7dt2zaefPLJuP7667MSGo1I9vmS5L1XZZ07d46XX345rrnmmqhVq1ZOataqVSuuu+66eP75578VGs2WM888MxYuXBiXXnppNGzYMCc1UzniiCPilVdeERoFAAASR3AUAAAAgCqjR48ecdddd6Wd8/vf/z5Gjx6do46qnnr16sUdd9wRs2bNioMPPjhrdRo3bhxXXXVVvPPOOzF48OCs1SmrQw89NObNmxd33nln7Lbbblmttd9++8Xjjz8eM2fOjL322iurtah6GjZsGDNnzoyzzjoro+uedNJJ8fOf/zyja+bK/vvvH3Pnzo1Ro0ZFhw4dslqrYcOGcd1118WCBQuif//+Wa31jSSfL0nee1VVq1atuPrqq2PevHkxbNiwqFmzZtZq9e/fP+bMmRNXXHFFzp/mXlBQEDfddFMsW7YsbrnllpwHmPfaa694+OGHY8qUKbH77rvntDYAAEBVIDgKAAAAQJUyfPjwOPvss9PO+dGPfhQLFizIUUdV00EHHRSzZs2KadOmRd++fTMW+OjQoUPccMMNsWTJkrj22mujSZMmGVk3E+rWrRsXXHBBLFq0KO6777444IADMva0ujp16sTAgQNj4sSJ8eqrr8agQYM8CS/BateuHffdd1+MGTMm9ttvv+1aq3PnzjF+/Ph4+OGHo0GDBhnqMPdq1qwZw4YNi/nz58c//vGPOOqoozL6RMROnTrFXXfdFR9//HFcccUVUbdu3YytXRZJPl+SvPeqrH379jFq1Kh499134/zzz48WLVpkZN3atWvHoEGDYs6cOTFx4sTo0qVLRtatqIKCgrjkkkvi/fffj9mzZ8f5558fbdq0yUqtmjVrxpFHHhljxoyJd999N0466aSs1AEAAKgO8kpKSkoquwkAAAAAcmvx4sXRrl27lOOV/SOjjRs3xqGHHhovvvhiyjl77713vPzyy9GoUaMcdpZZmbwOy5cvj7Fjx8YTTzwRc+bMic8//7xMn2vSpEl07do1+vTpE4MHD45OnTqVuWZV8NFHH8WYMWNiypQp8dprr8XHH39cps/VqFEj2rdvH/vtt18MGDAgBg4cWGmvy83l/VjV7/1vtG3bNpYsWbLNsenTp0fv3r1z2s/kyZPj73//e8ycOTNlX9/Iy8uL9u3bxyGHHBInnnhiHHbYYTtsUG7lypUxYcKEmDRpUrz22muxcOHC2Lp1a5k+27hx49h///2jb9++8YMf/CA6d+6c5W7Lb0c4XypqR9h7dTnvymrr1q0xa9asGDt2bLzwwgsxf/78WLduXZk+27p16/j+978fxx13XBxzzDHl+m+nM844I0aNGvWtr7dp0yYWL15c5nXK6913342pU6fGK6+8Eq+99losWLAgNm3aVK41atasGZ06dYr9998/DjnkkDj66KOr1C/EAAAAVCbBUQAAAACoJNkMtXz66afx1ltvxRdffBFFRUWxatWqyMvLi4KCgigoKIimTZvGPvvss8O9nnXFihXx5ptvxpdffhlFRUVRVFQU69evj4YNG0ZBQUE0atQoWrduHZ07d4569epVdrtUQ0uXLo358+fHihUr4quvvori4uJo2LBhNGvWLFq0aBHdunWLpk2bVnablWLdunUxb968WLZsWaxatSqKiopi9erVUatWrWjYsGE0bNgwWrVqFR06dIhddtmlststtySfL0nee1W1ZcuWePfdd+P999+PlStXxqpVq2LVqlVRs2bNaNSoURQUFMQuu+wSXbp02SHOpE2bNsVHH30Uy5Yti48//jhWrlwZ69ati3Xr1kVJSUnUq1cv6tatG40aNYpWrVrF7rvvHq1bt446depUdusAAABVkuAoAAAAAFSSHe1paAAAAAAAVH35ld0AAAAAAAAAAAAAALkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAkhOAoAAAAAAAAAAACQEIKjAAAAAAAAAAAAAAmRV1JSUlLZTQA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+hXzSu5BPepeicnAUAKAG5s6dG11dXZuNDRs2LKZOnVqnirbstddei8mTJ8f8+fMzr5922mnxve99r8ZV1db222+fOd79MRX1smbNmszxnupm2+jd/NG7ROjdPNK76FsAqB6vs/ljfYy+zR99S4TezSO9S4Tepbha610AAEARZD3eYNq0aTFgwIA6VLNly5Yti8mTJ8czzzyTef2oo46KG264IZqb0/4MUk+b7p426bXmzYLa0Lv5o3eJ0Lt5pHfRtwBQPV5n88f6GH2bP/qWCL2bR3qXCL1LcTk4CgBQZQsXLownnniibLwRH2+wdOnSmDRpUjz77LOZ16dMmRK33XZbtLW11biy2hs0aFDm+OrVq2tcSTZvFlSf3s0nvYvezSe9W2z6FgCqx+tsPlkfF5u+zSd9i97NJ72L3qXIHC8GAKiy2bNnl42NGjUqJk2aVIdqerZ48eKYOHHiFjcbd9xxRwwcOLDGldXHsGHDMseXLVtW40qy9VTH0KFDa1xJuvRuPuld9G4+6d1i07cAUD1eZ/PJ+rjY9G0+6Vv0bj7pXfQuRebgKABAlWVtOM4888yGekTASy+9FBMnToznnnsu8/oxxxwTd955Z6E2G3vssUfm+Msvv1zbQnrQUx177rlnjStJl97NJ72L3s0nvVts+hYAqsfrbD5ZHxebvs0nfYvezSe9i96lyBrnbzkAQIKeeOKJzE9+NdLjDV544YWYOHFi/P73v8+8fvzxx8dtt90W7e3tNa6svsaMGZM5vmjRohpXkq2nNwv22muvGleSJr2bX3q32PRufund4tK3AFA9Xmfzy/q4uPRtfunbYtO7+aV3i03vUnQOjgIAVFHWp9T23XffOPDAA+tQTbnnn38+Jk6cGC+99FLm9ZNOOiluvvnmGDBgQI0rq78xY8ZEU1NT2fjbb78dr776ah0q2lxPnyrce++9a1xJmvRufundYtO7+aV3i0vfAkD1eJ3NL+vj4tK3+aVvi03v5pfeLTa9S9E5OAoAUCVdXV1x4403lo1/+tOfrkM15Z599tmYOHFij5+aPO200+KnP/1ptLW11biyxtDe3h6jR4/OvPb444/XuJre17DPPvvUuJL06N1807vFpXfzTe8Wk74FgOrxOptv1sfFpG/zTd8Wl97NN71bXHoXHBwFAKia+++/P5YuXVo23tnZWYdqNrdgwYI44ogjYsmSJZnXOzo6Ys6cOdHa2lrjyhrLIYcckjn+6KOP1riSzT333HOxfPnysvG2traYMGFCHSpKi97NP71bTHo3//Ru8ehbAKger7P5Z31cPPo2//RtMend/NO7xaR3wcFRAICqmTVrVtnYgQceWPdPAc6fPz+OOOKIeOWVVzKvT58+PWbOnBktLS01rqzxTJw4MXP84YcfrnElm/vlL3+ZOX7QQQfFwIEDa1xNevRu/undYtK7+ad3i0ffAkD1eJ3NP+vj4tG3+advi0nv5p/eLSa9Cw6OAgBUxbp162LevHll4x0dHXWo5g+eeuqpmDRpUrz66quZ188555y47rrrornZMjGi5zcLHnzwwfjv//7vGlfzB1l/tyIijjjiiNoWkiC9mwa9Wzx6Nw16t1j0LQBUj9fZNFgfF4u+TYO+LR69mwa9Wzx6F97jbxIAQBX87Gc/i7feemuzsebm5jjzzDPrU1BE/OY3v4nJkyfH66+/nnn9i1/8YvzLv/yLzcYm9ttvvxg9enTZ+IYNG3rcsFfbypUr4+677868dswxx9S4mvTo3TTo3eLRu2nQu8WibwGgerzOpsH6uFj0bRr0bfHo3TTo3eLRu/Aef5sAAKpg9uzZZWOTJk2KUaNG1aGaiCeeeCKmTJnS4ycjzz333Pinf/qnaGpqqnFlje+ss87KHJ85c2aNK3nPTTfdFOvWrSsb32uvveLQQw+tQ0Vp0bvp0LvFonfToXeLQ98CQPV4nU2H9XFx6Nt06Nti0bvp0LvFonfhPQ6OAgBU2MqVK+POO+8sG6/X4w0effTROPLII+PNN9/MvH7eeefFVVddVeOq8uOzn/1s5vhDDz0UjzzySE1rKZVK8d3vfjfz2p/92Z/ZMPaT3k2L3i0OvZsWvVsM+hYAqsfrbFqsj4tB36ZF3xaH3k2L3i0OvQt/4OAoAECFzZs3L9auXbvZ2IABA2LatGk1r+Xhhx+Oo48+OlasWJF5/aKLLupx88l79t133x4/vXnFFVfUtJZbb701FixYUDbe0tLS45sa9J7eTYveLQ69mxa9Wwz6FgCqx+tsWqyPi0HfpkXfFofeTYveLQ69C3/g4CgAQIVlPd7guOOOi2HDhtW0joceeiiOOeaYWLlyZeb1GTNmxGWXXVbTmvLqwgsvzBy//fbb45577qlJDWvXro0LLrgg89r06dNj1113rUkdKdO76dG7xaB306N306dvAaB6vM6mx/o4ffo2Pfq2GPRuevRuMehd+AMHRwEAKujVV1+Ne++9t2y8s7OzpnXcf//98clPfjLeeeedzOuXXXZZXHrppTWtKc+OP/74Hj9p+oUvfCHWrFlT9Rq+/e1vx8KFC8vG29vbZVkBejdNejd9ejdNejdt+hYAqsfrbJqsj9Omb9Okb9Ond9Okd9Ond2FzDo4CAFTQ3LlzY+PGjZuNDR06NKZOnVqzGu6555447rjjYtWqVZnXv/vd78ZFF11Us3pS0dOjSH7/+9/HF77whare+9577+3xURRf/vKXY7fddqvq/YtA76ZL76ZN76ZL76ZL3wJA9XidTZf1cbr0bbr0bdr0brr0btr0LmzOwVEAgArKerzBKaecEu3t7TW5/9133x0nnHBCj596/Id/+Ic477zzalJLag477LD43Oc+l3lt5syZ8a1vfasq9/3d734Xp556arz77rtl18aMGROXXHJJVe5bNHo3XXo3bXo3XXo3XfoWAKrH62y6rI/TpW/TpW/TpnfTpXfTpndhcw6OAgBUyPPPPx+PPfZY2XhHR0dN7v9v//ZvceKJJ8batWvLrjU1NcX3v//9+OpXv1qTWlJ11VVXxV577ZV57dJLL42LL744SqVSxe735JNPxqRJk+LNN98su9ba2hqzZs2KIUOGVOx+RaV306d306R306d306NvAaB6vM6mz/o4Pfo2ffo2TXo3fXo3TXoXyjk4CgBQIVmfUhs5cmRMnjy56ve+/fbb45RTTol169aVXWtqaoprr702vvSlL1W9jtQNHjw4Zs2aFW1tbZnXL7/88jj11FPjjTfe6Pe95syZE4cffni88sormde/9a1vxcc//vF+3we9WwR6N016N316Nz36FgCqx+ts+qyP06Nv06dv06R306d306R3oZyDowAAFZK14TjjjDOipaWlqvedN29enHrqqbF+/fqya83NzXH99dfH5z//+arWUCQHH3xwzJ07t8dc582bF2PHjo1rr70285EiW/PMM8/EscceGx0dHbFq1arM3/PFL34xLrrooj5/b7Lp3WLQu+nRu8Wgd9OibwGgerzOFoP1cVr0bTHo2/To3WLQu+nRu1Cutd4FAACk4Mknn4wFCxaUjVf78QY/+clPorOzMzZs2JB5/ZJLLolDDz00nnvuuarW0ZM99tgjWlvTW3KefPLJcd1118VnP/vZzMeRLF++PP7iL/4iLrnkkvjc5z4XJ510Uuy///6x3XbbZX6/xYsXxwMPPBDXXXdd3HfffVt8xMlZZ50V3//+9yv2sxSd3s2md/Vuo9O72fSu3m1k+jZbqn0LQG15nc2W6uus9XEa9G02fatvG53ezaZ39W6j07vZUu1dek/6AAAVkPUptb333jsOOuigqt1z7ty58ZnPfCY2btzY4++59NJL49JLL61aDVvzwgsvxB577FG3+1fT2WefHUOGDInp06fH22+/nfl7XnvttfjOd74T3/nOd2LgwIExfvz42HHHHWP48OGxevXqWL58ebz00kuxePHiXt3z/PPPj8svvzyampoq+aMUmt7Npnf1bqPTu9n0rt5tZPo2W8p9C0DteJ3NlvLrrPVx/unbbPpW3zY6vZtN7+rdRqd3s6Xcu/SOg6MAAP3U1dUVc+fOLRuv9qfUrrnmmi1uNqi+k08+Ofbbb784+eST45lnntni712zZk08+eST23SfIUOGxA9/+MM45ZRTtunryaZ3i0vv5pveLS69m1/6FgCqx+tscVkf55e+LS59m296t7j0br7pXehZc70LAADIuwceeCCWLFlSNl7tDQeNYezYsfHkk0/G3/7t38bgwYMr+r2bmpriM5/5TDz99NPeKKgCvVtseje/9G6x6d180rcAUD1eZ4vN+jif9G2x6dv80rvFpnfzS+9CzxwcBQDop6zHG+y///4xduzYOlRDPbS3t8fFF18cCxcujK985SsxfPjwfn2/7bbbLk466aR49NFHY+bMmTF69OgKVcqm9C56N5/0Lno3f/QtAFSP11msj/NH36Jv80nvonfzSe9CzxwcBYA623777Xv8td1229W7PLZi/fr1cfPNN5eN+5RaMY0cOTKuvvrqeOWVV+LGG2+M0047rdcb/WHDhsWUKVPi7//+72PJkiVxyy23xIQJE6pccXHpXTald/ND77IpvZsP+hagcXlPKv+8zrIp6+N80LdsSt/mh95lU3o3P/QubFlTqVQq1bsIAIC8uu222+Kkk07abKy5uTkWLVoUH/zgB+tTFA3nlVdeid/+9rfxxhtvxJtvvhkrV66MQYMGxfDhw2P48OGx7777xrhx46KpqanepRaG3qU39G7j0bv0ht5tLPoWAKrH6yy9YX3cWPQtvaFvG4/epTf0buPRu7BlrfUuAAAgz2bNmlU2NnHiRJsNNjNq1KgYNWpUvctgE3qX3tC7jUfv0ht6t7HoWwCoHq+z9Ib1cWPRt/SGvm08epfe0LuNR+/ClnlUPQDANnr77bfjzjvvLBv3eANobHoX8knvQv7oWwCoHq+zkD/6FvJJ70I+6V3YOgdHAQC20S233BJ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}, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "bes, fig = defect_entry.get_eigenvalue_analysis()" - ] + "execution_count": 3 }, { "cell_type": "markdown", @@ -924,6 +924,54 @@ "print(bes)" ] }, + { + "cell_type": "markdown", + "source": [ + "Important terms included in ``print(bes)``:\n", + "1. ``P-ratio``: The ratio of the summed projected orbital contributions of the defect & neighbouring sites to the total sum of orbital contributions from all atoms to that electronic state. A value close to 1 indicates a localised state.\n", + "2. ``Occupation``: Occupation of the electronic state / orbital.\n", + "3. ``vbm has acceptor phs``/``cbm has donor phs``: Whether a PHS has been automatically identified. Depends on how VBM-like/CBM-like the defect states are and the occupancy of the state. ``(X vs. 0.2)`` refers to the hole/electron occupancy at the band edge vs the default threshold of 0.2 for flagging as a PHS (but you should use your own judgement of course).\n", + "4. ``Localized Orbital(s)``: Information about localised defect states, if present.\n", + "\n", + "Additionally, ``Index`` refers to the band/eigenvalue index in the DFT calculation, ``Energy`` is its\n", + "eigenvalue energy at the given ``K-point coords``, ``Orbitals`` lists the projected orbital contributions\n", + "to that state, and ``OrbDiff`` is the normalised difference in projected orbital contributions to the\n", + "VBM/CBM states between the bulk and defect supercells." + ], + "metadata": { + "collapsed": false + }, + "id": "f94fd9a12ebb6a68" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-09T21:53:29.799742Z", + "start_time": "2024-04-09T21:53:29.793037Z" + } + }, + "cell_type": "code", + "source": "print(f\"{0.0001:7.2e}\")", + "id": "ef0e2298ead2a106", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.00e-04\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "301c9bd96fc5a4ca" + }, { "cell_type": "markdown", "id": "32c2a4218d159e9c", @@ -2272,25 +2320,6 @@ "print(point_symmetry(relaxed_defect_supercell, bulk_structure=bulk_supercell, relaxed=False))" ] }, - { - "cell_type": "markdown", - "id": "558ae8f74f4abeea", - "metadata": {}, - "source": [ - "In the example, the neutral copper vacancy in Cu₂SiSe₃ was\n", - "determined to be a PHS. This was additionally confirmed by performing calculations in larger\n", - "supercells and plotting the charge density. Important terms included in ``print(bes)``:\n", - "1) ``P-ratio``: The ratio of the summed projected orbital contributions of the defect & neighbouring sites\n", - "to the total sum of orbital contributions from all atoms to that electronic state. A value close to 1\n", - "indicates a localised state.\n", - "2) ``Occupation``: Occupation of the electronic state / orbital.\n", - "3) ``vbm has acceptor phs``/``cbm has donor phs``: Whether a PHS has been automatically identified.\n", - "Depends on how VBM-like/CBM-like the defect states are and the occupancy of the state. ``(X vs. 0.2)``\n", - "refers to the hole/electron occupancy at the band edge vs the default threshold of 0.2 for flagging as a\n", - "PHS (but you should use your own judgement of course).\n", - "4) ``Localized Orbital(s)``: Information about the localised defects states." - ] - }, { "cell_type": "markdown", "id": "1cefbd2371107120", diff --git a/examples/chemical_potentials_tutorial.ipynb b/examples/chemical_potentials_tutorial.ipynb index 1d4c89f4..1415410b 100644 --- a/examples/chemical_potentials_tutorial.ipynb +++ b/examples/chemical_potentials_tutorial.ipynb @@ -36,19 +36,19 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "0419fb19-2c7a-40e8-a563-6df9de31451e", "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-02T15:39:37.084407Z", - "start_time": "2024-03-02T15:39:30.351429Z" + "end_time": "2024-04-09T23:03:37.643784Z", + "start_time": "2024-04-09T23:03:33.473921Z" } }, - "outputs": [], "source": [ "from doped.chemical_potentials import CompetingPhases" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", @@ -154,6 +154,7 @@ "source": [ "import doped\n", "import matplotlib.pyplot as plt\n", + "\n", "plt.rcdefaults()\n", "plt.style.use(f\"{doped.__path__[0]}/utils/doped.mplstyle\") # use doped style\n", "from pymatgen.ext.matproj import MPRester\n", @@ -334,7 +335,7 @@ "\n", "k-points convergence testing is done at GGA (PBEsol by default) and is set up to account for magnetic moment convergence as well. Here we interface with [vaspup2.0](https://github.com/kavanase/vaspup2.0) to make it easy to use on the HPCs (with the `generate-converge` command to run the calculations and `data-converge` to quickly parse and analyse the results).\n", "\n", - "You may want to change the default `ENCUT` (350 eV) or k-point densities that the convergence tests span (5 - 60 kpoints/Å3 for semiconductors & insulators and 40 - 120 kpoints/Å3 for metals in steps of 5 kpoints/Å3). Note that `ISMEAR = -5` is used for metals by default (better kpoint convergence for energies but should not be used during metal geometry relaxations) and k-point convergence testing is not required for molecules (Γ-point sampling is sufficient).\n", + "You may want to change the default `ENCUT` (350 eV) or k-point densities that the convergence tests span (5 - 120 kpoints/Å3 for semiconductors & insulators and 40 - 1000 kpoints/Å3 for metals). Note that `ISMEAR` is set to `0` (gaussian smearing) for semiconductors & insulators and `2` (2nd order Methfessel-Paxton smearing) for metals by default, following the recommended choices in `VASP`, and k-point convergence testing is not required for molecules (Γ-point sampling is sufficient).\n", "\n", "Note that `doped` generates \"molecule in a box\" structures for the gaseous elemental phases\n", "H2, O2, N2, F2 and Cl2. The molecule is placed in\n", @@ -359,7 +360,9 @@ } ], "source": [ - "cp.convergence_setup(user_incar_settings={'GGA': \"PE\"}) # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" + "cp.convergence_setup(\n", + " user_incar_settings={\"GGA\": \"PE\"}\n", + ") # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" ], "metadata": { "collapsed": false, @@ -383,6 +386,16 @@ }, "id": "1f5f72e255e8bca6" }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "```{tip}\n", + "_Usually_ we expect mostly equivalent energy convergence with respect to _k_-points / basis set for semi-local (GGA) and hybrid DFT. _**However,**_ this may not be the case when there is a major qualitative change in behaviour between semi-local/hybrid DFT, such as going from metallic at the GGA level to semiconducting with hybrid DFT – which can occur for relatively low band gap systems. In these cases, it can be worth performing the convergence tests with hybrid DFT to see if convergence is reached at lower _k_-point densities / basis set sizes.\n", + "```" + ], + "id": "d7c6d3cbe30fd891" + }, { "cell_type": "code", "execution_count": 11, @@ -441,8 +454,8 @@ "source": [ "Next, you want to relax each competing phase with the converged k-point mesh, and calculate the energy with the same DFT functional and settings as your defect supercell calculations. `doped` can generate these folders for the relaxations of the competing phases.\n", "\n", - "The _k_-point meshes are Γ-centred (as opposed to Monkhorst-Pack) by default. By default `doped` will\n", - "make the inputs assuming a HSE06 `INCAR` (see [`HSESet.yaml`](https://github.com/SMTG-Bham/doped/tree/main/doped/VASP_sets/HSESet.yaml) for default values) and kpoint densities of 95 kpoints/Å3 for metals and 45 kpoints/Å3 for semiconductors. Assuming you've followed the k-point convergence testing workflow above, you should change the `KPOINTS` file to match the converged mesh in each case, however the default densities are good starting points. `doped` will automatically set `SIGMA` and `ISMEAR` accordingly depending on whether the phase is a semiconductor or metal, and will set `NUPDOWN` appropriately for molecules (i.e. O2 has triplet spin).\n", + "The _k_-point meshes are Γ-centred Monkhorst-Pack by default. By default `doped` will\n", + "make the calculation inputs assuming a HSE06 `INCAR` (see [`HSESet.yaml`](https://github.com/SMTG-Bham/doped/tree/main/doped/VASP_sets/HSESet.yaml) for default values) and kpoint densities of 200 kpoints/Å3 for metals and 64 kpoints/Å3 for semiconductors/insulators. Assuming you've followed the k-point convergence testing workflow above, you should change the `KPOINTS` file to match the converged mesh in each case, however the default densities are good starting points. `doped` will automatically set `SIGMA` and `ISMEAR` accordingly depending on whether the phase is a semiconductor or metal, and will set `NUPDOWN` appropriately for molecules (i.e. O2 has triplet spin).\n", "\n", "These relaxations can be set up with:" ] @@ -451,7 +464,9 @@ "cell_type": "code", "outputs": [], "source": [ - "cp.vasp_std_setup(user_incar_settings={'ENCUT':600}) # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" + "cp.vasp_std_setup(\n", + " user_incar_settings={\"ENCUT\": 600}\n", + ") # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" ], "metadata": { "collapsed": false, @@ -478,11 +493,12 @@ "outputs": [], "source": [ "from pymatgen.io.vasp.inputs import Kpoints\n", + "\n", "converged_kpoints_dict = {\n", - " \"Zr3O_EaH_0\": [2,2,2],\n", - " \"Zr_EaH_0\": [11,11,11],\n", - " \"ZrO2_EaH_0\": [8,8,8],\n", - "} # etc...\n", + " \"Zr3O_EaH_0\": [2, 2, 2],\n", + " \"Zr_EaH_0\": [11, 11, 11],\n", + " \"ZrO2_EaH_0\": [8, 8, 8],\n", + "} # etc...\n", "\n", "for name, kpts in converged_kpoints_dict.items():\n", " kpoints = Kpoints.gamma_automatic(kpts=kpts)\n", @@ -529,14 +545,26 @@ "Remember that the final `ENCUT` used for the energy calculations should be the same as for your host\n", "material & defects, and that you may still need to account for Pulay stress by increasing `ENCUT` for\n", "the geometry relaxations (a typical rule of thumb being 1.3*converged `ENCUT`) or re-relaxing each\n", - "structure until the volume change is minimal (roughly <0.3%). This is not the case for the\n", - "molecule-in-a-box competing phases however, due to the large simulation box size and fixed volume." + "structure until the volume change is minimal (roughly <0.3%). This is not a concern for the\n", + "molecule-in-a-box competing phases, due to the large simulation box size and fixed volume." ], "metadata": { "collapsed": false }, "id": "621f6eb8e7815ba5" }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "```{tip}\n", + "For hybrid DFT competing phase relaxations, it is often a good idea to use the [`NKRED(X,Y,Z)`](https://www.vasp.at/wiki/index.php/NKRED) `INCAR` tag(s), which reduce the _k_-point grid used in the Fock exchange potential by the factor specified, to speed up the calculations while retaining good accuracy. Note that `NKRED(X,Y,Z)` needs to divide into the number of _k_-points in the corresponding direction. Typically `NKRED(X,Y,Z)` has a greater effect on energy rather than force accuracy, and so it is often useful to use `NKRED(X,Y,Z)` values of `2` – or possibly `3` for high _k_-point densities – to pre-converge the structure relaxation, before then continuing the calculations without `NKRED(X,Y,Z)` for the final energy; which often then only requires one or two ionic steps. \n", + "\n", + "`NKRED(X,Y,Z)` is particularly useful for metals, where overall dense _k_-point grids are required, but the Fock exchange contribution typically converges at much lower _k_-point densities. In such cases, `NKRED(X,Y,Z)` can be used to greatly reduce the computational cost with minimal loss of accuracy. A further relaxation/energy calculation without `NKRED(X,Y,Z)` may not be required in these cases, but it is good practice to check the accuracy by comparing the energies of the same structure with and without `NKRED(X,Y,Z)`.\n", + "```" + ], + "id": "47203ec1c58e5147" + }, { "cell_type": "markdown", "id": "7e57cb66", @@ -624,7 +652,9 @@ "cell_type": "code", "outputs": [], "source": [ - "ex_cp.convergence_setup(user_incar_settings={'ENCUT':550}) # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" + "ex_cp.convergence_setup(\n", + " user_incar_settings={\"ENCUT\": 550}\n", + ") # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" ], "metadata": { "collapsed": false, @@ -674,7 +704,9 @@ }, "outputs": [], "source": [ - "ex_cp.vasp_std_setup(user_incar_settings={'ENCUT':550}) # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" + "ex_cp.vasp_std_setup(\n", + " user_incar_settings={\"ENCUT\": 550}\n", + ") # For custom INCAR settings, any flags that aren't numbers or True/False need to be input as strings with quotation marks" ] }, { @@ -719,8 +751,8 @@ "source": [ "### Read in data from `vasprun.xml` files\n", "\n", - "Once you've calculated your competing phases, you will want to parse the results to determine the chemical potential limits of your host material. To do this, we need to parse the `vasprun.xml` files from your final\n", - "production-run competing phase calculations. To download the `vasprun.xml` files from the HPCs recursively, you can recursively `rsync`:\n", + "Once you've calculated your competing phases, you will want to parse the results to determine the chemical potential limits of your host material. To do this, we need to parse the `vasprun.xml(.gz)` files from your final\n", + "production-run competing phase calculations. To download the `vasprun.xml(.gz)` files from the HPCs recursively, you can recursively `rsync`:\n", "\n", "```bash \n", "rsync -azvuR hpc:'path/to/the/base/folder/competing_phases/./*_EaH_*/vasp_std/vasprun.xml*' .\n", @@ -735,31 +767,36 @@ }, { "cell_type": "code", - "outputs": [], "source": [ "from doped.chemical_potentials import CompetingPhasesAnalyzer\n", + "\n", "cpa = CompetingPhasesAnalyzer(\"ZrO2\")" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-02T15:41:24.830770Z", - "start_time": "2024-03-02T15:41:24.817759Z" + "end_time": "2024-04-10T04:16:01.243204Z", + "start_time": "2024-04-10T04:15:54.441658Z" } }, "id": "643890c87cd24d4f", - "execution_count": 22 + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 23, "id": "e273f8fa", "metadata": { "ExecuteTime": { - "end_time": "2024-03-02T15:41:26.926880Z", - "start_time": "2024-03-02T15:41:26.600163Z" + "end_time": "2024-04-10T04:16:01.558269Z", + "start_time": "2024-04-10T04:16:01.244396Z" } }, + "source": [ + "# in this case we have our competing phases in the ZrO2 subfolder of the competing_phases folder,\n", + "# with 'relax' subfolders in each _EaH_ folder\n", + "cpa.from_vaspruns(path=\"./competing_phases/ZrO2/\", folder=\"relax\")" + ], "outputs": [ { "name": "stdout", @@ -769,12 +806,7 @@ ] } ], - "source": [ - "# in this case we have our competing phases in the ZrO2 subfolder of the competing_phases folder,\n", - "# with 'relax' subfolders in each _EaH_ folder\n", - "cpa.from_vaspruns(path='./competing_phases/ZrO2/',\n", - " folder='relax')" - ] + "execution_count": 2 }, { "cell_type": "markdown", @@ -788,30 +820,32 @@ }, { "cell_type": "code", + "source": [ + "cpa.from_vaspruns(\n", + " path=\"./competing_phases/ZrO2/\",\n", + " folder=\"relax\",\n", + " csv_path=\"competing_phases/zro2_competing_phase_energies.csv\",\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T04:16:01.780902Z", + "start_time": "2024-04-10T04:16:01.559253Z" + } + }, + "id": "451de7a7a43f13e9", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing 8 vaspruns and pruning to include only lowest-energy polymorphs...\n", - "Competing phase formation energies have been saved to competing_phases/zro2_competing_phase_energies.csv.\n" + "Competing phase formation energies have been saved to competing_phases/zro2_competing_phase_energies.csv\n" ] } ], - "source": [ - "cpa.from_vaspruns(path='./competing_phases/ZrO2/',\n", - " folder='relax',\n", - " csv_path='competing_phases/zro2_competing_phase_energies.csv')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:41:44.122965Z", - "start_time": "2024-03-02T15:41:43.650047Z" - } - }, - "id": "451de7a7a43f13e9", - "execution_count": 25 + "execution_count": 3 }, { "cell_type": "markdown", @@ -828,11 +862,12 @@ "outputs": [], "source": [ "from pathlib import Path\n", - "path = 'path/to/base'\n", + "\n", + "path = \"path/to/base\"\n", "all_paths = []\n", "for p in Path(path).iterdir():\n", - " if not p.name.startswith('.'):\n", - " pp = p / 'relax' / 'vasprun.xml'\n", + " if not p.name.startswith(\".\"):\n", + " pp = p / \"relax\" / \"vasprun.xml\"\n", " if pp.is_file():\n", " all_paths.append(pp)\n", "\n", @@ -841,7 +876,8 @@ "metadata": { "collapsed": false }, - "id": "1a9d0c0c4e350842" + "id": "1a9d0c0c4e350842", + "execution_count": null }, { "cell_type": "markdown", @@ -862,6 +898,18 @@ }, { "cell_type": "code", + "source": [ + "table = cpa.to_LaTeX_table()\n", + "print(table)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T02:28:18.071044Z", + "start_time": "2024-04-10T02:28:18.057524Z" + } + }, + "id": "8800d00cc85863b7", "outputs": [ { "name": "stdout", @@ -869,11 +917,11 @@ "text": [ "\\begin{table}[h]\n", "\\centering\n", - "\\caption{Formation energies ($\\Delta E_f$) per formula unit of \\ce{ZrO2} and all competing phases, with k-meshes used in calculations. Only the lowest energy polymorphs are included}\n", + "\\caption{Formation energies per formula unit ($\\Delta E_f$) of \\ce{ZrO2} and all competing phases, with k-meshes used in calculations. Only the lowest energy polymorphs are included}\n", "\\label{tab:competing_phase_formation_energies}\n", "\\begin{tabular}{ccc}\n", "\\hline\n", - "Formula & k-mesh & $\\Delta E_f$ (eV) \\\\ \\hline \n", + "Formula & k-mesh & $\\Delta E_f$ (eV/fu) \\\\ \\hline \n", "\\ce{ZrO2} & 3$\\times$3$\\times$3 & -10.975 \\\\ \n", "\\ce{O2} & 2$\\times$2$\\times$2 & 0.000 \\\\ \n", "\\ce{Zr} & 9$\\times$9$\\times$5 & 0.000 \\\\ \n", @@ -885,19 +933,7 @@ ] } ], - "source": [ - "table = cpa.to_LaTeX_table()\n", - "print(table)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:41:54.943783Z", - "start_time": "2024-03-02T15:41:54.905232Z" - } - }, - "id": "8800d00cc85863b7", - "execution_count": 26 + "execution_count": 4 }, { "cell_type": "markdown", @@ -917,62 +953,23 @@ "cell_type": "markdown", "id": "dbfaed51e5acdf17", "metadata": {}, - "source": [ - "### Read in data from a csv\n", - "\n", - "As a sidenote, you can also read in the data from a previously parsed `csv` file, as long as it contains the following headers: `'formula', 'energy_per_fu', 'energy_per_atom', 'energy', 'formation_energy'`\n" - ] + "source": "As a sidenote, you can also read in the data from a previously parsed `csv` file:" }, { "cell_type": "code", - "outputs": [], "source": [ - "cpa.from_csv(csv_path='competing_phases/zro2_competing_phase_energies.csv')" + "cpa.from_csv(csv_path=\"competing_phases/zro2_competing_phase_energies.csv\")" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-02T15:42:00.354756Z", - "start_time": "2024-03-02T15:42:00.328669Z" + "end_time": "2024-04-10T02:28:53.838150Z", + "start_time": "2024-04-10T02:28:53.805347Z" } }, "id": "bffb68b477e64f90", - "execution_count": 27 - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "784d5a74-7d5d-4c1c-a481-d13fca3b098d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-03-02T15:42:01.244741Z", - "start_time": "2024-03-02T15:42:01.235549Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculated chemical potential limits: \n", - "\n", - " Zr O\n", - "0 -10.975428 0.000000\n", - "1 -0.199544 -5.387942\n" - ] - }, - { - "data": { - "text/plain": "{'limits': {'ZrO2-O2': {'Zr': -20.81910468, 'O': -7.006602065},\n 'Zr3O-ZrO2': {'Zr': -10.04321996, 'O': -12.394544425}},\n 'elemental_refs': {'O': -7.006602065, 'Zr': -9.84367624},\n 'limits_wrt_el_refs': {'ZrO2-O2': {'Zr': -10.975428439999998, 'O': 0.0},\n 'Zr3O-ZrO2': {'Zr': -0.19954371999999942, 'O': -5.387942359999999}}}" - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cpa.chempots" - ] + "outputs": [], + "execution_count": 5 }, { "cell_type": "markdown", @@ -988,40 +985,97 @@ }, { "cell_type": "code", + "source": [ + "cpa.calculate_chempots()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:12:28.734429Z", + "start_time": "2024-04-10T03:12:28.695809Z" + } + }, + "id": "974ea8f21882a78f", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Calculated chemical potential limits: \n", + "Calculated chemical potential limits (in eV wrt elemental reference phases): \n", "\n", - " Zr O\n", - "0 -10.975428 0.000000\n", - "1 -0.199544 -5.387942\n" + " Zr O\n", + "Limit \n", + "ZrO2-O2 -10.9754 0.0000\n", + "Zr3O-ZrO2 -0.1995 -5.3879\n" ] }, { "data": { - "text/plain": " Zr O\n0 -10.975428 0.000000\n1 -0.199544 -5.387942", - "text/html": "
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" + ] }, - "execution_count": 29, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", "source": [ - "cpa.calculate_chempots()" + "Here, the compositions listed under `Limit` are the competing phases which are in equilibrium with the host/bulk material at the given point in the phase diagram, which corresponds to a vertex on the chemical stability region of the host – i.e. corresponding to a chemical potential limit for the host material. \n", + "In other words, it represents an extremal point in the hyperplane of chemical potentials in which the host compound is stable. Thus in this case the \"ZrO2-O2\" limit corresponds to the oxygen-rich chemical potential limit, corresponding to the maximum oxygen chemical potential at which the host (ZrO2) is stable – in this case being in equilibrium with elemental $O_2$. Likewise \"Zr3O-ZrO2\" represents the Zr-rich limit, where the Zr chemical potential is maximised (under the constraint of the host material being stable with respect to decomposition). " ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:42:03.303532Z", - "start_time": "2024-03-02T15:42:03.272998Z" - } - }, - "id": "974ea8f21882a78f", - "execution_count": 29 + "id": "2e14bebf7d4770" }, { "cell_type": "markdown", @@ -1035,47 +1089,93 @@ }, { "cell_type": "code", + "source": [ + "cpa.calculate_chempots(csv_path=\"competing_phases/zro2_chempots.csv\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T03:12:30.264104Z", + "start_time": "2024-04-10T03:12:30.255726Z" + } + }, + "id": "74f8671827293d51", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved chemical potential limits to csv file: competing_phases/zro2_chempots.csv\n", - "Calculated chemical potential limits: \n", + "Calculated chemical potential limits (in eV wrt elemental reference phases): \n", "\n", - " Zr O\n", - "0 -10.975428 0.000000\n", - "1 -0.199544 -5.387942\n" + " Zr O\n", + "Limit \n", + "ZrO2-O2 -10.9754 0.0000\n", + "Zr3O-ZrO2 -0.1995 -5.3879\n" ] }, { "data": { - "text/plain": " Zr O\n0 -10.975428 0.000000\n1 -0.199544 -5.387942", - "text/html": "
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" + ] }, - "execution_count": 30, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "cpa.calculate_chempots(csv_path='competing_phases/zro2_chempots.csv')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:42:05.896534Z", - "start_time": "2024-03-02T15:42:05.885569Z" - } - }, - "id": "74f8671827293d51", - "execution_count": 30 + "execution_count": 5 }, { "cell_type": "markdown", - "source": [ - "To use these parsed chemical potential limits for computing the defect formation energies with `doped` (e.g. in `plotting.formation_energy_plot()`, `analysis.formation_energy_table()` etc.) we can use the `cpa.chempots` attribute, which is a dictionary of the chemical potential limits for each element in the host material:" - ], + "source": "To use these parsed chemical potential limits for computing the defect formation energies with `doped` (i.e. with the `DefectThermodynamics` plotting & analysis methods) we can use the `cpa.chempots` attribute, which is a dictionary of the chemical potential limits for each element in the host material:", "metadata": { "collapsed": false }, @@ -1083,28 +1183,42 @@ }, { "cell_type": "code", + "source": "cpa.chempots", + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T02:29:18.287104Z", + "start_time": "2024-04-10T02:29:18.284363Z" + } + }, + "id": "c064850757df33bb", "outputs": [ { "data": { - "text/plain": "{'limits': {'ZrO2-O2': {'Zr': -20.81910468, 'O': -7.006602065},\n 'Zr3O-ZrO2': {'Zr': -10.04321996, 'O': -12.394544425}},\n 'elemental_refs': {'O': -7.006602065, 'Zr': -9.84367624},\n 'limits_wrt_el_refs': {'ZrO2-O2': {'Zr': -10.975428439999998, 'O': 0.0},\n 'Zr3O-ZrO2': {'Zr': -0.19954371999999942, 'O': -5.387942359999999}}}" + "text/plain": [ + "{'limits': {'ZrO2-O2': {'Zr': -20.81910468, 'O': -7.006602065},\n", + " 'Zr3O-ZrO2': {'Zr': -10.04321996, 'O': -12.394544425}},\n", + " 'elemental_refs': {'O': -7.006602065, 'Zr': -9.84367624},\n", + " 'limits_wrt_el_refs': {'ZrO2-O2': {'Zr': -10.975428439999998, 'O': 0.0},\n", + " 'Zr3O-ZrO2': {'Zr': -0.19954371999999942, 'O': -5.387942359999999}}}" + ] }, - "execution_count": 31, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", "source": [ - "cpa.chempots" + "The keys in the `'limits'` and `'limits_wrt_el_refs'` sub-dictionaries list the competing phases at the given chemical potential limit, as explained above, while `'elemental_refs'` gives the computed reference energies per atom of the elemental phases (used to obtain the formal chemical potentials, which are referenced to the energies of the elements in their standard states).\n", + "\n", + "As shown in the defect thermodynamics and plotting customisation tutorials, we can specify the chemical potential limit at which to obtain the defect formation energies using the `limit` parameter, which we can set to either ``\"X-rich\"/\"X-poor\"`` where X is an element in the system, in which case the most X-rich/poor limit will be used (e.g. \"O-rich\"), or a key in the ``chempots[\"limits\"]`` dictionary. " ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:42:08.210353Z", - "start_time": "2024-03-02T15:42:08.207050Z" - } - }, - "id": "c064850757df33bb", - "execution_count": 31 + "id": "25965a38994147da" }, { "cell_type": "markdown", @@ -1118,22 +1232,22 @@ }, { "cell_type": "code", - "outputs": [], "source": [ "from monty.serialization import dumpfn, loadfn\n", "\n", - "dumpfn(cpa.chempots, fn='competing_phases/zro2_chempots.json')\n", - "zro2_chempots = loadfn('competing_phases/zro2_chempots.json')" + "dumpfn(cpa.chempots, fn=\"competing_phases/zro2_chempots.json\")\n", + "zro2_chempots = loadfn(\"competing_phases/zro2_chempots.json\")" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-03-02T15:42:09.395542Z", - "start_time": "2024-03-02T15:42:09.378486Z" + "end_time": "2024-04-10T03:12:34.322998Z", + "start_time": "2024-04-10T03:12:34.319740Z" } }, "id": "6ef36683e38ec4b1", - "execution_count": 32 + "outputs": [], + "execution_count": 6 }, { "cell_type": "markdown", @@ -1159,6 +1273,7 @@ ], "source": [ "from pymatgen.analysis.chempot_diagram import ChemicalPotentialDiagram\n", + "\n", "cpd = ChemicalPotentialDiagram(cpa.intrinsic_phase_diagram.entries)\n", "plot = cpd.get_plot()\n", "plot.show(\"png\", dpi=400)" @@ -1230,8 +1345,8 @@ } ], "source": [ - "cpd = ChemicalPotentialDiagram(loadfn('competing_phases/ytos_phase_diagram.json').entries)\n", - "plot = cpd.get_plot(formulas_to_draw=['Y2Ti2S2O5'])\n", + "cpd = ChemicalPotentialDiagram(loadfn(\"competing_phases/ytos_phase_diagram.json\").entries)\n", + "plot = cpd.get_plot(formulas_to_draw=[\"Y2Ti2S2O5\"])\n", "plot.show(\"png\", dpi=400)" ] }, @@ -1249,45 +1364,46 @@ }, { "cell_type": "code", + "source": [ + "la_cpa = CompetingPhasesAnalyzer(\"ZrO2\", extrinsic_species=\"La\")\n", + "la_cpa.from_vaspruns(\n", + " path=\"./competing_phases/La_ZrO2/\",\n", + " folder=\"relax\",\n", + " csv_path=\"./competing_phases/zro2_la_competing_phase_energies.csv\",\n", + ")\n", + "df = la_cpa.calculate_chempots(csv_path=\"./competing_phases/zro2_la_chempots.csv\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T04:10:58.017289Z", + "start_time": "2024-04-10T04:10:57.692650Z" + } + }, + "id": "443e8c66a800bb1a", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing 11 vaspruns and pruning to include only lowest-energy polymorphs...\n", - "Competing phase formation energies have been saved to ./competing_phases/zro2_la_competing_phase_energies.csv.\n", + "Competing phase formation energies have been saved to ./competing_phases/zro2_la_competing_phase_energies.csv\n", "Calculating chempots for La\n", "Saved chemical potential limits to csv file: ./competing_phases/zro2_la_chempots.csv\n", - "Calculated chemical potential limits: \n", + "Calculated chemical potential limits (in eV wrt elemental reference phases): \n", "\n", - " Zr O La La_limiting_phase\n", - "0 -10.975428 0.000000 -9.462987 La2Zr2O7\n", - "1 -0.199544 -5.387942 -1.381074 La2Zr2O7\n" + " Zr O La La-Limiting Phase\n", + "Limit \n", + "ZrO2-O2 -10.9754 0.0000 -9.463016 La2Zr2O7\n", + "Zr3O-ZrO2 -0.1995 -5.3879 -1.381266 La2Zr2O7\n" ] } ], - "source": [ - "la_cpa = CompetingPhasesAnalyzer(\"ZrO2\", extrinsic_species=\"La\")\n", - "la_cpa.from_vaspruns(path='./competing_phases/La_ZrO2/',\n", - " folder='relax',\n", - " csv_path='./competing_phases/zro2_la_competing_phase_energies.csv')\n", - "df = la_cpa.calculate_chempots(csv_path='./competing_phases/zro2_la_chempots.csv')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-03-02T15:44:23.897322Z", - "start_time": "2024-03-02T15:44:23.117335Z" - } - }, - "id": "443e8c66a800bb1a", - "execution_count": 38 + "execution_count": 2 }, { "cell_type": "markdown", - "source": [ - "As before, we can get the chemical potential limits in the format required for `plotting.formation_energy_plot()`, `analysis.formation_energy_table()` etc. using `cpa.chempots`, which can be easily dumped to a reusable `json` file for later use:" - ], + "source": "As before, we can get the chemical potential limits in the format required for the `DefectThermodynamics` plotting and analysis methods using `cpa.chempots`, which can be easily dumped to a reusable `json` file for later use:", "metadata": { "collapsed": false }, @@ -1299,8 +1415,8 @@ "source": [ "from monty.serialization import dumpfn, loadfn\n", "\n", - "dumpfn(la_cpa.chempots, fn='competing_phases/zro2_la_chempots.json')\n", - "la_chemlims = loadfn('competing_phases/zro2_la_chempots.json')" + "dumpfn(la_cpa.chempots, fn=\"competing_phases/zro2_la_chempots.json\")\n", + "la_chemlims = loadfn(\"competing_phases/zro2_la_chempots.json\")" ], "metadata": { "collapsed": false, @@ -1363,9 +1479,9 @@ } ], "source": [ - "first = loadfn('competing_phases/zro2_la_chempots.json')\n", - "second = loadfn('competing_phases/zro2_y_chempots.json')\n", - "extrinsic_species = 'Y' # this should be set to whatever is the extrinsic species in the second dictionary\n", + "first = loadfn(\"competing_phases/zro2_la_chempots.json\")\n", + "second = loadfn(\"competing_phases/zro2_y_chempots.json\")\n", + "extrinsic_species = \"Y\" # this should be set to whatever is the extrinsic species in the second dictionary\n", "d = combine_extrinsic(first=first, second=second, extrinsic_species=extrinsic_species)\n", "print(d)" ] diff --git a/examples/competing_phases/zro2_chempots.csv b/examples/competing_phases/zro2_chempots.csv index a179193e..e54b7edc 100644 --- a/examples/competing_phases/zro2_chempots.csv +++ b/examples/competing_phases/zro2_chempots.csv @@ -1,3 +1,3 @@ -Zr,O --10.975428439999998,0.0 --0.19954371999999942,-5.387942359999999 +Limit,Zr,O +ZrO2-O2,-10.9754,0.0 +Zr3O-ZrO2,-0.1995,-5.3879 diff --git a/examples/competing_phases/zro2_competing_phase_energies.csv b/examples/competing_phases/zro2_competing_phase_energies.csv index 5303aa56..b467dfdd 100644 --- a/examples/competing_phases/zro2_competing_phase_energies.csv +++ b/examples/competing_phases/zro2_competing_phase_energies.csv @@ -1,9 +1,9 @@ -formula,formation_energy,energy_per_atom,energy_per_fu,energy,kpoints,Zr,O -ZrO2,-10.975428440000002,-11.610769603333333,-34.83230881,-139.32923524,3x3x3,1.0,2.0 -O2,0.0,-7.006602065,-14.01320413,-14.01320413,2x2x2,0.0,2.0 -Zr,0.0,-9.84367624,-9.84367624,-19.68735248,9x9x5,1.0,0.0 -Zr,0.025160013333334064,-9.818516226666667,-9.818516226666667,-58.91109736,6x6x6,1.0,0.0 -Zr2O,-5.728958971666668,-10.807637838888889,-32.42291351666667,-194.5374811,5x5x2,2.0,1.0 -Zr3O,-5.986573519999993,-10.63105107625,-42.524204305,-85.04840861,5x5x5,3.0,1.0 -Zr3O,-5.935114089999992,-10.61818621875,-42.472744875,-84.94548975,5x5x5,3.0,1.0 -ZrO2,-10.951109995000003,-11.602663455,-34.807990365,-278.46392292,3x3x1,1.0,2.0 +Formula,Formation Energy (eV/fu),Formation Energy (eV/atom),DFT Energy (eV/atom),DFT Energy (eV/fu),DFT Energy (eV),k-points,Zr,O +ZrO2,-10.975428440000002,-3.658476146666667,-11.610769603333333,-34.83230881,-139.32923524,3x3x3,1.0,2.0 +O2,0.0,0.0,-7.006602065,-14.01320413,-14.01320413,2x2x2,0.0,2.0 +Zr,0.0,0.0,-9.84367624,-9.84367624,-19.68735248,9x9x5,1.0,0.0 +Zr,0.025160013333334064,0.025160013333334064,-9.818516226666667,-9.818516226666667,-58.91109736,6x6x6,1.0,0.0 +Zr2O,-5.728958971666668,-1.909652990555556,-10.807637838888889,-32.42291351666667,-194.5374811,5x5x2,2.0,1.0 +Zr3O,-5.986573519999993,-1.4966433799999983,-10.63105107625,-42.524204305,-85.04840861,5x5x5,3.0,1.0 +Zr3O,-5.935114089999992,-1.483778522499998,-10.61818621875,-42.472744875,-84.94548975,5x5x5,3.0,1.0 +ZrO2,-10.951109995000003,-3.650369998333334,-11.602663455,-34.807990365,-278.46392292,3x3x1,1.0,2.0 diff --git a/examples/competing_phases/zro2_la_chempots.csv b/examples/competing_phases/zro2_la_chempots.csv index 81e4dc3a..d7a15702 100644 --- a/examples/competing_phases/zro2_la_chempots.csv +++ b/examples/competing_phases/zro2_la_chempots.csv @@ -1,3 +1,3 @@ -Zr,O,La,La_limiting_phase --10.975428439999998,0.0,-9.462987480000002,La2Zr2O7 --0.19954371999999942,-5.387942359999999,-1.3810739400000038,La2Zr2O7 +Limit,Zr,O,La,La-Limiting Phase +ZrO2-O2,-10.9754,0.0,-9.46301592,La2Zr2O7 +Zr3O-ZrO2,-0.1995,-5.3879,-1.3812659200000006,La2Zr2O7 diff --git a/examples/competing_phases/zro2_la_competing_phase_energies.csv b/examples/competing_phases/zro2_la_competing_phase_energies.csv index 930b2733..ebfec920 100644 --- a/examples/competing_phases/zro2_la_competing_phase_energies.csv +++ b/examples/competing_phases/zro2_la_competing_phase_energies.csv @@ -1,12 +1,12 @@ -formula,formation_energy,energy_per_atom,energy_per_fu,energy,kpoints,Zr,O,La -ZrO2,-10.975428440000002,-11.610769603333333,-34.83230881,-139.32923524,3x3x3,1.0,2.0,0.0 -La,0.0,-5.00458616,-5.00458616,-20.01834464,10x10x3,0.0,0.0,1.0 -O2,0.0,-7.006602065,-14.01320413,-14.01320413,2x2x2,0.0,2.0,0.0 -Zr,0.0,-9.84367624,-9.84367624,-19.68735248,9x9x5,1.0,0.0,0.0 -Zr,0.025160013333334064,-9.818516226666667,-9.818516226666667,-58.91109736,6x6x6,1.0,0.0,0.0 -La2O3,-18.01721104,-9.809237911,-49.046189555,-392.36951644,3x3x3,0.0,3.0,2.0 -Zr2O,-5.728958971666668,-10.807637838888889,-32.42291351666667,-194.5374811,5x5x2,2.0,1.0,0.0 -Zr3O,-5.986573519999993,-10.63105107625,-42.524204305,-85.04840861,5x5x5,3.0,1.0,0.0 -Zr3O,-5.935114089999992,-10.61818621875,-42.472744875,-84.94548975,5x5x5,3.0,1.0,0.0 -ZrO2,-10.951109995000003,-11.602663455,-34.807990365,-278.46392292,3x3x1,1.0,2.0,0.0 -La2Zr2O7,-40.87683184,-10.874506463181818,-119.619571095,-239.23914219,3x3x3,2.0,7.0,2.0 +Formula,Formation Energy (eV/fu),Formation Energy (eV/atom),DFT Energy (eV/atom),DFT Energy (eV/fu),DFT Energy (eV),k-points,Zr,O,La +ZrO2,-10.975428440000002,-3.658476146666667,-11.610769603333333,-34.83230881,-139.32923524,3x3x3,1.0,2.0,0.0 +La,0.0,0.0,-5.00458616,-5.00458616,-20.01834464,10x10x3,0.0,0.0,1.0 +O2,0.0,0.0,-7.006602065,-14.01320413,-14.01320413,2x2x2,0.0,2.0,0.0 +Zr,0.0,0.0,-9.84367624,-9.84367624,-19.68735248,9x9x5,1.0,0.0,0.0 +Zr,0.025160013333334064,0.025160013333334064,-9.818516226666667,-9.818516226666667,-58.91109736,6x6x6,1.0,0.0,0.0 +La2O3,-18.01721104,-3.6034422079999997,-9.809237911,-49.046189555,-392.36951644,3x3x3,0.0,3.0,2.0 +Zr2O,-5.728958971666668,-1.909652990555556,-10.807637838888889,-32.42291351666667,-194.5374811,5x5x2,2.0,1.0,0.0 +Zr3O,-5.986573519999993,-1.4966433799999983,-10.63105107625,-42.524204305,-85.04840861,5x5x5,3.0,1.0,0.0 +Zr3O,-5.935114089999992,-1.483778522499998,-10.61818621875,-42.472744875,-84.94548975,5x5x5,3.0,1.0,0.0 +ZrO2,-10.951109995000003,-3.650369998333334,-11.602663455,-34.807990365,-278.46392292,3x3x1,1.0,2.0,0.0 +La2Zr2O7,-40.87683184,-3.716075621818182,-10.874506463181818,-119.619571095,-239.23914219,3x3x3,2.0,7.0,2.0 diff --git a/examples/parsing_tutorial.ipynb b/examples/parsing_tutorial.ipynb index b80a2c4d..50b5053c 100644 --- a/examples/parsing_tutorial.ipynb +++ b/examples/parsing_tutorial.ipynb @@ -145,7 +145,7 @@ "the supplied `dielectric` constant and calculation outputs, and will\n", "warn you if any required outputs are missing. \n", "\n", - "Additionally, the `DefectsParser` class automatically checks the consistency and estimated error of the defect finite-size charge correction, and will warn you if the estimated error is above `error_tolerance` (50 meV by default). As shown later in the [Charge Corrections](#charge_corrections) section, we can directly visualise the \n", + "Additionally, the `DefectsParser` class automatically checks the consistency and estimated error of the defect finite-size charge correction (using the standard error of the mean potential difference in the sampling region, times the defect charge), and will warn you if the estimated error is above `error_tolerance` – 50 meV by default. As shown later in the [Charge Corrections](#charge_corrections) section, we can directly visualise the \n", "finite-size charge correction plots (showing how they are being computed) easily with `doped`, which is \n", "recommended if any of these warnings about the charge correction accuracy are printed.\n" ] @@ -391,6 +391,15 @@ "plot = CdTe_example_thermo.plot(limit=\"Te-rich\") # can set limit to X-rich/poor and doped will automatically determine the most X-rich/poor limit" ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "```{tip}\n", + "As shown above, can specify the chemical potential limit at which to obtain and plot the defect formation energies using the ``limit`` parameter, which we can set to either ``\"X-rich\"/\"X-poor\"`` where X is an element in the system, in which case the most X-rich/poor limit will be used (e.g. \"Cd-rich\"), or a key in the ``chempots[\"limits\"]`` dictionary (e.g. ``\"Cd-CdTe\"`` from that shown above). Alternatively, one can also provide a single chemical potential limit in the form of a dictonary to the ``DefectThermodynamics`` methods – see docstrings for more details.\n", + "``` " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1889,7 +1898,7 @@ "outputs": [], "source": [ "# you can run this cell to see the possible arguments for this function (or go to the python API docs)\n", - "v_Cd_thermo.get_formation_energies?" + "CdTe_defects_thermo.get_formation_energies?" ] }, { @@ -2001,14 +2010,20 @@ }, { "cell_type": "code", - "execution_count": 18, "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-12T20:05:20.933795Z", - "start_time": "2024-02-12T20:05:17.562932Z" - }, - "collapsed": false + "end_time": "2024-04-09T22:00:40.279253Z", + "start_time": "2024-04-09T22:00:14.272141Z" + } }, + "source": [ + "from doped.analysis import DefectParser # can use DefectParser to parse individual defects if desired\n", + "F_O_1_entry = DefectParser.from_paths(defect_path=\"YTOS/F_O_1\", bulk_path=\"YTOS/Bulk\",\n", + " dielectric = [40.7, 40.7, 25.2]).defect_entry\n", + "print(f\"Charge: {F_O_1_entry.charge_state:+} at site: {F_O_1_entry.defect_supercell_site.frac_coords}\")\n", + "print(f\"Finite-size charge corrections: {F_O_1_entry.corrections}\")" + ], "outputs": [ { "name": "stdout", @@ -2019,13 +2034,7 @@ ] } ], - "source": [ - "from doped.analysis import DefectParser # can use DefectParser to parse individual defects if desired\n", - "F_O_1_entry = DefectParser.from_paths(defect_path=\"YTOS/F_O_1\", bulk_path=\"YTOS/Bulk\",\n", - " dielectric = [40.7, 40.7, 25.2]).defect_entry\n", - "print(f\"Charge: {F_O_1_entry.charge_state:+} at site: {F_O_1_entry.defect_supercell_site.frac_coords}\")\n", - "print(f\"Finite-size charge corrections: {F_O_1_entry.corrections}\")" - ] + "execution_count": 1 }, { "cell_type": "markdown", @@ -2097,20 +2106,22 @@ "If there is still significant variance in the site potential differences in the sampling region (i.e. a \n", "converged plateau is not obtained), then this suggests that the charge correction may not be as accurate\n", " for that particular defect or supercell. This error range of the charge correction is automatically \n", - " computed by `doped`, and can be returned using the `return_correction_error` argument in \n", + " computed by `doped` (by multiplying the defect charge with the standard error of the mean of the electrostatic potential differences in the sampling region), and can be returned using the `return_correction_error` argument in \n", " `get_kumagai_correction`/`get_freysoldt_correction`, or also by adjusting the `error_tolerance` argument: " ] }, { "cell_type": "code", - "execution_count": 20, "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-12T20:05:35.271472Z", - "start_time": "2024-02-12T20:05:34.420649Z" - }, - "collapsed": false + "end_time": "2024-04-09T22:00:41.043346Z", + "start_time": "2024-04-09T22:00:40.280603Z" + } }, + "source": [ + "correction = F_O_1_entry.get_kumagai_correction(error_tolerance=0.0001) # extremely strict tolerance, 0.1 meV" + ], "outputs": [ { "name": "stdout", @@ -2123,24 +2134,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "core.py:239: UserWarning: Estimated error in the Kumagai (eFNV) charge correction for defect F_O_1 is 0.001 eV (i.e. which is greater than the `error_tolerance`: 0.000 eV). You may want to check the accuracy of the correction by plotting the site potential differences (using `defect_entry.get_kumagai_correction()` with `plot=True`). Large errors are often due to unstable or shallow defect charge states (which can't be accurately modelled with the supercell approach). If this error is not acceptable, you may need to use a larger supercell for more accurate energies.\n" + "core.py:289: UserWarning: Estimated error in the Kumagai (eFNV) charge correction for defect F_O_1 is 1.33e-03 eV (i.e. which is greater than the `error_tolerance`: 1.00e-04 eV). You may want to check the accuracy of the correction by plotting the site potential differences (using `defect_entry.get_kumagai_correction()` with `plot=True`). Large errors are often due to unstable or shallow defect charge states (which can't be accurately modelled with the supercell approach; see https://doped.readthedocs.io/en/latest/Tips.html#perturbed-host-states). If this error is not acceptable, you may need to use a larger supercell for more accurate energies.\n" ] } ], - "source": [ - "correction = F_O_1_entry.get_kumagai_correction(error_tolerance=0.0001) # extremely strict tolerance, 0.1 meV" - ] + "execution_count": 2 }, { + "metadata": {}, "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2024-02-12T20:05:40.056487Z", - "start_time": "2024-02-12T20:05:39.223749Z" - }, - "collapsed": false - }, "outputs": [ { "name": "stdout", @@ -2160,6 +2162,7 @@ "output_type": "execute_result" } ], + "execution_count": 21, "source": [ "correction, error = F_O_1_entry.get_kumagai_correction(return_correction_error=True)\n", "error # calculated error range of 1.3 meV in our charge correction here" diff --git a/examples/plotting_customisation_tutorial.ipynb b/examples/plotting_customisation_tutorial.ipynb index e2306a48..10a641bd 100644 --- a/examples/plotting_customisation_tutorial.ipynb +++ b/examples/plotting_customisation_tutorial.ipynb @@ -86,9 +86,7 @@ "output_type": "display_data" } ], - "source": [ - "plot = CdTe_thermo.plot(limit=\"Te-rich\") " - ] + "source": "plot = CdTe_thermo.plot(limit=\"Te-rich\") # plot at the Te-rich chemical potential limit" }, { "cell_type": "code", @@ -1287,7 +1285,8 @@ "metadata": { "collapsed": false }, - "id": "99126e54a2b941a7" + "id": "99126e54a2b941a7", + "execution_count": null }, { "cell_type": "markdown", @@ -1346,7 +1345,8 @@ "metadata": { "collapsed": false }, - "id": "5a52b1b2599cab50" + "id": "5a52b1b2599cab50", + "execution_count": null } ], "metadata": { diff --git a/examples/thermodynamics_tutorial.ipynb b/examples/thermodynamics_tutorial.ipynb index 9481404e..7d26996f 100644 --- a/examples/thermodynamics_tutorial.ipynb +++ b/examples/thermodynamics_tutorial.ipynb @@ -23,21 +23,21 @@ }, { "cell_type": "code", - "execution_count": 2, "id": "1e72fb8cce88d581", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-27T13:07:45.610837Z", - "start_time": "2024-02-27T13:07:37.212184Z" - }, - "collapsed": false + "end_time": "2024-04-09T22:39:45.135157Z", + "start_time": "2024-04-09T22:39:32.896034Z" + } }, - "outputs": [], "source": [ "%matplotlib inline\n", "from monty.serialization import loadfn\n", "CdTe_thermo = loadfn(\"CdTe/CdTe_LZ_thermo_wout_meta.json\") # load our DefectThermodynamics object" - ] + ], + "outputs": [], + "execution_count": 5 }, { "cell_type": "markdown", @@ -486,31 +486,41 @@ }, { "cell_type": "code", - "execution_count": 4, "id": "de06c27ba3cfc255", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-27T13:07:55.559729Z", - "start_time": "2024-02-27T13:07:54.085987Z" - }, - "collapsed": false + "end_time": "2024-04-09T22:40:05.069903Z", + "start_time": "2024-04-09T22:40:03.651119Z" + } }, + "source": [ + "CdTe_thermo.dist_tol = 2\n", + "plot = CdTe_thermo.plot(limit=\"Te-rich\")" + ], "outputs": [ { "data": { - "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", "source": [ - "CdTe_thermo.dist_tol = 2\n", - "plot = CdTe_thermo.plot(limit=\"Te-rich\")" - ] + "```{tip}\n", + "As shown above, can specify the chemical potential limit at which to obtain and plot the defect formation energies using the ``limit`` parameter, which we can set to either ``\"X-rich\"/\"X-poor\"`` where X is an element in the system, in which case the most X-rich/poor limit will be used (e.g. \"Cd-rich\"), or a key in the ``chempots[\"limits\"]`` dictionary (e.g. ``\"Cd-CdTe\"`` from that shown above). Alternatively, one can also provide a single chemical potential limit in the form of a dictonary to the ``DefectThermodynamics`` methods – see docstrings for more details.\n", + "``` " + ], + "id": "7651cedbde5c5638" }, { "cell_type": "markdown", @@ -689,7 +699,7 @@ "id": "41a561cead89d920", "metadata": {}, "source": [ - "For example here we see that $Cd_{i}^{+2}$ (coordinated by Te anions) is our dominant (lowest-energy) compensating native donor under _p_-type (Te-rich) conditions, and we can see the doping window of 0.85 eV corresponds to its formation energy at the VBM under Te-rich conditions in the plot above.\n", + "For example here we see that $Cd_{i}^{+2}$ (coordinated by Te anions) is our dominant (lowest-energy) compensating native donor under _p_-type (Te-rich) conditions, and we can see the doping window of 0.49 eV corresponds to its formation energy at the VBM under Te-rich conditions in the plot above.\n", "Meanwhile, $V_{Cd}^{-2}$ is our dominant native compensating acceptor under _n_-type (Cd-rich) conditions. From this initial analysis, we can see that our native defect thermodynamics suggests that CdTe is more _n_-type dopable than _p_-type dopable (having alarger _n_-type doping window/limit)." ] }, @@ -885,10 +895,10 @@ "\n", "for anneal_temp in anneal_temperatures:\n", " fermi_level, e_conc, h_conc, conc_df = CdTe_thermo.get_quenched_fermi_level_and_concentrations(\n", - " fermi_dos=fermi_dos, limit=\"Cd-rich\", annealing_temperature=anneal_temp\n", + " bulk_dos_vr=fermi_dos, limit=\"Cd-rich\", annealing_temperature=anneal_temp\n", " ) # quenching to 300K (default) – alternatively can specify quench (operating) temperature\n", " annealing_fermi_level, annealing_e_conc, annealing_h_conc = CdTe_thermo.get_equilibrium_fermi_level(\n", - " fermi_dos=fermi_dos, limit=\"Cd-rich\", temperature=anneal_temp, return_concs=True,\n", + " bulk_dos_vr=fermi_dos, limit=\"Cd-rich\", temperature=anneal_temp, return_concs=True,\n", " )\n", " annealing_dict[anneal_temp] = {\n", " \"annealing_fermi_level\": annealing_fermi_level,\n", @@ -1096,24 +1106,26 @@ "id": "5e4ecbd74d30b478", "metadata": {}, "source": [ - "As with all semiconducting/insulating materials, the band gap (and thus relative energies of defects and charge carriers) in CdTe is dependent on the temperature. Of course, there are many effects of temperature on the free energies of defect formation (e.g. see [Mosquera-Lois et al. _Chem Soc Rev_ 2023](https://doi.org/10.1039/D3CS00432E)), but here for a crude first approximation we will account for the (experimentally-known) band gap renormalisation as a function of temperature, assuming that the renormalisation occurs symmetrically such that the VBM and CBM eigenvalues are down/up-shifted by the same amount (ΔEg/2) at each temperature while the defect formation energies / transition levels remain fixed. " + "As with all semiconducting/insulating materials, the band gap (and thus relative energies of defects and charge carriers) in CdTe is dependent on the temperature. These changes in the band edge positions can affect the defect/carrier formation energies & concentrations, and thus the Fermi level position / doping behaviour. In `doped`, we can accounted for these changes in band edge positions (if known) using the `delta_gap` parameter in the `get_quenched_fermi_level_and_concentrations()` method and/or the `scissor_dos()` function as shown below.\n", + "\n", + "Of course, there are many effects of temperature on the free energies of defect formation (e.g. see [Mosquera-Lois et al. _Chem Soc Rev_ 2023](https://doi.org/10.1039/D3CS00432E)), but here for a crude first approximation we will account for the experimentally-known band gap renormalisation of CdTe as a function of temperature, assuming that the renormalisation occurs symmetrically such that the VBM and CBM eigenvalues are down/up-shifted by the same amount (ΔEg/2) at each temperature while the defect formation energies / transition levels remain fixed.\n", + "\n", + "To do this, we first define a function for the temperature-dependent band gap, by fitting to the experimental data: " ] }, { "cell_type": "code", - "execution_count": 16, "id": "d758acc49400c698", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-14T15:15:49.073505Z", - "start_time": "2024-02-14T15:15:49.068653Z" - }, - "collapsed": false + "end_time": "2024-04-09T22:45:39.518847Z", + "start_time": "2024-04-09T22:45:39.511612Z" + } }, - "outputs": [], "source": [ "import numpy as np\n", - "belas_data =[(1.5110277123929035, 293.61411498177165), # CdTe gap vs Temp, 2014 study\n", + "band_gap_data =[(1.5110277123929035, 293.61411498177165), # CdTe gap vs Temp, Belas et al. 2014\n", " (1.4978368122096875, 323.8690412012745),\n", " (1.4771103223451387, 372.54000946743133),\n", " (1.4544939046587835, 422.5264093083491),\n", @@ -1134,39 +1146,38 @@ " (1.1266016792578308, 1172.322406922116),\n", " (1.1039876737320649, 1223.6242383377944)]\n", "\n", - "params = np.polyfit([x[1] for x in belas_data], [x[0] for x in belas_data], 1, full=True)[0]\n", + "params = np.polyfit([x[1] for x in band_gap_data], [x[0] for x in band_gap_data], 1, full=True)[0]\n", "\n", - "def belas_linear_fit(T): # linear fit of the CdTe experimental band gap vs T\n", - " return params[1] + params[0] * T" - ] + "def band_gap_linear_fit(T): # linear fit of the CdTe experimental band gap vs T\n", + " return params[1] + params[0] * T\n", + "\n", + "# alternatively we could also use a Cubic Spline fit such as this (or any other fitted function):\n", + "# from scipy.interpolate import CubicSpline\n", + "# band_gap_spline_fit = CubicSpline([x[1] for x in band_gap_data], [x[0] for x in band_gap_data])" + ], + "outputs": [], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Let's quickly plot the experimental data and our linear fit, to confirm it looks reasonable:", + "id": "4edad84e8044afe7" }, { "cell_type": "code", - "execution_count": 17, "id": "2140aea7fd1f1276", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-14T15:15:50.489371Z", - "start_time": "2024-02-14T15:15:50.267694Z" - }, - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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EYMeOHTh16hRCQ0Px+PFjxMTEIDU1FV5eXvDx8UFAQAACAwPRpEkTq1wTuXXrFnbu3IkLFy4gNDQUr1+/RkxMDBQKBXLlygU/Pz9UqFABlStXRvv27VG+fHmTtv/PP/9gwIABamWnT59G7dq1Zdd/8+YNdu/ejdOnTyM0NBQPHz5EbGwsEhIS4OrqqnofVKhQAc2aNUPz5s0tfuNsUlISgoKCcOzYMYSGhuLOnTuIjo5GbGwsXFxcVNdvAgMD0bBhQ7Rr187qN/dmh2tOmX+3AgICcPPmTY3rh4WFYdeuXbh8+TKuXbuG169fIzY2FqmpqfD09ISfnx8CAgJQvXp1tGrVCjVr1tT5/MVUUlJScOTIERw9ehTXrl1TO+5SKBTw9PREvnz5EBAQgKpVq6Jly5aoVq2aWa/JZId9nS0IGxQZGSk8PT0FALXH77//bu3QjLZ//37RsmVLYW9vL3l+2h6+vr5i4sSJ4uXLl5I6jxw5Ill/7NixZnsOcu3179/fbO1p8uzZM/HVV18JX19fvV5LBwcH0aFDB3HixAmLx5yV7PLaanL58mXh6uqqFp+zs7O4cOGCSeq/ffu28PLyUqvf09NT3Lx5U6ftM792x48fl11v9+7dolGjRkKhUOj13smXL5/Gz6EpJSYmij/++EP4+/vrFR8AUa1aNbFu3TqRlpZmklimTJkiaeP58+dq6yQkJIg5c+aIokWLaozrwYMHWbaVO3dutW2KFStmUMzFihVTq6dVq1aSdV68eCG++uoryfstq0f+/PnFd999J+Li4nSKJSYmRkyZMkXrayP3UCgUonXr1uLo0aMGvQb6uHPnjhg4cKDw8PDQK0YXFxfx6aefiqtXr5osFl32XVxcnPjll19EkSJF9P58VK5cWaxcuVIolUq9Y5P7fjbFY8WKFXrHEh8fL1asWCGaNGkiHBwcDGq3RYsWYteuXXq3rcmKFSskbcyfP99k9Rsi8/sJsNxh8f79+yVtDx061OD6kpOTxaZNm0T79u2Fi4uLQfu8Vq1aYu3atSb7fZD7TKxfv16y3vXr10Xfvn31jtvR0VH06tVLXLlyxSTxavPo0SPx5Zdf6n1MaW9vLzp06CBOnTolW2/m92Du3LlNFnNERISYMWOG3r8vAIS7u7sYMmSICAsLk6078+9xly5djIr1zp07YtKkSQYd1wAQXl5eYtSoUeLu3btGxZFRo0aN1Nr48ccfZde7evWq6N27t3ByctL7Ne7bt68IDQ01WcyWlvk5GXpcZmnZ7Xzq8ePHwtvbWxLTunXrTFJ/dj6OW716tex6J06cEB06dND7moiPj48YOXKkTucVxkhNTRX//POPqFSpkt7fVwEBAeKvv/4Sb9++NUkscsdXp0+flqwXEhIiOnbsqPdxobu7uxg8eLDZX1Mh3h0PfPbZZ3q/V52cnESfPn00fp9mXr9atWomizk7XXPK/LsVEBAgWSc5OVksXrxYBAQE6P3eLV26tJg3b55ISUkxWcyahIWFic8//1zvc/H3v0XTp08Xb968MWlM2WlfZwc2mUz47rvvJDuoatWqJjv5sYYrV66IWrVq6f1Byfzw8PAQf/75p9qFkA8tmZCcnCwmT55s8Ml0xkfr1q3Fw4cPLRZ7Vqz92upC7oCmRIkSIioqyqh6ExMTRcWKFSV1b968Wec6Mm+bOZlw69Yt0bBhQ6PfN66urmLKlCkiOTnZqOcsZ/369SJ//vxGx1imTBmTXIjOKplw6NAhnS7qZpdkglKpFPPmzRNubm5Gvb4FCxYUO3bs0BrHkiVLhJ+fn9H7sl+/fiI+Pt6g10Kb2NhYMWzYML1P5DI/FAqF6N27t4iIiDA6pqySCf/++69JXtNatWrpnKR8LzskE2JjY8XUqVP1PsjV9mjfvr149eqVXq+FHCYT1K1bt07S9oQJE/SuJzk5Wfzxxx+iUKFCJtvnderUEffv3zf6OWaVTIiPjxfDhg0TdnZ2RsVrb28vxowZY5aT2/j4ePHll18KR0dHo1/Xrl27Sm42MEcyITk5WcyYMcPo3zEAws7OTgwdOlRERkaqtWGqZMKVK1fEJ598ovfNG5oezs7O4o8//jAoIZxZVsmE6Oho8b///c/o2BUKhejRo4d48eKF0TFbWubnwmSC4RYsWCCJKSAgwKj3si0cx2VOJjx79kx06tTJ6O8CBwcHMWLECBEbG2t0zJkFBQUZnPjM+ChYsKDYunWr0fFklUx4/fq16Nq1q9Hxurq6ipkzZ5rk+zWzV69eiT59+pjk+3T48OGSm7oyr2eKZEJ2vOaUVTLh8OHDokSJEkbHW7ZsWdmElSk8ffpUdOvWzSTHBb6+vmLevHlGv2ez477ODmwumZCamiry5csn2Sn79++3dmgGmzFjhsF3Dmp6tGnTRvUl+iElE27fvi0CAwNN+lp6eHiIDRs2WCT+rGTHg185gwcPlsTZoUMHo77I+/fvb/T7OPP2GZMJGzZsMMkPRMbHRx99JG7cuGHwc84oLi7OJAeCGR8KhUJ88803Ij093eC4tCUT5s2bp/OBQHZIJrx9+1Z06NDBZK+vnZ2dmDNnjqT9+Ph40atXL5PuyypVqpj0QkRISIjshVZjHn5+fuLIkSNGxaVp36WlpYmBAweaNF5vb29x+PBhnWOzdjJhy5YtomDBgmaJIX/+/OL69euG7DIVJhPUTZ8+XdL2P//8o1cdwcHBBt1ZpsvD09NTBAcHG/UctSUT7ty5I0qVKmXSmBs2bCiio6ONijmjM2fOmOV7MOMJuKmTCTdu3BDlypUz+fuhSJEi4vz586p2jE0mJCYmilGjRhl9kVPTo3PnzkbfZKYtmRAaGmpQjw9tD19fX7Ft2zajYra0zM+ByQTDJScny958o6kXdVZs5TguYzIhODhY+Pj4mPy7y1R3A6empoqRI0eaLPn5/tGnTx+RlJRkcFzakglnzpwRefPmNWm8Xbt2NWnyfteuXSJPnjwmjdHf31/cunVL1Ubm/xubTMiu15y0JRO+//57k753nZ2dZXubGmPlypUG9UTI6tG+fXuDb2zNrvs6O7C5Qfb379+Ply9fqpVVq1YNLVu2tFJEhktNTcXnn3+ONWvWyP5foVCgZs2aaN++PSpXrozixYsjV65cSE1NRVRUFG7cuIFz587hv//+Q3h4uNq2e/fuRaNGjdTGX8zpQkJC0KFDB0REREj+Z29vj4YNG6Jt27aoVKkSChUqBC8vL7x9+xaRkZEICwvDyZMnsXPnTsn28fHx6NWrFx49eoRx48ZZ6unYtHnz5uHixYs4f/68qmznzp2YNWsWvv32W73rW7JkCVauXKlW1qBBA8ycOdPoWIF345V+8cUXEEKolTs4OKBRo0Zo3bq11vfN9u3b8ebNG0m9169fR506dbB161adx1KX8/LlS7Rr1w4XLlyQ/X+FChXQqVMnVKtWDf7+/siVKxfS09MRExODW7du4dy5c9i+fTvu3r2rtp0QAr/88gvu3r2L9evXw9nZ2eAYM1u2bBlGjRolKQ8ICEDLli3RqFEjFCxYEH5+fnByckLBggVN1rYhkpOT0bZtWxw5ckSt3NHREU2aNFG9BwoWLAgPDw/Exsbi9evXuHjxIg4ePIj9+/cjPT1dbVulUomvvvoKuXPnRt++fQEAiYmJaNWqFU6ePCmJoXDhwujUqRNq166NcuXKwdvbG/b29oiIiMCTJ08QHByMXbt2SfYjAFy6dAkdO3ZEcHCw0WMw79ixA7169UJiYqLkf87OzmjRogVatGiBwMBA5M+fHx4eHkhISMDr169x7do1HDlyBHv37kVcXJzatq9fv0arVq2wfPly9O7d26gYM0pPT0e3bt2wbds2yf8CAwPRrl07VK1aFaVLl4aXlxfs7OwQERGB58+f48SJE9i/fz8uX74s2TY6OhodO3bEsWPHUKVKlSzjqFu3Lp4/fy77vwIFCqgtFypUSO37UZtcuXJluY7c3CUZlSxZEs2aNUP16tVRqlQpFCxYEO7u7khLS0NUVBSePXuGM2fO4MCBAzh79qxk+xcvXqBt27Y4c+YM8ubNq1PcpN3WrVslZZrGN5azbNkyDB48GEqlUvb/BQoUQPPmzVGzZk2UKVMGhQsXhoeHB4QQiIqKwsuXL3Hu3DkcOXIER44ckfz+xcXFoXPnzjh9+jQCAgL0e3JZuHHjBpo0aSI5nndxcVGNz1yhQgUUKlQInp6eSEhIQGRkJG7fvo0jR45gz549ssd6x44dQ+fOnbFv3z44OTkZFePWrVvRp08fvH37Vvb/BQsWVPu+9vX1haOjI2JiYhAeHo7Lly9j7969OHXqlNo+ev36NZo2bYpt27ahVatWRsWY2b59+9C9e3fJd+97np6eaNWqFRo1aoTy5csjf/788PT0RFJSEqKjo3H79m1cuHABu3fvxq1bt9S2ffz4MRo0aIBdu3ahadOmRsUZHx+P2rVrS+a+e8/BwQF169ZF3bp1UaVKFRQvXhy+vr5wdXVFfHy86hzozJkz2LZtG169eiWpY9u2bRg1ahQWLFhgVKxyTp8+jTZt2iAmJkbyv8DAQHTs2BFVq1ZVHY8plUrV6/v+vC3z/GLAu3HMu3TpgtmzZ2Ps2LEmj5uyNycnJ4wZMwZfffWVWvmGDRu0zu8mx9aO4wDgv//+Q48ePZCSkqJWrlAoULt2bbRr1w6VK1dGsWLFkCtXLqSkpEi+C549eyap9/Hjx2jWrBn++ecfjfP16CI+Ph7dunXTOE+Xv78/OnfujBo1aqBMmTLw9vaGQqFATEwM7t69i4sXL2Lnzp24evWqZNs1a9bgwYMH2LlzJ3x8fAyOMbNjx46hXbt2iI+PVyt//1vQtGlTlC9fHvny5YO7uzvi4uIQGRmp9T0AAJs3b4aLiwtWrVpl9Pj5f/75J0aPHi05h3rP398fnTp1Qo0aNRAQEABvb2/Y2dkhJiYG9+7dw6VLl7Br1y5cvHhRbbt79+6hbt26CAoK0ukYXh+2eM1p1KhRmD9/vqS8VKlS6NChg9rr6+DggMjISLx69QqnT5/GwYMHZc9bk5OT0bdvX+TKlQtt2rQxKj6lUonx48fj119/lf2/QqFArVq10K5dO1SqVAklSpRQXe+IiorCrVu3cPLkSezatUt2PsVdu3ahVatWOHjwoF7zK9nivrYo6+Yy9Ddo0CBJZuevv/6ydlgG6du3r2ymSqFQiP79+6tlU7Oye/du2WGSmjdvLoKCgiTlOa1nwtWrV2WzmO+7OIaHh+tUT3Jysli6dKnGu43+/vtvsz6PrGTHO2k0efjwoeTONXt7e73vaLlw4YJwdnZWqyd//vyScfl1kfm1O378uPjvv/8kQyy4uLiIsWPH6txGSkqKWLZsmcZug25ubuLs2bN6xyvEuy7KmrLhbdq0EWfOnNG5rgMHDoi6devK1tWrVy+Deo7I9Uw4fPiwZO6MqlWrir179+pdf0bm7JkwfPhwtTI7OzsxfPhw8fjxY53qvH37tujSpYvG/X/jxg2Rnp4uWrZsKfl/yZIlxYYNG3TqIZKeni5WrVolChQoINvWF198YdBr8l5QUJDscB5ubm5i8uTJOndxj4uLE7///rtkn73/HjB0LH65fTd+/HhJG+3atdPrM3fgwAFRtWpV2de0RIkSIiEhwaB438tcpynHahVCiJ07d0racHFxEYMGDRKXL1/Wq66zZ89qHHaxTZs2BsfIngn/59ChQ5J2q1Spolcdly5dkvRqtbe3F927dxcnTpzQ6/v8+vXrol27drL7vHz58gb3XpM7Zlm0aJEoU6aM5PtlypQp4vXr1zrVGx8fL2bPnq1xTOVx48YZFO97e/fu1dhjODAwUGzZskXn1/fBgwdi2LBhkvo8PT3F1atXTdYz4fDhwxp7VxYvXlz8/fffIjExUef6Tp8+LTvUiKenp7hw4YLRPRPkfi+LFy8ufv/9d73GOE5KShKLFy/WeCfjxo0b9YorI7meCffu3ZO9g7ZLly7i0qVLOtd96NAhUb9+fdmYAYg///zT4LgtKXPc7JlgnKdPn0ruHM6XL59eddjacdzq1atFSEiI5LzB3t5eDBw4UOd5UJRKpdi8ebPskLjv69u+fbtBMaempooWLVrI1lunTh29Rsg4e/asxt/bRo0aGTRErtzx1aZNmyTfVb6+vmLOnDk6z+sWEREhJk6cqHGYP2OvwS1ZskTjd2CDBg3EoUOHdK4rNDRU9OzZU1JPwYIFxZMnTyTlhh6HZ/drTnI9ExYuXCipv27duiIoKEjnes+ePSuaNm0qG6u3t7d49uyZQfG+N3LkSNm6HRwcxODBg/X6Hti7d6+oXr26bH1t2rTR+fgtu+/r7MDmkgmZx6dTKBQ6n3xkJ9OmTZN9MxUvXlwcO3bMoDqVSqWYP3++5MJrvXr1JO3kpGTCq1evZLuFBgQEGDzxb1xcnGziyt7e3uD9YwrZ9eBXk3379kku1OfLl0/nH5yoqCjJBXoHBweD90Hm127NmjWSixE1atTQe6z09xISEsSIESNkP9t58+YVT5480as+pVIp2rRpI6nL09PT4G6F778n5A4Mf/rpJ73rk0smZD6QHzNmjFFDKb1nrmRC5tcif/784uTJkwbVPWvWLNkupF27dhW//PKLpLxPnz4GzXfw+PFjUblyZUl9dnZ24uLFiwbFfuvWLZErVy5JnbVr1zZ4UstXr17JDh3l7u4ubt++rXd9mfedh4eH2uvt5uZm8ISFSUlJol+/frKf32+++cagOt/LXJ+pkwlCCNGtWzfV79Q333xj1DwHqampsr+BADROJJsVJhPeefnypWy7hnR3/uabb1TbDxgwQDx69Mio2DQdmxr6mZI7ZvH09JR8Fu7du2dQ/aGhoaJkyZKyx2qGTsocFhYmm6Swt7cX06dPF6mpqQbVe+HCBfHRRx+p1VmqVCnJ8BOGJBPu3bsne8KrUCjE+PHj9UoiZLZ//37J8GmlS5eWXPjTN5nw7Nkz1e9N4cKFxb///mvUsET379+XfS+UK1fO4OOPzBdlpkyZIqpUqSI5XtizZ49B9SuVSrFw4ULJa/n+t9zYGzAsIXPcTCYYTy7JpOscNrZ4HLdgwQLJnD+lS5cWISEhBsWbmpoqpk2bJjuEmpubm943WAghxNChQyV1OTo6irlz5xo8hO/GjRslv4cAxODBg/WuS+74KnPdrVq1kszZo6ujR4/Kzkfm6elp0M19QrxLqMol7V1dXcXixYsNqlMIIQ4ePCgKFy6sVqfcZ8qQ43BbuOaU+XfL0dFR7dqgvb29mDNnjkHv2/T0dPHtt9/KHid269ZN7/remzVrlmyd5cuXN/h1VSqVYvbs2bLvsUWLFmW5vS3s6+zAppIJr169krz4lSpVsnZYejt//rzsG7tatWoGf8lndPz48SzHG8xJyYTu3btL2qtVq5ZksjhDyH25lSpVyqgTM2Nk54NfTaZOnSqJuUGDBlmekCuVStmD199++83gWLI60OrcubN4+/atwfW/9/fff8tOKNmuXTu96vnrr78kdfj5+el195smhw8flkzO6OTkJK5du6ZXPXLJhIyP2bNnGx3re+ZKJmR85M+f3+iJRzUdaGV+DB482Kh5RF68eCE7h1CvXr30ris9PV3UqVNHUlfHjh2N/kykp6fLJtnq16+v9/PXtu88PDwMPvl8T9P3jru7u153y2Ym93tvas+fPxe1a9fWq7eSNmlpabJ34nXo0MGg+phMeHehWu6CZ4sWLQyqLyEhQdSvX9/gi5lyPv/8c0l8FStWNKiurOYRady4sdGTx9+7d0/24pkh34NpaWmyd7O5urqKnTt3GhWnEELExMSIZs2aaX1N9E0mpKamitq1a0vqcXZ2Ntn4+8+fPxfVqlXTGrchEzD//fff4rPPPjPZPBeaEkFbtmwxqL7MF2UyHzMGBASYZBLFU6dOyd79XahQIYPHeLaUzDEzmWC8jEni9w9dJum11eO4zJ+runXrmuQcfvv27bKJusqVK+s13v+ePXskdbi5uel1V7cmoaGhsvMZ6DsXqNzxVebfQ0MT4e+dPn1a9ka0CRMm6F1XbGys7F3aefLkMckx7NOnT7Mc496Q43BbuOaU+Xcr48OY3jkZyfUiUCgUBt2Mee7cOdnroi1atBAxMTFGx7pt2za19+1nn32m0++qLezr7MCmkgknTpyQvPCDBg2ydlh6USqVkrtagHcZeF27HeoiODhYODk5afwyySnJBLkfeH9/f5O+ll9//bWkjR9++MFk9esjOx/8apKeni57d/3XX3+tdbuff/5Zsk3Xrl2NikXbQUXTpk1NOpnUnDlzZNv5999/ddr+5cuXkgNsZ2dng+8KlrNjxw7JXfRNmzbVqw5tyQRDLi5oY+5kgr29vUle39TUVNleAxkf9erVM/rAXggh/vvvP0ndjo6Oeh/syCWuatWqZZLkmhDvfvve3zmf8bFq1Sq96tGWTDDVhbOoqCjZJI3cZNq6ylyXOZIJ5nDjxg1JYtTV1dVk3fA/lGTC3bt3xfDhw2VPmMqUKWPSYxZjRUREyN4tqeuQbxlpSyaUKFHCqARdRuvXr5fU7+DgoHfP5T/++ENSj52dnU4X8nQVHx+vcUg1QP9kwuLFi2VjNsUFg4wiIiIkPSvM+XtvqIkTJ0piGzhwoEF1absoU6BAAZ2HONDFqVOnZIepGj58uMnaMIfM8TKZYLy1a9dKYpsyZUqW2+WE47jy5cubLLkohBBbtmyR7S2s641OSUlJkoveCoXC4ASlnHPnzkk++6VLl9arR5W2ZELNmjWNmtw5I7lz89y5c+vdq2z06NGSelxdXVWTRpvC8+fPtb7X9D0Ot5VrTtp+t4w5j8koOTlZdjixL7/8Uq960tPTRYUKFWS/t0x5YX3RokWicOHCOt94Yyv7OjuwqWTCypUrDf4xyC62b98ueQ6Ojo4Gd8fWRm5IjfePnJJMyDyus0KhMHh4Ek1SU1MlJ365c+c2+m46Q8i9tm3bthXPnz8328MUX5yRkZGiePHiktg1naAfOXJE0j01ICBAxMbGGhWHps9D3rx5zXIxp0ePHpK2KlasqNNdPHLjwP/8888mj3HUqFGSdvQ5mNOUTMiVK5fJLhS9Z+5kwsiRI00W65o1azS+3xwcHERoaKhJ2lEqlaJcuXKSNvQZMiUlJUXSldPNzc3goUc0iY6OlrRTrlw5ve5q07TvDLkLWZvp06dL2mjWrJnB9Rl7EmNN7du3l8RvSHdcW0kmGPN7+ejRI3Ht2jVx8uRJsXz5cjFmzBiticXq1auLFy9eWPU1kCN315m+F4yE0J5M2Ldvn8niVSqVknkYgHdjcesqKSlJdi4aY+dfkPPgwQPh7u4u+7rok0xITk6W7YY/ceJEk8csxLs7/+Xu9AWyTzLh1atXkouHJUuWNKguTRdlFAqFCA4ONnHkQnZca2dnZ72HyLSkzPEymWC88+fPS2IbMmSI1m1ywnGci4uLXvNF6krufCpv3rw6zYUl95nMal8Y4vfff5e0o89wtpqSCY6OjiY73xDi3TAtcqNfHD9+XOc6Xrx4Ifs7Yo45UENCQmRHCjDkONxWrjlp+t2qW7euUb3hM5M7z/X399erDrnEqbe3t3j69KnJ4nxPn7nvbGVfZwc2lUyQu2PIkJMba2rYsKHkORjSPUwXaWlpolKlSrJfKDkhmXDs2DFJO/369TN5O5raWrp0qVna0iarIQPM8TDVUGLnz5+XzOeRK1cucefOHbX1nj17Jrkr2N3dXYSFhRkdg6bnuGbNGqPrlvP69Wvh7e0taS+rcXDj4+Mld4aWLFnSJHeyZxYTEyM5MOzTp4/O22tKJpjjO8acyQQHBweTHrykpKTI7nvAuHEl5fz666+SNoYNG6bz9qtWrZJs//3335s0Rm1tHTx4UOft5fadnZ2d5HvEWBEREZITECcnJ4M/g5ljtqVkgtyx1++//653PbaSTLDEw93dXUydOtWkveFMSa7H06hRo/SuR9MxS4MGDUwes1xvwAEDBui8/fLlyyXblyhRwmR39WYm970N6JdM2Lp1q2T74sWLG9RzSFc//PCDbNzZJZkghJBN4Blyc4OmizLm6hWvVCpFgwYNJO2NHz/eLO2ZQuZYmUww3t27dyWx9ejRQ+s2tn4cB0BMnz7dLPEmJSVJ5twEIBYuXJjltpm38/LyMsvQY2lpaaJ06dJqbdWvX1/n7TUlE/r27WvyWL/88ktJO9OmTdN5+++//16yvakvdGekaWJffY7Dbemak6bfLX0ms9ZFamqq7PB8+szdJdcrQZf5DMzJlvZ1dmAHG5KQkCAp8/DwsEIkhnnx4gVOnDihVubp6Ymvv/7aLO3Z29tj0qRJZqk7O/j3338lZePGjTNLWw0aNEDdunXVyrZu3WqWtnKqatWq4c8//1Qri4mJQdeuXfH27VsAQFpaGnr06IGXL1+qrbd06VJ89NFHZokrICAAvXr1MkvdefLkwRdffCEpl3vvZrRv3z7ExcWplY0ZMwYODg4mjQ8AvLy8MGzYMLWyXbt2ITU11ah6R4wYYdT2lta2bVsULFjQZPU5OjqiVq1asv/73//+Z7J2AKB+/fqSslu3bum8feb3o6urq+z71hQ+/fRTFC1aVK3M2O/SZs2aoVSpUkbVkVnu3LlRoUIFtbKUlBTcu3fPpO3YgsqVK0vKXr9+bflAcpAFCxZgwoQJcHR0tHYossy9z4cOHWqyut5r1KiRpOzGjRs6by/3uzxhwgS4uLgYFZcmw4YNQ548eYyqY+PGjZKyCRMmwMnJyah6tfnqq6/g7e1ttvpNwZzvXwcHB0yePNkkdWWmUCgwdepUSXlWx4yUs+TKlUtSFh0drXUbWz+O8/HxwZdffmlUHZo4Oztj4sSJkvKsPlcXLlyQHPMNHjzYLN9/9vb2+Oabb9TKTp06JTkf1pet/NZOnToVCoXCqLg0+fbbb43+TbT1a05lypRB06ZNjaojMwcHB9SrV09Srut74dq1a7h27ZpaWbFixfD555+bJD5D2fq+tjSbSibYup07d0KpVKqVdevWDb6+vmZr85NPPjFb3da2fft2teXKlSujfPnyZmsv8wXnQ4cOIS0tzWzt5USDBg2S/EhcuXJFdeF5woQJOH78uNr/R40ahZ49e5otpsGDB8POznxfhcOGDZMcIO3YsQNCCI3bZH5v29vbmy3hAUjf29HR0QgJCTG4vpIlS6JEiRLGhmVRrVu3NnmdNWvWlJS5urqiYcOGJm2nSpUqkouSul70TkxMxMGDB9XKWrVqZfRFLk3s7e3RrVs3tbJ9+/YZVWe7du2M2l6TOnXqSMru3r1rlrays9y5c0vKIiMjrRBJzvHZZ58hX758GDlyJMLDw60djoQ597m9vb1Zvm8DAwPh5uamVqbr5zUhIQGHDx9WK3N3d0efPn1MFl9mbm5uRtefOWYnJyd0797dqDqz4uHhke3PLcz5/m3Tpg2KFStmkrrkNG3aFGXLllUre/DgAa5evWq2Nsm25YTjuD59+pj1BtE+ffpIkjTHjh1DVFSUxm0yn4u9r8dcunbtqnYsr1QqceDAAYPry507N2rXrm2K0NQYc2x8584d3Lx5U62sZMmSaNGihUlik1OoUCG0bdvWqDps/ZqTsc9fE2PeC3IXzQcOHGiWGyf1Yev72tJsKpng7u4uKYuPj7dCJIY5d+6cpKxTp05mbdPe3h6dO3c2axvW8OLFCzx+/FitrE2bNmZtM3MmPjk5WfKDSFlbsGABqlSpola2YsUKtG/fHr/++qtaed26dSVlpmbuz2CBAgUkP7aRkZG4f/++xm3Onj2rtlyzZk2zJh0rVKggOfm+cuWKwfVlzrLbArkL/8YqUKCApKxSpUpwdnY2aTtOTk7w8fFRK4uJidFp26tXryIpKUmtzNLfpQ8ePJD0xNGH3MGsKcj1VNH1dc1J5E7ws7pLkrIWHR2NBQsWoHTp0pgxYwbS09OtHZKKOfd5mTJlzPJ7Zm9vj3z58qmV6fp5vXLlClJSUtTKWrVqBVdXV5PFJ+fjjz82eNvHjx9L7ravWbOmRXoNmCMZZErmfP+a+5gRkL8RTO4cknImue8tbZ/rnHAcZ+7PlZOTk+TGk/T0dFy8eFHjNpnPxQoUKIBKlSqZJT7gXe+MihUrqpUZcy5Ws2ZNs9wsly9fPkm9uv7WWuNaGGDcb21OuOaUHc+Tzpw5IymzxHtBm5ywry3NppIJmS+WAO92uq0IDQ2VlGkaBsOUTN2tKTuQu0NHrluzKZUrV06SLQ0LCzNrm7ro378/xLv5T8zyuHz5sknjdXFxwZYtWySf5927d6st582bF//++6/Zh4EoWbKkWesH5D/nmu4ye/v2rSSrb+73NgDJkC7GvLfNmcE3lzJlypi8TrnfLHO0I9eWrol2a3yXZj5ZEkLg+vXrBteX+S5OU5Hbf8acLGcHSqUSCQkJiIiIwIsXL3R+ZJadLnybmrG/mUqlEvHx8Xjx4gVu3bqF3bt3Y86cOejWrZvs8BUpKSmYPHkyOnbsqBryz9TPJzExEZGRkdlin5vr8wpIP7OpqalITk7OcjtrHZ9Xr17d4G3l7v6zxLGCJdsB3n0+YmJi8PLly2zx/rXE+0KfY0bKeeQSX1klEzKzteM4c9zQk5m+n6vM/7PE917m19WYczFz/dYqFArJ+1HXY2Nr/dbWqFHD4G1zwjWn7HielDlR5u7uLrkWYWk5YV9bmnX7kejJ399fUnb79m0rRGKYzJmuPHnyIG/evGZv1xYv7GVFbliAHj16oEePHhaNg0M9GKZEiRJYvXo1OnToIDvcj729PTZs2IBChQqZNQ57e3uz1v+e3I9j5u+D954+fSo56V24cCEWLlxoltg0Mea9bc5eFObg5OQET09Pk9crd3ekubqdZ25L1y6Sct+lljiwz8zQ95ujoyO8vLxMHM07cu8JY+cSsaTExETs3r0bx48fx/nz5/Hw4UO8ePFC6xBrZDyFQgF3d3e4u7sjX758KFOmDNq2bYvRo0cjLS0NO3fuxPTp0yV3RO7Zswc9evTA9u3bDR47ODU1FQcOHEBwcDDOnj2LBw8e4NmzZ9kq+WOu70BA82c2q95gcr/Hljh2NmZYD7kLjvnz5zciGt3J9bozhQcPHmDnzp04deoUrl+/jkePHiE2NtYsbRkqICDA7G3oc8xIOY/cmOOZe11lZOvHcYBl5sDU53OVlpaG58+fq5Xt3bvXbOP6a2LMa2ru39o3b96olnU9NrbWb60x39s54ZqTud4Lhp4npaWl4enTp2pl5cqVs/jnK7OcsK8tzaaSCXJ3dNpSt8/MB8SWuuBmaxf2dJFdhprIbic5tqRdu3b45ptvMHv2bMn/ZsyYgSZNmpg9BrlxdS3Vjqb3cE54b8vdqZCdyQ2hlxPa0oWtv98scQJqa16/fo0ZM2Zg+fLlNt+TIqdxcHBA586d0blzZ8yePRsTJkxQm0tr586dmDt3LkaPHq1XvXFxcfjtt9+wYMECREREmDhq08qOn1m575/sfuyckJAgKbPUa2vq37ETJ05gypQpOHLkSLZPdlpiPGd9jhkp55HrEa7t7tjs8t7I7ufEH9q5GH9r/48x39t8L5hedj3mygn72tJsapgjPz8/Se+Eq1evZvsTp/cSExPVljNPFGcu2e3ilSmYYygAQ2Qeo5L0I3cyDEB2KAhzMPd4yO/JfQY1Pfec8N621OtqKuacgNuabenC1t9v2e31tLatW7ciICAAc+fOZSIhmxs3bhyWLFkiKZ82bZrWSSEzO3bsGD766CNMmzbNJo6Hs+NnNvPxOWC5Y3RD6XNcYWpyr5chkpKSMHToUDRo0ACHDx/O9okES7HmviXrO3nypKRMWzLB1o/jLOVDOxfjb61p8L1getn1fZAT9rWl2VTPBABo0qQJ7t27p1oWQmDTpk0YNmyYFaPSjZubm9rJvakOxrOSEw9A5Q4Irl27liOHdMqp/v33XyxYsED2f6NHj0a1atWMGuNQF0+ePDFr/e/pcweh3Hv7zz//xIgRI0weF5Hc+y0+Pj5HJqFzusWLF2Po0KG8IGdDPv/8c2zfvh07duxQlUVHR2PDhg06Hdfu2rULXbp0kUweTPqRO4m11DG6oeTGUH/58qVF2jZFO0lJSWjfvj0OHTpkgohyFmv2OiHrCg8Pl0xMmi9fPpQoUULjNjyO042x52Jff/01fvnlF5PH9SHR9FtrqZsIDcFrTqYn95pmh2Mu7mv9Za80lQ7kZvletmyZ5QMxQOYvSkuNh2VL427pSu5HR587+ci6bt++jUGDBmn8f3JyMrp27Wr2uywtNY603GdQ0zjvfG+TJfH9ljOcPXsWI0aMkE0k1KpVC7NmzcL+/fvx8OFDREVFITU1VecJhR88eGCFZ/ThGDlypKTsv//+y3K7hw8fonfv3rKJhAoVKmDq1KnYtWsX7t69i8jISCQnJ+s1kfSHRO57MLsfO5cqVUpSlnlCQ3ORG4ZFX+PGjZNNJLi7u6NHjx5YsWIFzpw5gxcvXiAuLg5KpVLn9+6UKVOMjk8TXecjMoY+x4yUs2zYsEHy/du1a1et2/A4Tjf6fK68vLwk47fzNTWetX5rjZnvjJ8v05P7fGWcg8NauK/1Z3PJhFatWkkmIbpw4QIOHDhgpYh0V7RoUbXlyMhIvHjxwuztXr9+3extWJrcHRpyE1ZR9pOYmIguXbpIhuBo2rSp2nJ4eDg+/fRTtfGkbVVYWJikLPP3wXuFCxeGo6OjWhnf22Qu/C7NGb766ivJRa7ChQsjKCgIISEhGDduHFq2bIlixYrB29vbIuN+k26aNm0quTsy8+TMciZOnCgZV9Xb2xvr169HaGgopkyZgnbt2sHf3x++vr5wcnIyadw5idzv8bVr18zerjFDkRUpUgR+fn5qZWfOnEF8fLyxYWUpKCjIqO1v3rwp2zO1U6dOuHv3LjZs2IDPPvsMNWvWRL58+eDh4WH1iRnfu337ttnb0OeYkXKOpKQk/PHHH5LyrCb/zAnHcZb43tLnc2Vvb48iRYqoldnaa5odWeu39tatWwZvmxM+X9mNps+XtW9k4b7Wn80lExwcHDB48GBJ+cSJE7P9RcdKlSpJykJCQsze7tmzZ83ehqXJjR2ZE59nTjR06FDJgcPnn3+OQ4cOSXorBAUFYfLkyWaN5/79+2atH4CkyzIAVKxYUXZdJycnlCtXTq2M720yF36X2r7Lly/j1KlTamW+vr4IDg5G8+bNrRQV6cre3h6FChVSK4uIiNA6RGVERAQ2bdqkVubk5IR9+/ahZ8+eZokzJ7PW8fm5c+eM2j7zTRhJSUnYunWrUXVmJSkpCatXrzaqjr///ltyzta5c2ds2bIF+fPnN6puc7PE+0KfY0bKOf7++288f/5craxMmTKoX7++1u1ywnGcJeLV93OV+XW9cuUKkpOTTR3WB8UWr4XlhM9XdpT5dU1ISJBN+FkS97X+bC6ZALwbT93T01Ot7MKFC5g3b56VItJNzZo1JWW6dGU3RlpaGnbv3m3WNqzBx8cHZcuWVSvbvn27xYatIcMsWrRIchIaGBiIP//8EwAwf/58yYHGzJkz1caTNrXt27ebrW7g3djCp0+fVivLnTs3SpYsqXGbunXrqi3fvXsXoaGhZomPPmwVKlSQdPM298UoMq09e/ZIyn788Uf4+/tbIRoyhK+vr6QsJiZG4/oHDhyQ9ET58ssvUatWLZPH9iGoWLEiXFxc1MqCgoLMPoavsecAcncs//zzz2a9ueqvv/4yei62zN9Zbm5uWLRoUbabJFKOuY8ZAWDbtm2SMnPPIUbW9fDhQ3z//feS8ilTpmTZKycnHMeZ+3OVmpoquR5ib2+PKlWqaNwm87lYQkKCTYyEkZ3JXQuzxHeqMb+1vOZkHpk/X4D5r4tmhftaf9n/qE2Gr68vxo8fLyn/9ttvceHCBStEpJv27dvD3t5erWzz5s1mHStu06ZN2WIMMnPo0qWL2vLr169t7uDpQ3LhwgV8+eWXamVeXl7YvHkzXF1dAQAuLi7YvHmz2kGxEAL9+vXDnTt3zBLX0qVLzdqtbtGiRZIT+44dO2o9Ocj83gbe3bFEZGqOjo7o0KGDWtnFixeNvmOWLOfq1atqy3Z2dujdu7eVoiFDvHr1SlKW+Xgxo8z7HAD69u1r0pg+JK6urmjZsqVaWWJiIlatWmW2NhMSErB27Vqj6mjbti0KFy6sVnbz5k38/vvvRtWrSXh4OH788Uej6nj79i3u3r2rVta6dWvJkE3Z1d69e/HkyROz1X/s2DHJ8LQlSpRAYGCg2dok60pPT0e/fv0kw56VK1dOp55mOeE4bu3atWZN3q5fvx7R0dFqZQ0bNpRN5L/HczHTK168uOS77OHDh9i3b5/Z2gwPDze6fl5zMj25z9fy5cstMi+RNtzX+rHJZAIAfPPNN5KhQJKTk/HJJ5/g8ePHVopKuzx58ki6JCckJOCXX34xS3vp6en4+eefzVJ3dvDpp59KLshOmzbN6l9CJBUVFYVu3bpJuocuW7YMpUuXVisrVaqUZFL1mJgYdOnSxSwHmtevX8eWLVtMXi8AREdHy/aY6t69u9btmjRpgoIFC6qVLVu2DA8fPjRleEQAIHvh+bvvvrNCJB8OUyYwM98w4OfnJzuJmKEsNanrhyolJQXPnj1TK7O3t4ePj4/GbeRuEsn8W2qM8PBwk9VlK+Tu8p81axaSkpLM0t5ff/1l9M0+zs7OskNBTpo0CSdOnDCq7swSExPRo0cPyQU5fUVFRUm+/0z53gWA8+fPm7S+jFJTUzFz5kyz1T9t2jRJWVbHjGTbhgwZguPHj6uV2dnZ4e+//9a5t46tH8dFRkbir7/+MkvdqampstdDsvpclSpVSnIn/Z49eyS9zUk/cr+106ZNM9uNfbNmzTJqAmaA15zMQe7z9eDBA/zzzz/WCej/477Wj80mE5ycnLBq1So4OzurlYeHh6N58+ZmvWsksyNHjiAiIkKndb/55htJ2e+//26Wk/Xff/89Rw+N8tFHH+Hjjz9WKwsLC8P06dOtFBHJEUKgf//+ePDggVr5F198ga5du8pu07VrV4waNUqtLDQ0VHa+FFMYPXq01iElDPXVV19Jeh5VqFABrVq10rqdvb09xo0bp1aWnJyMzz//3OqTE1HO06ZNG0lX7/3791v9gC4ned/76j1jJl7NLPOE7aZOumb+LiLT2rt3L96+fatW5u/vr3XC5Mz7HIDRQ89kZOzd57aoe/fuKFasmFqZpqFHjPXgwQPZi8aGGDhwIKpXr65WlpKSgvbt20vmUjFUfHw8OnXqZJKxrc393n3w4IHZh3dduHCh7Pjrxlq5ciUOHz6sVubk5IQvvvjC5G2R9QkhMGbMGMkNVMC76wUNGzbUua6ccBw3depUs9w09dNPP+HmzZtqZX5+fujXr1+W206cOFFS9vnnn0t+s0l3Q4cOhYeHh1pZSEgIFi5caPK2QkJCTNKbhNeczEPu8zVu3DjJ3DGmoGsygPtaPzabTACA6tWrY+7cuZLy27dvo3bt2rh8+bLZY1i9ejVatWqFJk2ayHZTz6xFixaoU6eOWllqaiq6du2qc0JCF0ePHjX7xLXZwdSpUyVDAUyfPl12vFFjjRs3jneGG2DWrFnYuXOnWlnNmjXx66+/at3ul19+kYz/vHbtWsyfP9/kMT59+hS9e/c26Zh4S5YskT2I/+6777Ic/xQABg8ejCJFiqiVHTlyBF9//bWpQlRZtWqVZB/Rh0Xu4uGIESNMfgdWamoqRo4cafTdrbYmd+7casvh4eEmm8gv8+S9cXFxuHHjhknqXr58OW7fvm2SukhKCCF7HJu5F2tmmfc5YLpJ4g4ePCh7cSunc3BwkD2x/e2330x6TJmQkICuXbua7AK6g4MD1q5dC3d3d7XymJgYNGnSBAsWLDBqDoWrV6+idu3aCAoKUpXJJQR05evrK5mfwlTvXaVSic8//9wkdWXVTs+ePfHy5UuT1Xnp0iWMHDlSUj5w4EDZzzvZtvj4eHTp0gVz5syR/K9evXr44Ycf9K7T1o/jTP3dCAD79u2TvQj49ddfw83NLcvtO3bsKJmv5ObNm+jXr5/J56Y5dOgQFi1aZNI6syNfX1/ZBOnXX39t0smYX7x4gZ49e5psP/Gak+nJfb6ioqLQtWtXkybsVq9ejfLly+t8gwX3tR5EDjBp0iQBQPJwdnYWv//+u0hPTzd5m4mJiWLo0KFq7ZUtW1Y8e/Ysy21DQ0OFk5OTJN5q1aqJly9fGh3b8ePHhbe3t6peJycn4erqqtbW2LFjjW5HkyNHjkieW//+/c3Wntz+d3Z2Fhs2bDBJ/WlpaWLYsGECgKhYsaKIi4szSb2GsPRra6zg4GBhb2+vFq+vr694+PChTts/evRI+Pr6qm3v6OgoTp48aVA8ct8TGR+9e/cWKSkpBtWd0erVq4WDg4Ok/nbt2ulVz4EDB2TjHDt2rMm+11auXCkcHByEp6enCA0N1Xv7KVOmSOLbuXOnSWLLLHfu3GrtFCtWzKB6ihUrplZP7ty5TRvo/7dz507JazNlyhSztFWtWjVJW/rq3bu3pA4fHx9x5MgRk8SYkJAgPv74YwFAtGjRQqSlpeldh6X2nRBCrFixQvJ6zJ8/36C6GjRoIKlr9+7dJolz6dKlkroHDRpkdL2HDh0Sbm5ust9BXbp00bs+U76eppL5/WTpw+LZs2fLvr6nTp3Sut3Bgwcl2zRv3tzoeK5evSr8/PxkY6pWrZre9ckds5jz+LNRo0aS9vQ5ZktPTxf169eX1OHq6mqS37WYmBjRtGlTtbpz5cplku+0PXv2CEdHR9l9V6NGDbFr1y6hVCp1ru/Bgwfiiy++kBzLeHh4iC1bthj1nSD3Gh8/ftyQp62iVCpVx+pyD0P2n9z7KeOjUqVKOp33ZeXSpUsif/78kvoLFSokoqKijK7fnDLHbOhxmaVZ83wqODhYlC1bVvY9VblyZaP2uS0ex2V+NGvWTMTGxhoda1BQkPDw8JB9jfU517t27ZpwdnaW1NOzZ0/x9u1bo+MUQoj9+/cLDw8P4eDgIA4ePKj39pY+vjL2WDwxMVGUKlVKEnPu3LnFmTNnjI7v6dOnIjAwUOtvrSHHNLZwzcnY4yB9mOIYT9Pnq2XLlib5Hti5c6eqfjs7O/HVV1+JxMTELLezhX2dHeSIZIIQQowbN07jj1LlypVFUFCQydrasmWLKFGihGxbul4s/OWXX2S3L1GihDhx4oRBcSmVSvHnn39KPpDfffed5EJTTkomJCcnizp16si+nl9++aVRH85Hjx5JTvw6duyo1wmZKdlSMuH58+eSkyOFQiF27dqlVz27d+8WCoVCrZ6CBQuKFy9e6B2T3OctX758amUNGzYUDx480LtuIYRISkoS48aNk8QLQOTLl088ffpU7zrHjh0r+95u0aKFuH//vkFxCvHuhGDUqFGS1+P169d61cNkgma2lkx48+aN7MG9g4ODmDFjhlGJttDQUFGlShW1ekeOHKl3PbaaTJg8ebKkrjp16hh0Ip5ZZGSk7A0Ky5YtM7jO5cuXS25CyPhgMsE4qamp4ptvvpF9bXVJCqSmpspefPz+++8Njmnnzp2S79iMjw8hmSCEEHfu3FG7Ief9w97eXsyYMcPgz+zFixdF+fLl1eosXbq05HzAmO+0rVu3av3clihRQowaNUps2bJF3Lx5U0RHR4u0tDSRkJAgnj59Ko4cOSJ+/fVX0bRpU8mNIACEi4uLOHjwoHjw4IFR3wl//vmnZPvixYsbfEwTHR0tevToofF5G3pcIvd+qlu3rtpy0aJFjbpQu2rVKuHp6Sn7fjtw4IDB9VpK5riZTNDs+vXrshf73z8qVKhg9E2Ftngc5+npKflurFixorh8+bJBcaanp4vffvtNNrnq7u4url69qned8+fP1/i7aGicQrz7LZ8+fbpa0tbX11fcuXNHr3psLZkghBAnT56UPXZ1c3MTS5YsMTi2Q4cOicKFC6vV2bBhQzFmzBijj2ls4ZqTrSUThBBiwYIFGr8TL168aFBsSqVS/Prrr7LfAwsWLMhye1vY19lBjkkmCCHEwoULZe8Gfv+oUqWKWLx4sYiIiNC77tevX4u///5b8mOX8VGpUiW9DgL+97//ydZjZ2cnPvvsM3H79m2d69q7d6+oXbu2pK4WLVqI1NTUHJ1MEEKIiIgIjXd5FCpUSPz+++8iJiZG5/qePn0qxo8fL9zd3WV/5G7dumXGZ6OZrSQT0tLSZH/MJkyYYFB9EyZMkNTVqFEjvU/q5Q4CT548KUnAubu7i0mTJul8YT0tLU2sXbtWlClTRvY96OHhIc6fP2/IUxdKpVLjSbKrq6v46quvdO7pIYQQ8fHxYuHChaJIkSKyda5evVqv+JhM0MzWkglCCHHv3j3Zi5QARJkyZcTSpUtFUlKSzvXduXNHDBkyRPZgzs/PT0RGRuoVn60mEy5duiT7mg4YMEDryX1KSoqYNGmSCAsL01r/l19+KVv/sGHD9EoQnjx5UrRs2VKtjho1akju6GIywTDR0dFi8eLFomTJkrL7y8vLS9y9e1enuubMmSNbR7du3cSjR490jik0NFTyG1OsWDFRunRpye+lvmwxmSCEEIcPH5a9yAG8O9bfunWrzid9Dx8+FMOGDZOcn3h6eopr165JLlIZ+512/vx5UbRoUdnYjXkULFhQnD17VgghjE4mxMfHi4IFC0rqyJs3r1i5cqXOr21SUpJYuHCh5MJR8+bNTXJcIvd+evDggeTzq1AoRO/evbP8ns7o+PHjsnG+fyxatEjveK0hc9xMJqiLjo4W//77r2jTpo3sjUYZPz+muuBni8dxt2/flu2JPmLECL1+z3bt2iWqV68u+9wdHByM6hGq6QYAe3t7MXDgQHHt2jWd60pOThbr1q0T5cqVk61z+vTpesVmi8kEIYRYs2aNxs9Fw4YNxeHDh3Wu69q1a6JXr16SegoVKiSePXsmuTnPkGMaIbL/NSdbTCYIIcT48eM1fm6HDBki7t27p1M9SqVS7N27V+P3QPPmzXUe3SG77+vswAE5yNChQ1G5cmX07dsXd+/elfz/0qVLGDx4MIYPH44aNWqgbt26qFixIkqWLIm8efPCzc0NCoUCiYmJePXqFR48eIArV67g9OnTOHPmjNbx1Hv37o2FCxfC09NT53jfTzSzZMkStXKlUol//vkHK1euRO3atdG2bVtUrVoVxYsXh5eXF1JTUxEVFYWbN2/izJkz2L59Ox49eiSpv1q1avj333/h4JCjdrOs3Llz48iRI+jYsSPOnTun9r+nT59izJgxmDhxIpo0aYImTZqgQoUKKFq0KDw9PaFQKBAbG4sHDx7g8uXLOHz4MI4ePSo7xp67uzu2bduGMmXKWOqpZent27d48eKFxdpzcnKCr6+v1nUmTpyIo0ePqpU1atTI4Ekdf/zxR5w6dUqtzqNHj2L8+PFZzr2Qlbp162LdunXo1asXUlJSALwbt3PGjBn45Zdf0LRpU7Ro0QKVKlVCoUKF4OnpiaSkJLx58wZhYWE4deoUtm/frnHOlNy5c2Pbtm2oVq2aQfEpFAqsXr0a7u7uWL58udr/3r59izlz5mDu3LmoUaMGWrZsiUqVKsHf3x+5cuWCk5MTEhIS8OTJE4SFheHo0aPYt2+fxvFIZ8yYgT59+hgUJ+UMJUuWRHBwMNq3by/5Hb19+zYGDRqEr776Ci1btkSDBg1QoUIFFCxYEJ6enlAqlYiOjsb9+/dx/vx5HDx4UOMklX5+fggKCsryuySnqFy5Mtq2bYs9e/aola9YsQLHjx/H8OHDUb9+feTLlw8pKSkIDw/H0aNHsWzZMjx9+hT379/HunXrNNb/ww8/YPfu3ZJ9tnDhQqxYsQJt27ZF06ZNUbFiReTLlw8eHh5ITk5GdHQ0bt++jYsXL2LHjh24deuW2va+vr5Ys2YN6tata7oXI5sz9vdUCIG3b98iISEBMTExuHv3Lm7fvo1Tp07h5MmTGieBc3FxwZYtW+Dv769TOyNHjsSmTZskY8Bu2rQJ27ZtQ7NmzdCqVSsEBgaqPqNpaWmIiYnBvXv3cPnyZezatQuXLl2SxLFmzRqMHj3aoOefEzRp0gTbtm1Djx49EB8fr/a/K1eu4JNPPkGhQoXQqVMn1K5dGx999BF8fHzg6OiI2NhYhIeH49KlS9i7dy9OnjwpOZ50c3PD5s2bUb58eRw5csSksVerVg3Xrl3DlClTMH/+fJ0nHdSmb9+++O233+Dn56dxHV3mgnrP3d0dixcvRocOHSCEUJW/evUK/fv3x+TJk9GlSxfUqFEDZcuWhbe3N5ydnREfH49Xr17h2rVrOHHiBHbv3o2YmBi1ujt06ID//e9/OHjwoP5PVAd58uTBnj170KxZMzx9+hTAu8/82rVrsXbtWlSvXh3t2rVD1apVUbJkSXh5eUEIgejoaNy5cwfnzp3Djh07JBPCvmdvb485c+Zg8ODBZomfNDP0fEoIgYSEBMTFxal+U2/cuIGLFy/i9OnTWj+Dbm5umDZtGsaOHavXZ0gbWzyOK126NHbs2IF27dqpPtOpqalYsGABFi5ciPr166N169aoUqUKihYtKns95L///kN4eLhs/a6urli9ejXatm1rcIyzZ8+Gs7OzZA6G9PR0LFu2DMuWLUPFihXRpk0bVK5cGWXKlIG3tzdcXV2RmJiI58+f4/r16zh+/Dj27t2LN2/eyLYzZMgQ2fl7cqLevXsjKSkJQ4cOlXxOjh07hqZNm6JUqVL4+OOPUbNmTQQEBMDb21t17eb+/fu4ePEidu3ahQsXLkjqz5MnD3bt2oUCBQqYLOYP+ZqTOc2cORNOTk6Sa0VpaWlYtGgRFi9ejDp16qgmm39/XVSpVOLNmze4ffs2Tp48iZ07d2qcg6BGjRrYsmUL7Ox0mzaY+1oHVk1lmEl8fLyYMGGCcHFx0XgHgKkehQoVEuvWrTMq3l9++UXjHVCGPlq1aqWWhczpPRPeS0xMFAMGDDDLvi5RooRRXRlNQe61tfSjUaNGWmPcvn275C6DfPnyGT2u7LNnzyRDEgEQmzZt0rmOzNtmvCvh8OHDwsfHx6SvVWBgoF49jLIyZ84crUMYGPpwd3cX69evNygm9kzQzBZ7JrwXGRkp2rdvb5bvkKpVq+rVmyYjW+2ZIMS7O5Tz5Mlj0GtmZ2eX5R0rN2/elNyha8zD19dXXLhwQQgh/ezl5J4J1ngUKFAgy3kS5Lx48UJUqFDBZHG4urqKPXv2CCGk3ykfUs+E9y5evCj8/f1Nuq/z5Mmjtq9N3TMho/v374tRo0YJLy8vveN0cHAQ3bt3lx2/+tatW5L1e/furXd88+fP13q3tr6PVq1aiaSkJNnfXlP1THj/fgoPDxcVK1Y0+XtD36FArS3zc7DlngmWfnTq1MngYyFd2OJx3NWrVzX2nDb0Ubx4cZOMw//ehg0bTH6+CLzrifHbb78ZFJOt9kx4LygoSPYc35hHyZIlxc2bN1VtmKpnwnvZ9ZqTrfZMeG/Dhg2yQ00a+2jfvr3B89Fk132dHeiWlrEx7u7u+Omnn3D79m2MGDEC7u7uJm+jQIECmDFjBm7fvo1evXoZVdfXX3+Nixcvol69ekbH5e7ujnnz5mHv3r3w8PAwuj5b4+rqiuXLl+PQoUMoV66cSep0cnLCmDFjEBoaikqVKpmkzpzq/v376N+/v9qdZvb29li/fr3RdwUUKFAA69atk2STP//8c413eOmjSZMmCAsLw8cff2x0XW5ubvjxxx9x/vx5lC5d2uj63hs9ejRCQ0PRpk0bk9XZvn17hIWFoWfPniark2yfr68vdu7ciY0bN6JIkSImqdPDwwM//fQTzpw5g2LFipmkTltSrFgx/Pfff8iTJ4/e2yqVShw6dEjrOgEBATh9+jSaNGliaIgqderUwaVLl1C1alWj6yLNHB0dMWTIEISFhaFOnTp6b58vXz4cP34c3bp1MzqWsmXL4syZMyb9fbF1VapUwdWrVzF27Fg4OTkZXd8nn3xi8L42RIkSJTB37ly8evUK+/btw+jRo9G4cWPkzZtXche0m5sbqlSpgn79+mHlypV4/vw5Nm7ciJo1a0rqjYqKkpS5urrqHd/IkSOxbds2g74TM7Kzs8OkSZOwe/duODs7G1WXrooUKYJz587h22+/NboHuEKhQO/evXH9+nW0a9fORBFSdmRvb4+uXbvi5MmT2LZtm1mPhWzxOK5ixYoIDQ3FwIEDje6p4ejoiFGjRuHatWuy32OG6tGjB27cuIHevXvrfIdzVurVq4cLFy5gzJgxJqnP1jRv3hzXr1/HZ599ZvRrqlAoMGTIEFy+fBkBAQEmilCK15zM4/3nq1evXibpreXr64t58+Zhx44d8Pb2NqgO7mstrJ3NsISoqCjx119/iSZNmshOKKbrw8PDQ3Tp0kVs3LjRqAmMtAkKChItW7bUO04fHx8xfvx4jXM2fCg9EzJSKpVi27ZtBu/3fPnyibFjx4rHjx9bPHZNssOdNJp6JiQlJYmqVatK1td33Mes/Pjjj5I2ypYtq1PWPfN2mu5KOH78uGjbtq2ws7PT67XJnz+/XnMtGOPChQvi008/lR13L6uHm5ub6NWrl2r8Y2OwZ4JmttwzIaOUlBSxcuVKUbNmTYPuIi1evLiYOnWq3uPqyrHlngnvhYeHi44dO+r8WhYrVkxs2LBB5/qVSqXYuHGjqFy5st77qly5cmLt2rWS8UTZM8G0j1KlSonJkyfrNRZ0Vvbv3y/q16+vdyxFixYVCxYsEMnJyWr1sWeCusePH4vRo0dLxvXO6mFnZyfatm0rTpw4IVuvOXsmaJOeni5iYmJERESEZN9nZe/evZLnaeicWEK8u4P622+/1fu1dXBwEF26dJHMVWCJngkZ3bt3TwwePFi4ubnpFb+7u7vo16+fXnMtZDdyv1e2wJLnU/b29qJu3bpixowZJv3O14ctHsdduXJF9OzZU3auBm0PHx8fMXLkSIu81rdv3xZDhw41qKeCo6Oj6NChgwgKCjI6DlvvmZDRjRs3xIABA4SHh4der6eTk5Po1auXCA0Nla3X1D0TMspO15xsvWdCRjdu3BADBw40qHdl0aJFxY8//ijevHljsniEyF77OjvI+YPpA/D29sawYcMwbNgwREdH4/Tp0wgJCcGNGzfw4MEDPH36FPHx8UhMTISdnR3c3d2RK1cuFClSBCVKlEDlypVRq1YtVK9e3ex3vDRv3hzNmzdHZGQkdu3ahZCQEFy9ehWPHj1CTEwM3r59C3d3d3h7e6NMmTKoVKkSWrRogaZNm8LR0dGssdkahUKBTp06oVOnToiMjMTevXtx7tw5XLt2Dffv30dMTAzi4uJgZ2cHDw8P5M+fH6VLl0a1atXQqFEj1KtXD/b29tZ+Gjbjiy++wMWLF9XKWrdubfJxHydNmoSTJ09i3759qrKbN29iwIAB2LRpk0naqF+/Pnbv3o2XL19ix44dOHXqFK5evYonT54gNjYWSqUSnp6eyJ07N8qVK4fKlSujRYsWqFu3rsnGPM1K1apVsXbtWiQnJ+Pw4cM4fvw4rl27hlu3biEqKgqxsbFIS0uDu7s78uTJA39/fwQGBqJBgwZo1qzZB9lziQzj6OiIfv36oV+/fnj27Bn27NmDCxcuICwsDI8ePUJsbCzi4uLg4OAAT09PFCpUCKVLl0aNGjXQpEkTVK9e3WKfC1tQpEgRbN++HdevX8fmzZtx/Phx3L59G2/evEFycjJ8fHxQpEgR1KpVC61atUK7du30+i1SKBTo3r07unfvjsuXL2P//v04ffo0bt++jWfPniEhIQF2dnbw9PREnjx58NFHH6Fq1aro0KGDbd8dk404ODjAyckJ3t7e8PPzQ5EiRVC6dGlUqVIFdevW1XleBH20bNkSLVu2xJ07d7Bnzx6cPHkSt27dwpMnTxAfH6/63fL19UXZsmVV83jUqVOHn08dFC5cGHPmzMEvv/yCo0eP4vDhw7hy5Qpu3ryp+s11dHSEl5cXihQpgooVK6Ju3bro2LEj8ubNa+3wJezs7ODl5WXQto8fP5aUFSpUyOBYfH198fPPP2Pq1KnYv38/goODcfHiRdy/fx9v3rzB27dv4eLiInltO3XqhNy5cxvcrqmULFkSixYtwpw5c7Bv3z4cOXIEV65cwf379xEdHY2kpCR4enrC29sbpUqVQmBgIBo2bIiWLVsa1KODsh8XFxd4enqqzg0CAgJQrlw5lC9fHg0bNoSPj49V47PF47jAwECsX78eMTEx2LVrF44dO4YrV66oromkpqbC09MTPj4+KFu2LCpVqqQau9xS80WWLl0aCxcuxPz583HixAkEBwfj2rVruHHjBiIiIhAXF4eUlBS4ubnB19cXJUuWRPny5VG/fn20aNHig5k3TB9ly5bF8uXL8ddffyEoKEi13+/cuYPo6GjEx8fDyckJuXLlQrFixVCpUiU0aNAA7du3R65cuawSM685mUfZsmWxdOlSLFy4UDXnQGhoKG7duoU3b94gLi4OAFSvaUBAAKpWrYoWLVqgRo0aJus5lBH3tTqFEBnGIyEiysEyHwhXq1YN58+ft1I0RERERLbl888/x4oVK9TKDh06hKZNm1opIvNo3Lgxjh49qlYWFxfHmzGIjFC8eHE8evRItZw7d25ERERYMSIiIjJEjpwzgYiIiIiIiEzr9OnTassKhQKVK1e2TjBEREREZHFMJhAREREREZFWZ8+exc2bN9XKKlWqxOE6iIiIiD4gTCYQERERERHZMEuMXPvbb79Jyjp27Gj2domIiIgo+2AygYiIiIiIyEaFhISgcePGeP36tdnaCAoKwubNm9XKFAoFBgwYYLY2iYiIiCj7YTKBiIiIiIjIBqWkpGDQoEE4duwYKlWqhJ07d5q8jYcPH+LTTz+FUqlUK+/ZsyeKFy9u8vaIiIiIKPtiMoGIiIiIiMgG/fzzzwgLCwMAPH/+HB07dkTbtm1x+fJlk9QfFBSEmjVrIiIiQq3c3d0dM2bMMEkbRERERGQ7mEwgIiIiIiKyMdHR0Zg9e7akfO/evahSpQoaN26MNWvWICYmRu+6z507hz59+qB169aywyfNnz8fJUqUMChuIiIiIrJdDtYOgIiIiIiIiPTj7e2Nbdu2oWfPnoiKipL8/+jRozh69CicnJxQq1Yt1KlTBxUrVkSJEiXg5+cHNzc3pKWlITo6GtHR0Xjw4AFCQkJw8uRJVW8HOVOmTOFcCUREREQfKCYTiIiIiIiIbFDLli1x/fp1fPnll/j3339l10lJScHx48dx/Phxo9pycnLCH3/8gWHDhhlVDxERERHZLg5zREREREREZKPy58+PjRs34uzZs+jYsSPs7Ex/itewYUNcuHCBiQQiIiKiDxyTCURERERERDauRo0a2L59O8LDwzFr1izUqVMH9vb2Btfn7OyMbt264dChQzh69CgqVKhgwmiJiIiIyBZxmCMiIiIiIqIcolChQhg3bhzGjRuHmJgYnDx5EpcuXcK1a9fw6NEjPHnyBDExMXj79i3S09Ph7OwMLy8vFChQAMWLF0fFihVRp04dNGrUCG5ubtZ+OkRERESUjSiEEMLaQRARERERERERERERUfbFYY6IiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrJhOIiIiIiIiIiIiIiEgrB2sHQESalS1bFk+fPlUrc3NzQ8mSJa0UERERERERERGR8e7fv4/ExES1skKFCuHmzZtWioiIsqIQQghrB0FE8jw9PREfH2/tMIiIiIiIiIiIzM7DwwNxcXHWDoOINOAwR0REREREREREREREpBWTCUREREREREREREREpBWTCUREREREREREREREpBUnYCbKxtzc3CRzJnh4eKBChQpWioiIiIiIiIiIyHjXrl2TXPNwc3OzUjREpAsmE4iysZIlS+LVq1dqZRUqVMDp06etFBERERERERERkfHq1KmDkJAQtbKSJUtaKRoi0gWHOSIiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq2YTCAiIiIiIiIiIiIiIq0crB0AERERERGZXnp6OuLi4pCYmIj09HQolUoIIawdFhEREWUTCoUCdnZ2sLe3h5ubGzw9PWFvb2/tsIgoG2MygYiIiIgoh3ifQIiNjUVCQoK1wyEiIiIbERMTg+fPn8Pd3R1eXl5MLBCRLCYTiIiIiIhygKSkJISHhyM9Pd3aoRAREZGNSkhIQEJCAl69eoWiRYvCxcXF2iERUTbCOROIiIiIiGwcEwlERERkSunp6QgPD0dSUpK1QyGibITJBCIiIiIiG8ZEAhEREZkDEwpElBmHOSIiIiIislHvT/KzSiQ4ODjA3t4edna8l4iIiIjeUSqVSE9PR1pamsZ13h9r+Pv7cw4FImIygYiIiIjIVsXFxWlMJDg7O8PLywteXl5wcnKycGRERERkK1JSUhAbG4vY2FgkJydL/p+eno64uDh4e3tbPjgiylZ4axIRERERkY2KjY2VLXd3d0fx4sWRJ08eJhKIiIhIKycnJ+TJkwfFixeHu7u77DqajjmI6MPCZAIRERERkQ1KT09HQkKCpNzZ2RmFCxfmkEZERESkFzs7OxQuXBjOzs6S/yUkJHB+JiJiMoGIiIiIyBbFxcXJlnt5eTGRQERERAaxs7ODl5eX7P80HXsQ0YeDZxlERERERDYoMTFRtlzTBQAiIiIiXWg6ltB07EFEHw4mE4iIiIiIbJDcUAMODg6cI4GIiIiM4uTkBAcHB0m5Uqm0QjRElJ0wmUBEREREZIPkTujt7e2tEAkRERHlNHLHFJwzgYiYTCAiIiIiskFCCEkZ50ogIiIiU5A7ppA79iCiDwvPNoiIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiIiIiIiISCsmE4iIiIiIiOiD9PDhQygUCtVj5syZ1g4pR0hPT8dHH32kel1HjRpl7ZDIxI4fP67av/b29rh06ZK1QyIiIgtgMoGILC7lyVO8DQ21dhhERERERGQGy5Ytw40bNwAAbm5umDhxopUjIlNr0KABWrVqBQBQKpUYO3aslSMiIiJLYDKBiCwq9elThPfrh/ABn+Pt5cvWDoeIiIjogxMcHKx2N76+Dzc3N+TPnx/lypXDJ598gmnTpuHkyZMQQlj7qVE2kJKSgmnTpqmWhw8fjvz58+u07cWLF/Hbb7+hW7duqFKlCvz8/ODq6goHBwfkypULZcqUQbt27fDTTz8h1Aw3JwkhcPjwYYwePRp16tRB/vz54ezsDA8PDxQrVgxt27bFjz/+iDt37uhVb+YeMFk9WrdubfLnJmfJkiVq7fbq1Uuv7adPn676+8iRI9i7d6+pQyQiomyGyQQispjUZ8/wqF9/pD57BmV8PMIHDkIiu8MSERER2ZS3b9/i5cuXuHnzJrZt24apU6eifv36KFmyJObNm4e0tDRrh0hWtG7dOjx79gwA4ODgkOUQR3Fxcfjtt99QqlQpVKtWDV9//TU2b96My5cvIyIiAklJSUhPT0dsbCzu3LmDPXv2YNKkSQgMDETt2rVx8OBBk8T977//onTp0mjWrBnmzp2LkJAQvHz5EikpKUhISEB4eDj27t2L77//HmXKlEGrVq3MktCwlHPnzuGLL75QLVeoUAFLly7Vq47q1aujcePGquXZs2ebKjwiIsqmmEwgIotQJRKePlWVKRMS8HjgICReZEKBiIiIyNY9fPgQX375JSpXroy7d+9aOxyykj/++EP1d6dOnVCkSBGN665cuRL+/v74+uuvce/ePb3bOnPmDFq0aIGRI0ciPT3dkHARFRWFNm3aoEePHnrFcODAAVSpUgU//PCDzfXKef36Nbp06YLk5GQAgJeXF7Zu3Qp3d3e96/ryyy9VfwcHB+PChQsmi5OIiLIfB2sHQEQ5X+rz53jU/zOkPnki+Z8yMRGPBw1CkaVL4Fa1qhWiIyIiIvqwjRgxAiNHjtRpXSEEEhISEB0djZs3b+Ls2bPYuXMnoqOjVeuEhYWhfv36OHLkCMqVK2emqCk7OnPmDK5cuaJaHj58uOx6b9++xcCBA7F+/XrJ/8qWLYu2bduqhjnKmzcvACAmJgY3btzAqVOnsHXrViQmJqq2WbBgAaKiorB27Vq94n38+DGaNGkiSSLUqVMHnTp1QrVq1eDn54fk5GQ8efIEJ0+exLp16/D8+XMA7yaanjJlCsLCwrBu3TrY29vLtlOoUCHVHBKaWOqzkp6ejp49e+Lx48cAAIVCgZUrV6J06dIG1dexY0cUKVJEVd+iRYuwePFik8VLRETZC5MJRGR2b1atRur/P7iUo0xMRPig/6Ho4kVwq17dgpERERFRdpaYkoYbz2Px+M1bpKQp4eRghyK+rihXwAtuTjyVMZU8efKgbNmyem/XvHlzAEBycjIWLFiAqVOnIi4uDgDw8uVL9OnTB2fPntV4gZVynowX8wsVKoRGjRpJ1klJSUGHDh1w6NAhVZmdnR0GDBiA8ePHa72o3bhxYwwbNgx//vknxo8fj0WLFqn+t27dOtSvXx/Dhg3TKdbIyEi0aNFCLZFQsWJFLFy4EPXq1ZOsX6NGDXTu3BkzZ87EggULMGnSJCQkJAB4N0SSq6sr/vnnH9m2HB0ds/yMFStWDI8ePdIpdmN8++23OHz4sNpyp06dDK7Pzs4OPXv2xC+//AIA2Lx5M+bPnw9nZ2djQyUiomyIwxwRkdnlHTsGXu3aaV1HJCYifPAQJJ47Z6GoiIiIKDuKTUrFPycfoP3846gwZT+6LDyN0RsvY9yWqxi98TK6LDyNClP2o/384/jn5APEJqVaO+QPnrOzM8aMGYOjR4/Cx8dHVX7x4kWsXr3aipGRJaWnp2Pjxo2q5e7du8POTnrJwcnJCXXr1lUtFyhQAMeOHcPSpUt1vjs+V65c+PvvvzFz5ky18u+++w7x8fE61TFw4EDcunVLtfzJJ5/gzJkzsomEjBwcHPDll1/ixIkTahNLr1y5EkuWLNGpbWvZvHkzfv31V9Vy8+bN1SZRNtSnn36q+jsqKgp79uwxuk4iIsqemEwgIrNTODig4KyZ8GrfXut6IjER4UOGIuHsWQtFRkRERNlFaroSfx6+g9o/HcLUnddx7WkslBqGIVcK4NrTWEzdeR21fzqEPw/fQWq60rIBk0SVKlXw22+/qZVt2LDBStGQpYWEhODVq1eq5a5du2pc94cffsCiRYtQokQJhISEZHkBX5Px48ejadOmquXIyEhs3rw5y+3Wrl2L7du3q5abNWuGjRs3wtXVVee2K1eujD179qjNMzBmzBg8kRnaNTu4ceMGBgwYoFouWrQo1q9fL5vw0VflypXh7++vWt65c6fRdRIRUfbEZAIRWYQqodCxg9b1RGIiHg8ZioQzTCgQERF9KO69jkfnv07i1wO3kZii3ySqiSnp+PXAbXT+6yTuvdbtjmQyn969e8Pb21u1fPz4cesFQxa1b98+1d+5cuVCrVq1tK4/ePBg3LhxA0WLFjWq3XHjxqktZxw+SU5KSgomT56sWvb19cW6devg4KD/0GlVqlTBzz//rFqOj4/HtGnT9K7H3GJjY9G5c2dVrw1nZ2ds3rwZefLkMVkbLVu2VP29f/9+k9VLRETZC5MJRGQxCnt7FPz5Z+T6uKPW9cTbt3g8ZAgSQs5YKDIiIiKylqtPotFl4SlcexprVD3Xnsaiy8JTCH0SY6LIyBBOTk6oUaOGajkxMRGxscbtW7INBw4cUP3dpEkTnebKMMW4+o0bN1a7uz6riY7XrFmDhw8fqpZ/+OEH1STPhhgxYgTKly+vWl6xYgXCw8MNrs/UhBDo37+/2pBO8+fPV/ucmkKLFi1Ufz979gyhoaEmrZ+IiLIHJhOIyKIU9vYo8NNPyPXxx1rXE0lJeDx0KBJCQiwUGREREVnavdfx6Lf8LKITTTPvQXRiKvouP8MeClaW+W7n95PU6is9PR1BQUEYMWIEatWqBT8/Pzg5OcHDwwPFihVD+/btMWvWLKtfuE1ISMCBAwcwefJkNG3aFOXKlYOfnx8cHR3h4+MDf39/tG3bFtOnT0dYWJjR7QkhcPz4cYwfPx6NGzdG0aJF4e7uDgcHB/j6+sLf3x/NmzfHhAkTsH37diQmJprgWWr39u1bXLx4UbUsN/GyuTg7O6u952JitCcUly9frvo7b968GDhwoFHt29nZqfWOSE9Px6pVq4yq05R+/vln/Pfff6rlzz//HP/73/9M3k7mfX7q1CmTt0FERNbHZAIRWdy7hMIM5OrcWet6Iinp3ZBHPBAlIiLKcVLTlRi1/pLJEgnvRSem4ssNlziHghVFRUWp/lYoFGqTMutCCIFVq1ahTJkyaNmyJf766y+cPXsWERERSE1NRUJCAsLDw7F79258++23KF68OHr37q12t7kl3Lp1CyNHjkT+/PnRqlUrzJgxA0eOHMHNmzcRERGBtLQ0REdH4/79+9i7dy++++47VKhQAa1bt8bt27cNanPXrl2oWLEiGjZsiNmzZ+Po0aN4/PgxEhMTkZ6ejqioKNy/fx+HDh3CzJkz0alTJ+TNmxe9evXC+fPnTfwK/J+LFy8iLS1NtVytWjWztSUnPf3/hkfLOIdBZo8ePcLJkydVy7169YKLi4vR7Xfr1g0eHh6q5TVr1hhdpykEBQXhu+++Uy1Xq1YNCxYsMEtbvr6+KFGihGr5LOfBIyLKkZhMICKrUNjbo8CM6cj1ySda1xPJyXg8bDjiMxz0ExERke1bdPQewp6ZZ/iba09jsejoPbPUTdoplUpcvnxZtezv76/XxdoXL16gVatW6N+/P+7fv6/TNkIIrFu3DoGBgVi7dq2+IestMTERI0aMQLly5bBgwQLVOPS62r9/P6pWrYotW7bovI0QAqNHj0aHDh307t2QkJCADRs2oGbNmpgwYYJe2+rq3Llzqr/t7OxQpUoVs7QjJy4uDm/evFEtFyhQQOO6R44cUVvunMXNTbpydXVFmzZtVMu3bt3C06dPTVK3oR49eoRevXpBqXyXWPX19cXmzZtNkjzRJGMSKeN7goiIcg79ZxgiIjIRhZ0dCkz/EbBTIGaz5pMpkZyMJ8NHoPCCBfCoX8+CERIREZE5xCal4q9g817s/yv4HvrVLQ4vF0eztkPqdu3ahRcvXqiWO3TooPO29+7dQ4sWLfDgwQO18kaNGqFjx46oVKkS/Pz8kJaWhmfPnuHs2bPYuHGj6i7/uLg49OnTBy9fvsSYMWNM84QySUpKQtWqVdXGnweAIkWKoFGjRmjQoAGKFCmC3Llzw8XFBdHR0bhx4waOHDmCrVu3IjX1XU+chIQEfPrppzh06BDq16+fZbtTp07F3Llz1cpq166Njz/+GBUrVkTevHnh5uaG2NhYRERE4MqVKwgJCUFQUBBSUlIAvEtIZDUpsqEyjo9fokQJtbv0ze3UqVMQQqiWq1evrnHdEydOqP52cHAw6etRv359bNq0SbV87Ngx9OrVy2T16yMpKQmffPIJIiMjAbxL8Kxbtw7Fixc3a7uVKlXC5s2bAQA3b95Eenq6TnNnEBGR7WAygYisSmFnhwI//ACFQoHoTZs1rvcuoTD8XUKhQdYnXERERJR9bb3wBIkp6VmvaITElHRsu/gU/esWN2s79H+ePHmCkSNHqpadnJzwxRdf6LTtmzdv0Lp1a7VEQp06dbBgwQLZu9yrVq2K9u3bY9q0adiwYQNGjRqFiIgIAMDYsWNRuHBhdO/e3chnJOXi4oIOHTqokgkff/wxRowYgebNm0OhUMhu07BhQwwZMgRPnjxBv379VHfHp6SkqCbGdXDQfGp+69Yt/PTTT6plb29vrF27Fm3bttW4zfskTlRUFNasWYMffvgBZcuWRadOnfR9yjq5c+eO6u9SpUqZpQ1NMvdGydhDILOMvTrKly9v0rv0MycxTDE/hqGGDRumNofFtGnT0KpVK7O36+/vr/o7NTUVjx49QsmSJc3eLhERWQ6HOSIiq1PY2SH/tGnw7tZN63oiJQVPRoxA/PHjFoqMiIiIzGHzxScWaWfThccWaYeA3bt3o06dOnj8+P9e83nz5qmNoa7NoEGDcPfuXdXy8OHDceLEiSyHy1EoFOjVqxcuX76McuXKqcoHDhxotomZf/zxR7Rv3x6HDh3Cf//9hxYtWmhMJGRUuHBh7Nu3Dw0bNlSV3b9/H+vXr9e63ZIlS9TmI9iyZYvWREJGPj4++OKLL3Dnzh38888/Om1jiIzJhIwXlM3t1q1baq9fuXLlULduXY3rZxw6S9f3pq4y16frMF2mtnDhQrV93aFDB0yaNEnrNpcvX0ZwcDCCg4ONmusg877P+L4gIqKcgT0TiChbeJdQmArY2SF640aN64mUlHdDHv05Hx6NGlkuQCIiohyu/Pf7kJousl7RSEIIpCrN3w7wbu6E0hP36HSh1xQc7RUI+6G1RdqyFiEEEhMTER0djZs3b+LMmTPYtGmT2jwJwLs7oYcMGaJTnbt27cK2bdtUy7169dJ7kthChQohKCgI1atXx4sXLxAfH49vvvkGG7UcVxrKxcUFO3fuNGhbJycnLF68GOXLl1dNGrxlyxb07dtX4zYZJwyuXr06mjZtqne73t7e8Pb21ns7XSQnJ6sNbVWsWDGztJOZUqnEsGHD1BItP/zwg8b1U1JS8OrVK9VykSJFTBpP/vz54ejoqBrKKmNizVJCQkIwevRo1bK/vz9WrVqV5Xfg6NGjcfToUQBAQEAAbt68aVD7mYdRsvSk6EREZH7smUBE2YbCzg75p3wP7149ta4nUlPxZOQXiAsOtkxgREREH4DUdIGUdKXZH5ZKJKiel9IyzyslXWmRZIw5TJs2DQqFQqeHnZ0dPDw8ULhwYTRv3hyTJk1SSySULVsWhw8fxvfff69z+xkvABcoUAB///23Qc+jUKFCWLhwoWp506ZN2fLO6ICAADTKcFPM8ePH1cb8z+z9uPeAZe/611XGC/QAkDdvXou0O3XqVLUJlRs3boyuXbtqXD8hIUFt2cvLy6TxKBQKtbkiMrdnbq9evULXrl1Vc2S4ublh69atZksiycmdO7da4uL169cWa5uIiCyDyQQiylYUdnbI//338PlU+2RlIjUVT0d/hTQeoBIRERFlG/Xr14ejo+6TXl+6dAnnzp1TLY8fP96oi7ydOnVCnTp1ALzrRWFoYsLcMk66/ObNG9V8D3L8/PxUf9+7Z96Jyw2R+YJxnjx5zN7mqlWrMH36dNWyj49PlsM4JSYmqi2bcr6E91xdXVV/x8fHm7x+TdLS0tC9e3c8ffpUVbZ48WIEBgZaLAYAsLe3h4+Pj2qZyQQiopyHyQQiynYUCgXyffcdfHr31rySnR0K/PgDHDKcXBERERGRdS1duhQNGjRA3bp1ceXKlSzX37Fjh+pvBwcH9OvXz+gYMg6vlLH+7CRfvnxqy2/evNG4bsYJjc+fP4+9e/eaLS5DZE6E5M6d26zt7dixAwMHDlT15nBwcMCGDRv0Hl7JHMOfWWpItczGjRunGqYIAEaOHIne2s6lzChjMklbkoyIiGwT50wgomxJoVAg3+RJgEKBqDVrMv8TBX/+Cbk6dLBOcEREREQ5SO/evTF58mS9tnn79i3i4+Px+PFj3LhxAwcPHsSZM2dUF3hPnz6NWrVqYc2aNVqHnjlz5ozq7+rVq6vd1WyojJMT3717F0+ePEHhwoWNrlcXkZGRiIuLQ3JystahizJPDh0TE6Nx3f79+2PVqlWq5U8++QTTp0/HsGHD4ObmZnzQRnr79q3acsa7801t79696N69u2qeBIVCgcWLF6Nly5ZZbpv5tcoctylk7P2Qccgjc9qwYQPmzJmjWq5bty5+//13i7QtJ2OPD3O8xkREZF1MJhBRtqVQKJBv0kTAToGoVavfF6LAzz8h18cfWzc4IiIiohyiVKlSKFu2rFF1/Pjjj7h//z6mTJmCNf//RpDk5GT06tULBQoUQL169WS3u379uurv8uXLGxXDe35+fihYsCCePXumasNcyYS7d+/in3/+waFDhxAaGmrwOPlKpVLj/5o2bYrOnTurJqlOSkrC119/jenTp6Njx45o1qwZatWqhTJlyljlzvjk5GS1ZWdnZ7O0s2/fPnTu3Fmtvblz52LAgAE6be/u7q62HBsba9L4hBBqQxtlbs8crl27hkGDBqmW8+XLh02bNuk11JipZdz/md8bRERk+5hMIKJsTaFQIN+ECVAoFHizajUK/PQTvDt1snZYREREOY6jvQKWGAVVCGHRSZgd7RQWu8D67jX8cJUsWRKrV69G3bp1MXz4cADvxnIfMGAArl+/DgcH6elnxsmFly1bhmXLlpk8rgcPHpi8zoSEBIwZMwZLly7VmggwlbVr16JPnz7YunWrqiw6OhqrVq1S9Vrw9PRElSpVUL16dVSrVg0NGjRAkSJFzB5b5gvGTk5OJm/jwIEDkkTCnDlz8MUXX+hch5OTE/z8/FTj+D958sSkMb58+RKpqamqZXP3homJiUHnzp1VCSwHBwds3LgRBQsWNGu7WWEygYgoZ2MygYiyPYVCgbzffguvdu3gauFJxIiIiD4UYT+0tlhb7ecfx7Wnpr0rWE6FQl7Y9UUDs7dD6oYNG4YzZ85g5cqVAIA7d+5gy5Yt6NGjh2RdSwyDYuo70KOiotCkSROd5oQwFVdXV2zZsgWbN2/GrFmzcP78eck6cXFxOHbsGI4dO6Yqq1ixIvr3748BAwbA19fXYvGaUlBQED7++GMkJSWpyubMmYPRo0frXZe/v78qmXD//n1ThQhAmrTy9/c3af0ZCSHQt29f3L17V1U2a9YsNGrUyGxtEhERAZyAmYhshEKhYCKBiIgoh+ha1TLj13erZv67skneN998o7ac8Y76jCwxFEzGcexN4fPPP1dLJLi6umLIkCHYvXs3Hj16hLi4OAghtD5WrFhhUNtdu3bFuXPncP36dfz222/o2rUr/P39Nfa+CQ0Nxddff43SpUtj9erVBrWZlczDGqWkpJis7kOHDpkskQCoD6V1/fp1k945f+7cOY1tmdqCBQuwc+dOtbKxY8dCoVAY9Mg4efOtW7c0rle5cuUsY8v4mppryCsiIrIe9kwgIiIiIiKL+qRaYczefwuJKelma8PNyR6dqxYyW/2kXfny5dXmLbh48aLsen5+fqqeA1WrVsXatWtNHkuePHlMVtf169fx33//qZYLFy6MgwcPIiAgwGRt6KJcuXIoV66cajk2NhaXL1/GpUuXcP78eRw5cgRPnz5V/f/Nmzfo168fEhMTMWTIEJPGkvmCsaku0B85cgQdOnRQ671iTCIBAOrXr68aSis1NRVnz55Fgwam6b108uRJteWGDRuapF45GXskZDdMJhAR5WxMJhBRjpX68hWSwq7Bs2lTa4dCREREGXi5OGJ4Y3/8euC22doY3tgfXi7Wm4SUoJZMeP78uew65cuXx7179wC860Fg7ETQ5vZ+6Kb31q1bZ/FEghwvLy80bNhQ7QL2sWPHMH36dAQFBanKvvzyS3Tq1An58uUzWduurq5qy6YYuio4OBjt27c3aSIBAJo0aaK2vHXrVpMkE5KSkrBnzx7VckBAAAoV+jCTmRl7kWR+bxARke1jMoGIcqTUV68Q3r8/UsLDUfCX2cjVrp21QyIiIqIMhjTyx76wF2aZO6FCIS8MaWS+8cpJN0L830TbaWlpsuvUq1cPO3bsAADcvHkTd+/eRalSpSwSnyEuX76s+jswMNBkd7WbQ8OGDbF//34MGzYMixYtAvDurvHly5djwoQJJmsnc8+PjJNqG+LYsWNo37692vBUpkgkAECxYsVQt25dnDp1CgCwfv16zJw50+g76Ddv3oz4+HjVcp8+fYyqLysTJ07E0KFDTVZf48aN8fLlS9XyjRs3ZNdzcXHJsq6IiAjV36bsFURERNkDkwlElOOkvX6N8P6fIeXhQwDAs2/GAUqBXB3aWzcwIiIiUnG0t8PcnlXQZeEpRCemmqxebzdHzO1ZBY72nB7O2jIOs6PpTvhPPvkE48ePVy3PmzcP8+bNM3tshnr8+LHq748++sjgeu7cuWOKcLKkUCjw66+/YsmSJVAqlQDeDdVkSnnz5lVbzngxWV/Hjx9H27ZtkZCQoCozVSLhvYEDB6qSCS9fvsTy5csxbNgwg+tTKpWYPXu2atne3h79+vUzOk5t8ubNK3ndjVG2bFlVMiEgIMDgHkLp6emIiopSLfv5+ZkkPiIiyj54hE1EOUra69d41P8zpDx48H+FSiWejR+PmEyTlBEREZF1+ft5YPXnteDtZprhiLzdHLH681rw9/MwSX1kuNDQULx48UK1XKJECdn1SpUqhZYtW6qWFy1ahGvXrpk9PkNlnOg44wVvfaSkpOCnn34yVUhZ8vDwQOHC/zfp+atXr0xaf+YLxobWf/LkSbMnEoB3vQaKFy+uWv7uu++MSoD8/fffCA0NVS0PGDAARYsWNSZEmxUZGanWI4nJBCKinIfJBCLKMdIiIvDoswFIuX9f+k+lEs/Gf4uY/9+NnoiIiLKHioVzYcuwuqhQyMuoeioU8sKWYXVRsXAuE0VGxvjll1/UlttpGXLyhx9+UF2kT0lJQbdu3YwaKuf58+dqQ86YUsaL0KdPn0Zqqv69ar7//nu91tc0RJSukpOT8fr1a9Wyj4+PUfVl5uzsjPz586uWHz16pHcdp06dQps2bdT2mzkSCQDg5OSE6dOnq5YjIyPRu3dvg17nq1evqvWscXd3x5QpU0wSpy16+P97hr+X8fNCREQ5A5MJRJRjxB44gJT/P4GfrP+fUIj+7z+LxURERERZ8/fzwLbh9fB1yzJwc7LXa1s3J3t83bIMtg2vxx4J2cTChQuxevVq1bKLiwt69uypcf1atWrhiy++UC3fvHkTjRs3xoOMPU11FBoailq1auGTTz5BSkqK3ttnpVmzZqq/IyIiJEmTrCxcuBCzZs3Sa5uvvvoKXbp0wZMnT/Ta7r2lS5eqTWRcu3Ztg+rRpkyZMqq/72k7HpcREhKC1q1bIy4uTlVmrkTCe71790anTp1UywcOHEDv3r2RnJyscx2hoaFo27atWgLkjz/+UOsF8qHJvO9Lly5tpUiIiMhcmEwgohzD99NPkWfkSO0rCYHnEyYiett/FomJiIiIdONob4eRTUsjZGIzTOtYHhUKecFOIb+uneJdT4RpHcsjZGIzjGxamnMkZAMPHjxAv379MHz4cLXySZMmoUiRIlq3nT17Nho1aqRavnbtGgIDA/HLL7/o1MvgzZs3GD9+PGrXro3Hjx8jKCgIQ4YMMeyJaNG3b194ePxf0mry5Mn4+eefkZ6ernW7ly9fYsCAAarXRtOwT5ldv34df//9N7Zu3YoyZcpg0KBBuHLlis7xrly5EmPHjlUte3h4oHfv3jpvr6uMF43v3r2r83ZnzpxBq1atLJpIeG/p0qVqcwP8+++/qF27NkJCQrRul56ejgULFqBevXpq84IMGDAAgwYNMlu8tiBjMsHR0RHFihWzYjRERGQOnICZiHIUv5EjAAUQMf9PzSsJgecTJwJCwPuTzpYLjoiIiLLk5eKI/nWLo3/d4khMScON57F4/OYtUtKUcHKwQxFfV5Qr4AU3J57KmEpERARu3ryp1zZJSUmIj4/HkydPcP36dRw8eBAhISFq46UDQNeuXfHtt99mWZ+zszO2b9+Ozp0748iRIwCA+Ph4jBs3Dj/99BPat2+Phg0bomzZsvD29kZycjJevXqFO3fu4MCBAwgODkZiYqKqvty5c0uSGqbg5+eHn3/+WdWTQgiBiRMnYunSpfj0009Rq1YtFChQAM7OzoiJicGNGzdw8OBB7NixQ9U7oESJEpg3bx46dOiQZXuzZs1SDb/z9u1bLFu2DMuWLUPJkiXRpEkT1KtXD/nz54efnx9cXFyQmJiIJ0+e4MqVK9i2bZvaWP4A8Ntvv5llHPuKFSuq/n7w4AHi4+PVki5yzp49i1atWiE2NlZVNmTIELRu3Vrv96OcAgUKIFcuzcOe5c6dGwcOHEDjxo1x//8Pk3r58mXUqVMH9erVQ6dOnVCtWjXkyZMHycnJePLkCU6dOoV169apJREAoHv37liyZInRMdu6jImucuXKwd5ev55mRESU/fEInIhyHL8RI6Cws8PrufM0ryQEnk+aBAglvLt0sVxwREREpDM3JwdUK+aLary51awWLFiABQsWmLROOzs7jBgxAnPmzNH5gmKuXLmwb98+TJgwAX/88QeUSiUAIDo6GmvWrMGaNWt0qqd8+fLYtGkTypUrZ3D82owcORJPnjxRG67o/v37auPwa1K6dGns3btXbQ4DbebOnQs7OzusXLlSLVFz//593L9/H8uWLdOpHjs7O8ycORODBw/WaX191axZU/W3UqnEpUuX0KBBA43rv3nzBq1atUJMTIxa+aJFi7Bo0SKTxLRixQp89tlnWtcpUqQIzp07h969e2Pfvn2q8pMnT+LkyZNZtmFvb4/vv/8e3333ndrk3B+qCxcuqP7O+J4gIqKcg32BiShHyjNsGPyy6h4tBJ5PmozozZstEhMRERFRTufo6Ij27dvj7NmzmDdvnt53Jjs5OeG3337D2bNn0aFDB70u0BYoUACLFy/GlStXzJZIeG/mzJnYtGlTlsM3vWdvb48hQ4bg3Llz8Pf317kdb29vrFixAqGhoRg0aBC8vPSfqLxx48Y4ffo0vvnmG7231VWVKlXg6OioWs54UVlObGwsoqOjzRaPPnx9fbF3716sX78eJUuW1Hm7Fi1a4OLFi/j++++ZSMC7BFHGeU6YTCAiypnYM4GIcqw8Q4cACgVez5mjdb3nk7+DUCrh0727hSIjIiIism3Ozs7w9PSEp6cnihUrhsDAQFStWhXt27dH7ty5ja6/WrVq2LFjBx49eoTt27fjxIkTuHbtGp4/f474+HjY29vDx8cHJUuWRI0aNdCyZUu0bNkSDg6WO8Xt2rUrPv74Y+zYsQO7d+/G+fPn8ejRIyQkJMDZ2Rm5c+dG+fLl0bhxY/Ts2dOo8ePLly+PJUuW4M8//8SZM2dw/PhxnDp1CuHh4YiMjMSbN2+Qnp4Od3d3FCxYEAEBAahduzY6duxo9sQK8G6S7erVq+P06dMAgKNHj1pk3gNT6tmzJ3r06IHDhw9j586dCAkJwYMHDxAdHQ0HBwf4+vqifPnyqFevHnr06KE26TS92+cZ1a9f30qREBGROSlE5kEtiSjbqFOnjmQCsNq1a6sO0kk3EUuW4PVvv2e5Xv5p0+DTgwkFIiKyDQ8fPlSNwf6eq6srihcvbp2AiOiDNm3aNEydOhXAuyGrIiMjOWb+B2T48OFYuHAhAKBo0aJ49OiRlSMiY1niOIPXPIhsD4c5IqIcL8///oe833yd5XovpkxB1IYNFoiIiIiIiChnadOmjervmJgYnDlzxorRkKUFBQWp/m7VqpUVIyEiInNiMoGIPgi5Bw5EXh3GiX0xdRqi1q+3QERERERERDlH9erVUbBgQdXyli1brBgNWdLly5dx9+5d1fLHH39sxWiIiMicmEwgog9G7oGfI+/48Vmu92LaD3izdq0FIiIiIiIiyhns7OzQq1cv1fLGjRuhVCqtGBFZyrp161R/58mThz0TiIhyMCYTiOiDknvAZ8j7bdYJhZc/TsebNUwoEBERERHpqk+fPqq/nz59KpmUl3IepVKJDRmGiu3Ro4dFJ0InIiLLYjKBiD44uT/7DPkmTshyvZfTp+PNqtUWiIiIiIiIyPZVrlwZNWrUUC3/9ddfVoyGLGHHjh14/Pixanno0KFWjIaIiMyNyQQi+iD59uuHfJMmZbney59+wpuVKy0QERERERGR7Rs9erTq7//++w9PnjyxXjBkdnPnzlX93bJlS1SoUMGK0RARkbkxmUBEHyzfvn2Qb/LkLNd7OXMWkm7ftkBERERERES2rVu3bihSpAgAIC0tTe1iM+UsFy5cQHBwsGp53Lhx1guGiIgsgskEIvqg+fbpjXzfaU8o5J86FS5lylgoIiIiIiIi2+Xo6IipU6eqlv/66y+8ePHCegGR2UzK0NO7RYsWaNasmRWjISIiS2AygYg+eL69eyP/lO9l/5d/6hT49Ohu4YiIiIiIiGxX//798dFHHwEAEhMT8dNPP1k5IjK148ePY//+/QAAOzs7zJ4928oRERGRJThYOwAiouzAp1cvQGGHFxnuosr3/Xfw6dnTekEREREREdkge3t7hIWFWTsMMqMGDRpACGHtMIiIyMLYM4GI6P/z6dkD+adNAxQK5PtuMnw//dTaIREREREREREREWUL7JlARJSBT4/ucKtWFc6lSlk7FCIiIiIiIiIiomyDPROIiDJhIoGIiIiIiIiIiEgdkwlERERERERERERERKQVkwlEREZKi4pC1IaN1g6DiIiIiIiIiIjIbDhnAhGREdKiohD+2QAk37qFtNev4ffFSGuHREREREREREREZHLsmUA6mTp1KhQKhc6PDRs2WDS+yMhIrFixAoMHD0bt2rVRqFAheHh4wNHREd7e3ihWrBiaNWuGESNGYNOmTYiJibFofJQzpUVFIXzA50i+dQsAELFgAV7Pmw8hhJUjIyIiIiIiIiIiMi32TCCbdu7cOcyYMQO7d+9GWlqa7DoxMTGIiYlBeHg4Dh8+jL/++guOjo7o27cvJk+ejBIlSlg4asoJ0qKiEP75QCTfvKlWHvHXXwAE8nzxBRQKhXWCIyIiIiIiIiIiMjH2TCCbFB0djT59+qBmzZrYvn27xkSCJqmpqVi+fDnu3r1rpggpJ0uPjkb4wIFIvnFD9v8Rfy3E67lz2UOBiIiIiIiIiIhyDPZMIJ2MHDkSPXv21Pj/4OBgDBs2zCKxhIWFoXXr1njy5IlauaenJ5o0aYLmzZujaNGiyJs3Lzw9PZGQkICIiAjcvn0bp06dwoEDBxAbG4uGDRuiRYsWFomZcpbEy5eRfPOW1nUi/14ECMBv9JfsoUBERERERERERDaPyQTSSZ48eZAnTx6N/3/x4oVF4rh06RJatGiByMhIVVm+fPkwefJkDB48GE5OThq3bdeuHb766iskJSXh77//Rq1atSwRMuVAno0bo+Avs/Hsm3GAUqlxvchFiwClEn5jvmJCgYiIiIiIiIiIbBqTCWQzXr9+jY8//lgtkdC9e3csX74c7u7uOtfj4uKC0aNHmyFC+pDkatcOCoUCT78ZB6Sna1wvcskSQCjhN3YsEwpERERERERERGSzOGcC2YwBAwbg8ePHquXx48djw4YNeiUSiEzJq21bFPrtV8DeXut6kUuX4dWvv3IOBSIiIiIiIiIisllMJpBN2LlzJ3bv3q1a/uSTTzBz5kze6U1W59W6NQr99luWCYU3y5bj1exfmFAgIiIiIiIiIiKbxGQC2YQJEyao/i5cuDCWLl1qxWiI1Hm1boVCv/8OOGgfOe7NihV4NXMWEwpERERERERERGRzmEygbC84OBhhYWGq5a+//ho+Pj5WjIhIyqtVSxSao0NCYeVKvJo5kwkFIiIiIiIiIiKyKUwmULa3fPly1d+enp4YMGCAFaMh0syrRQsU/mOODgmFVXj5089MKBARERERERERkc1gMoGyvaCgINXfnTp1gpeXlxWjIdLOs3lzFJ77B+DoqHW9qNWr8XLGT0woEBERERERERGRTWAygbK1sLAwvHjxQrVcp04dK0ZDpBvPZs1QeO7crBMKa9bg5Y/TmVAgIiIiIiIiIqJsT/tYHERWdvXqVbXl2rVrWymS7OPatWtWS6qcPn3aKu3aIs+mTVB43lw8HfUlRGqqxvWi1q0DIJDvu++gUCgsFyAREREREREREZEemEygbO3OnTtqy+XKlZOsk5CQgIMHD2L37t24dOkSXr58iVevXsHe3h7e3t4oXbo0atWqhQ4dOqB+/fqWCt1s4uPjERISYu0wSAeeTZqg0Px5ePrFqCwSCushhED+776Dwo4dxoiIiIiIiIiIKPthMoGytYcPH6r+dnFxgYuLi2r57du3+OOPPzBz5kzExsbKbp+YmIhnz57h6NGjmD17NsqXL4/p06ejU6dOZo6c6B3Pxo1ReMGfeDLyC4iUFI3rRa/fACgF8k/5ngkFIiIiIiIiIiLKdnjFirK1jEkCb29v1d83btxAuXLlMHHiRI2JBDlhYWHo3LkzevTogfj4eFOGSqSRR8OGKLzgTyicnLSuF71xIxI4lBQREREREREREWVD7JlA2VpCQoLqb09PTwDA2bNn0bZtW0RGRqr+16BBA7Rv3x6BgYHIly8f7O3t8ebNG9y/fx8nTpzA1q1bERMTo1r/33//xdOnT7F//364u7tb7gnRB8ujQQMUXrAAT0aM0NhDIe//Y+++45uq/j+Ov5N0U1pGyyzLsoeylClDv6JfVBQEBREKCDIFt4ALBEVQvyqKCCJLUJSNuFB/grK3sjelLXuUUlqatrm/P5BIoU2bNk06Xs/Ho49H7r3nnvMJRkjzzjnn5ZcV2KKFmysDAAAAAAAAMkeYgDzNet2HrikpKTp58qQefPBBe5Bw2223afr06WrYsGG697dp00Z9+vTRRx99pP/9738aO3asUlJSJElr1qxR//79NWfOnNx/Ii4UGBiounXreroMZEPgnS0V9umnVwOFpKQ010q9+KJK9u7lmcIAAAAKqaNHj6pKlSr243Hjxmn48OEerAhZcezYMfXv31+rV6/WLbfcookTJ6p169aeLgtIF69XAAUJYQLytOv3SIiLi9Pw4cN1+vRpSVKfPn00efJk+WSydIx0dVbDG2+8odatW6t9+/ZKTEyUJM2dO1dPPPGE7rvvvtx5Armgbt26WsdSOPlWYMsWqjD5U0UNHGQPFEq98LxKPtnHw5UBAAAA+UOvXr30+++/S5L+/vtvPfTQQ4qMjFRwcLCHKwNuxusVQEHCngnI065fgujcuXOaNWuWJOnee+/V1KlTsxQkXK9NmzaaMWNGmnNjxozJeaGAE4o0b64Kn02Wyc9Poc8/p5J9+3q6JAAAUIisXLlSJpMp2z8BAQEqU6aMatWqpU6dOmn06NFas2aNDMPw9FODh1SuXNn++qhcuXKuj7dp06Y0xxcvXtTBgwdzfVzkX0ePHs3R33uZ/Th63fN6BVCQECYgTwsJCbnpXNGiRTV79mxZLJZs9fnYY4+lmVK4du1aRUZGZrtGIDuKNGum8O+XK6RfP0+XAgAA4JTExESdOnVKe/fu1eLFizVq1Ci1bNnSvnzHtWVFgdzSrFmzNMclSpRQjRo1PFQN4BivVwAFCWEC8rTr1y+9pnv37ipVqlSO+o2IiEhzvGbNmhz1B2SHd/nyni4BAADAZY4ePaphw4apfv36fOsWuWr69Om67777VKRIEdWrV0+LFi1SYGCgp8sC0sXrFUBBwp4JyNOqVq1607l77rknx/3eeeedaY737duX4z4BAACA/Gjw4MEaMmRIltoahqHLly8rNjZWe/fu1caNG/Xdd98pNjbW3mbXrl1q2bKlfv/9d9WqVSuXqkZhFhYWph9//NHTZSAfe/jhhzVu3DiX9eft7Z3htey+XtkgHkBeRJiAPK1JkyY3nUtvtoKzypYtm+b4+l9+gLwq9dIlnZ81WyED+svkxV/fAADANUJCQlSzZk2n7/vPf/4jSUpKStKkSZM0atQoXbp0SZJ06tQpPfHEE9q4cWO2lycFgNwSHBycrb/3AKCwY5kj5GnlypW7aSMjV/wyYjanfenzCw7yutRLl3Tsyb46+8knOv7SyzJYixgAAOQRvr6+eu6557Rq1SoVL17cfn7r1q368ssvPVgZAAAAXIkwAXneww8/nOb48OHDOe4zOjo6zXF6Gz0DeUVqfLyO9e2rK3//LUmK++EHHX/pJQIFAACQpzRo0EDvv/9+mnPz5s3zUDUAAABwNcIE5Hndu3dPc/zzzz/nuM8///wzzXHdunVz3CeQG1Lj4xXVt5+u/PV3mvNxP/yomBdelJGc7KHKAAAAbta9e3cVK1bMfnzj+24AAADkX4QJyPMaN26sFi1a2I/nzJmjM2fO5KjPzz//3P7Yx8fnpg2ZgbwgNf6yovo9pcTt29O9fumnnwgUAABAnuLj46Pbb7/dfpyQkKC4uDgPVgQAAABXIUxAvjB69Gj74/j4eA0YMECGYWSrr88++0zr16+3Hz/44INp1nYF8orkY5FK2r/fYZtLP/+smOdfIFAAAAB5xo1LiF6+fDlb/aSmpuqXX37R4MGD1aRJE4WGhsrHx0eBgYGqVKmSHnjgAY0fP17Hjh1zRdnZdvnyZa1YsUKvvvqq7rrrLtWqVUuhoaHy9vZW8eLFFR4ervbt22vs2LHatWtXjsczDEN//vmnXn75ZbVp00YVK1ZUkSJF5OXlpRIlSig8PFz/+c9/NGLECC1dulQJCQkueJZ5y/r162Uymew/M2fOzNJ9vXr1st+T3uz0hIQEzZ8/X71791aDBg0UEhIiHx8fBQQEKCwsTPfcc4/Gjh2rffv2ufgZSTExMfrwww/18MMP65ZbblHRokXl7e2tkJAQNWrUSP3799cPP/ygFBctdequ1+3Ro0fT/LdasGBBmuupqan6+eefNXjwYDVv3lylS5eWn5+fLl68mNOnmGdk9fVav379NO2qVKmS5vqIESPSXL/x58MPP8z9JwOg0PPydAEonHbs2KF+/fppz549ateunaZOnerwA/27775bERERmjVrliRp0aJFGjBggCZNmiQvr6y/jH/++WcNHTrUfmyxWPT6669n/4kAucivdm1V/GKajvXtJ1t8fIbtLq1YoZjnnlP599+XycfHjRUCAADc7MKFC/bHJpPJ6S/uGIahL7/8UqNHj053v7Tk5GRdvnxZx44d0/fff68RI0aoW7dueuutt1S5cuWclp9l+/bt08cff6xZs2YpPoP3arGxsYqNjdXhw4f1448/6rXXXtO9996riRMnqnr16k6PuXz5cg0fPjzDD3cvXLigCxcu6PDhw/rtt98kSUWKFNGDDz6o559/Xo0bN3Z6zMIgOTlZ//vf//Tuu+/q3Llz6V6PiYlRTEyMfv31V73++ut66KGH9OGHH6pSpUo5GjsyMlKvv/665s6dq9TU1Juunzt3TufOndPWrVs1depUVahQQaNHj7YHI87yxOs2IwsXLtTw4cN18ODBm65l98uDAIDcxcwEuF1ycrI6duyoDRs2KC4uTgsWLNCzzz6b6X0TJ05M8+2RqVOnqmXLltq8eXOm9yYkJOjFF19U+/btlXzdN7hfeeUV3Xrrrdl7IoAb+Nevr4pfTJM5MNBhu0u//Kro556TYbW6qTIAAICb2Ww2bb9uicbw8HD5+fll+f6TJ0/q3nvvVURERLpBQnoMw9BXX32lW2+9VXPnznW2ZKclJCRo8ODBqlWrliZNmpThB7IZ+fnnn9WwYUMtXLgwy/cYhqFnnnlGDz74oNPfEr98+bLmzZunO+64QyNGjHDq3sIgMjJSTZo00fDhw9MNEtJjGIaWLFmi+vXr20Ob7Jg1a5bq1aun2bNnpxskpCcqKkp9+vTRf//7X6eW//XE6zYjhmFo0KBB6ty5c7pBAgAg72JmAtxu7969OnToUJpzy5cvz/S+oKAgrVixQq1bt9aBAwckSRs2bNDtt9+uZs2aqX379qpfv77KlSsnb29vnT59WjExMfrll1+0dOlSXbp0KU1/3bt31xtvvOG6JwbkEv/bblPF6V/o2JN9ZbvhdXy9+F9/U/Szzynsg/8xQwEAAHjE8uXLdfLkSfvxgw8+mOV7Dx06pHvuuUdHjhxJc75169bq0KGDbrvtNoWGhiolJUXHjx/Xxo0b9c0332j/P8tCXrp0SU888YROnTql5557zjVP6AZXrlxRw4YNb1rmpkKFCmrdurXuvPNOVahQQSVLlpSfn59iY2O1Z88e/f7771q0aJH9i02XL1/W448/rt9++00tW7bMdNxRo0bpo48+SnOuadOmeuihh1SvXj2VKlVKAQEBiouL09mzZ/XXX39p/fr1+uWXX2T958smhmGoSZMmLvqTKBgiIyN15513Kioqyn6uWbNm6tChg+rXr6/SpUvLbDbr7Nmz2r59u5YvX66VK1fa28bGxqpDhw76448/1KhRI6fGHjVqVJrlfCUpNDRUXbp0UcuWLRUeHq6AgABdvHhRe/bs0c8//6zvvvtOSUlJkq5+uN+qVSv99ttvKleunMOxPPW6zcizzz6ryZMn24/Dw8P10EMP6c4771Tp0qUVGBiookWLZrv//GrJkiW6cuWK/fjo0aP673//az+OiIjQ8OHDM7y/dOnSuVofAEiSDMAFfv/9d0OS/efrr7/OsO2uXbvStJVklCpVKstjXbhwwejQocNNfWT1x2w2GyNHjjRSU1Nd8dRzVdOmTW+qv2nTpp4uCx6S8Pffxt7Gtxu7a9R0+HNswEAjNSnJ0+UCAHLZkSNHjN27d6f5OXLkiKfLQj5w43v3N954wyX9RkVFGRUqVLD36+PjYxw+fDhL9547d86oWrVqmrqaNWtmbN261eF9NpvN+Oqrr4yQkJA0937zzTdZGvfIkSNp7hs3blym97zwwgv29g899JCxYsUKw2azZXpfVFSU0bZt2zTj3XLLLUZycrLD+/bu3Wt4eXnZ7ylWrJjx/fffZ+n5nT9/3pg4caIREhJitGzZMkv3ZEelSpXs9VWqVCnXxrlm3bp1af4cZ8yYkaX7IiIi7PfUqFHDuOOOO+zHDRo0MNavX59pH6tXr07zfK/1deXKlSzX/9FHH6W538/Pzxg/fryRkJDg8L6oqCjj0UcfTXNvw4YNjcTExEzHdPfr9pob/x/r37+//XHRokWNqVOnGikpKVnqKydurCMiIiLXx7wmu6/X7Pz95ErueJ/BZx5A/sMyR3C7GjVqqHbt2mnOdezYMcv3FytWTEuXLtXSpUudXqLo7rvv1saNG/XWW2/JbOblj/zFv149VZw+XeagIIft4n//XTFDh8nGkkcAAMBNvv/+ezVr1izNN7wnTpx40waiGenbt2+a5U4GDRqk1atXq0GDBg7vM5lM6tatm7Zv365atWrZzz/55JO5tjHzmDFj9MADD+i3337TkiVLdM8992Rp7fqwsDD99NNPatWqlf3c4cOH9fXXXzu87/PPP0+z6e7ChQvVvn37LNVavHhxPf300zpw4ECWNykuLPbt26eNGzdKkjp16qR169ZlaeZGixYttHHjxjT7c+zbt09ffPFFlsbdunVrmpkzZcqU0bp16/TSSy/J39/f4b1hYWH65ptv9N5776Xp76WXXsp0XHe/bjMyZcoUSVdnYaxatUr9+vWTxWLJVl8AAPdjmSO4ncVi0aJFi9S7d2/t2rVL7du314QJE5zup0OHDurQoYO2b9+uJUuWaMuWLdq1a5fOnz+v+Ph4+fr6KjQ0VLVq1VKrVq3UsWNH1axZMxeeEeA+/vXqquKM6TrW50nZLl7MsF38ypWKeXqoyn88UWaWPAIAOOnMp5/q3OTPcn2cyt/Mk98NXzLJSPTTTyt+5apcrkiquePvLLc9+J97FNypo0IHDcrFivIWwzCUkJCg2NhY7d27Vxs2bND8+fPT7JMgSaNHj1b//v2z1Ofy5cu1ePFi+3G3bt00adIkp+oqX768fvnlFzVu3FgnT55UfHy8XnzxRX3zzTdO9ZMVfn5++u6777J1r4+Pj6ZOnao6derY18hfuHChevTokeE9a9assT9u3Lix7rrrLqfHLVasmIoVK+b0fYVB48aN9dVXX8nX1zfL95QqVUpz585VixYt7Oc+/fRTDcrC3wX9+/e3/7f39/fXDz/8oPr16ztV8/PPP6+LFy9qzJgx9rH79eunevXqZXiPu1+3jphMJn355ZeZhoUAgLyHMAEu0aZNGxmGkeX2NWrU0Nq1a10ydv369Z1+8wXkZ/516lzdQyGzQGHVKkU//bTCJk6U2YlfjgAAUKpNxj9rZOcmZ94/GimpbqnJGUZyspRq83QZOTZ69Oib1m7Prpo1a+rTTz9V27Zts3zPm2++aX9ctmxZffZZ9oKs8uXLa/LkyfZZz/Pnz9fYsWNVrVq1bPWXW2rUqKHWrVvr//7v/yRJf/75pwzDyPBb4tdvChweHu6WGgsLk8mkqVOnOhUkXNO8eXO1adPGvofCrl27dPTo0TQzFm70/fffa/Pmzfbj0aNHZ/sD9VGjRun777/X1q1blZqaqvHjx2vOnDnZ6isrnH3dOvLYY4/p3nvvdXWJAAA3YJ0XAMiH/OvUUaUZ02UJDnbY7vKqPxQ95GnZ/tmoDQAAIDe1bNlS3t7eWW6/bds2bdq0yX788ssvKyiTJR0defjhh9WsWTNJV8Oq7AYTue36zWvPnz+vs2fPZtg2NDTU/vjQoUO5Wldh85///CdH345/9NFH0xxf/1pOz9SpU+2PS5curaFDh2Z7bLPZrHHjxtmP58+fnyZ4yg3OvG4dycnzdpXIyEjt3bs3xz8xMTGefioA4FaECQCQT/nVrq2Ks2bKksmU9ct//qnowUMIFAAAQK6bNm2a7rzzTjVv3lx//fVXpu2XLVtmf+zl5aWePXvmuIbrl1e6vv+8pHTp0mmOz58/n2HbqlWr2h9v3rxZP/74Y67VVdh07tw5R/c3bdo0zfH+/fszbJuYmKhffvnFfty9e/dszYi4Xrt27ez7klitVv3000856i8zzrxuMxIaGmoP/Dxp5cqVqlWrVo5/Bg8e7OmnAgBuxTJHAJCP+dWsqYozZ+hYr95KjY3NsN3l1asVPWiwwiZ9IrOfn/sKBAAAeV737t316quvOnVPYmKi4uPjFRUVpT179ujXX3/Vhg0b7EtXXdvMds6cOQ4/sN2wYYP9cePGjVW8ePHsPYnrXL858cGDBxUdHa2wsLAc95sV586d06VLl5SUlORwGa8bN4e+6GDpyoiICM2ePdt+3KlTJ40dO1YDBw5UQEBAzosuxHL6ofaNr6sLFy5k2HbHjh1KTEy0H7dr1y5HY1/Tvn17+x4jv//+u7p37+50H7nxus1IVja5BgDkXYQJAJDP+dWsqYqzZl4NFBz8AnN5zZqrgcKnkwgUAACAXdWqVVWzZs0c9TFmzBgdPnxYb7zxhn3d9qSkJHXr1k1ly5ZNs1Ht9Xbv3m1/XKdOnRzVcE1oaKjKlSun48eP28fIrTDh4MGDmjlzpn777Tft2LFDly9fzlY/NlvGe2/cdddd6tixo32T6itXruiFF17Q2LFj1aFDB919991q0qSJqlevnq316wuznO5BcWP4lZCQkGHb61/rkute77fddluGY2TEHa/bjFybSQEAyJ8IEwCgAPCrUePfQMHBdOPLa9cqetAghU2aJLO/vxsrBADkKxazTE6se59dznzwafKyuKUmZ5i8vSULK8dec8stt+jLL79U8+bNNWjQIElSSkqKevfurd27d8vL6+ZfP69f4/2LL77QF1984fK6jhw54vI+L1++rOeee07Tpk3L1geqzpo7d66eeOIJLVq0yH4uNjZWs2fPts9aKFq0qBo0aKDGjRurUaNGuvPOO1WhQoVcry2/slgsOZ7ZceNr2tFr4cb9DHLjv01mr3V3v27TUyyTJVrdJSIiQjNnzvR0GQCQ7xAmAEAB4Ve9+r9LHjkMFNbp0ooVCn7oITdWBwDIT0IHDVLoPx8G5xVhH3/s6RJuUvXXXzJvVAgNHDhQGzZs0KxZsyRJBw4c0MKFC/XYY4/d1Pb6ZV9yS1xcnEv7u3Dhgtq2bZulPSFcxd/fXwsXLtSCBQs0fvx4bd68+aY2ly5d0h9//KE//vjDfq5evXqKiIhQ7969VaJECbfVmx+kF27lJk+/1j3xuk0PS3MBQP7G12gAoADxq15dlWbNlKVkyQzbhAweTJAAAABy1Ysvvpjm+Ppv1F+vSJEiuV6Lo6VnsqNPnz5pPpD19/dX//799f333ysyMlKXLl2SYRgOf2bMmJGtsTt37qxNmzZp9+7dev/999W5c2eFh4dnOMtnx44deuGFF1StWjV9+eWX2RoTruHp17onX7cAgIKDmQkAUMD4VqumSrNmKrJXb6WePZvmWsigQQp9eoiHKgMAAIVFnTp10uxbsHXr1nTbhYaG2r9N3bBhQ82dO9fltYSEhLisr927d2vJkiX247CwMP3666+qUaOGy8bIilq1aqlWrVr247i4OG3fvl3btm3T5s2b9fvvvysmJsZ+/fz58+rZs6cSEhLUv39/t9aKq0JDQ9McL1u2TNWqVXPL2HnldQsAyP8IEwCgAPKtWvVqoBDRyx4olBw4QCEECQAAwE2uDxNOnDiRbps6dero0KFDkq5+qzqnG0HntmtLN13z1Vdf5YkPZIOCgtSqVSu1atXKfu6PP/7Q2LFj9csv/y7HNWzYMD388MMqXbq0J8os1G7ccNmdr/e8+roFAOQ/LHMEAAWUb3i4Ks2eJUtoiEoO6K/QoUOd2ugSAAAgJwzDsD9OSUlJt02LFi3sj/fu3auDBw/mel05sX37dvvjW2+9VXfeeafnislEq1at9PPPP6eZiZCUlKTp06d7sKrCq27dugoODrYff/fdd24bOz+9bgEAeRthAgAUYL633KJbli5V6LBhBAkAAMCtrl9mJ6Nvwnfq1CnN8cSJE3O1ppyKioqyP65du3a2+zlw4IArysmUyWTSe++9J7P531/9d+/e7ZaxkZa3t7ceeOAB+/GCBQsynLHjavntdQsAyLsIEwCggPMqUYIgAQAAuNWOHTt08uRJ+3GVKlXSbVe1alW1a9fOfjxlyhTt3Lkz1+vLruvfU12+fDlbfVitVr399tuuKilTgYGBCgsLsx+fPn3abWMjrcGDB9sfJyUl6fnnn3fLuPnxdQvJx8cnzfGVK1c8VAkA/IswAQAAAADgUu+++26a4/vvvz/Dtm+++ab9w06r1aouXbro3Llz2R77xIkTio+Pz/b9jlSuXNn+eN26dUpOTna6j9dff92p9hktEZVVSUlJOnPmjP24ePHiOeoP2desWTO1b9/efvz1119r0qRJOeozK7MFPPG6Rc6VKFEizfG1/WUAwJMIEwAAadgSEnTy7beVmku/hAMAgIJt8uTJ+vLLL+3Hfn5+6tq1a4btmzRpoqefftp+vHfvXrVp00ZHjhxxeuwdO3aoSZMm6tSpk6xWq9P3Z+buu++2Pz579uxNoUlmJk+erPHjxzt1z7PPPqtHHnlE0dHRTt13zbRp05SYmGg/btq0abb6gWtMnjxZRYsWtR8PHTpUEyZMcLofm82m559/XnXq1NHPP//ssK0nXrfIOT8/vzSzun744QclJCR4sCIAIEwAAFzHlpCgqP4DdGH2l4p6sq9SL13ydEkAACCfOHLkiHr27KlBgwalOf/KK6+oQoUKDu+dMGGCWrdubT/euXOnbr31Vr377rtZmmVw/vx5vfzyy2ratKmioqL0yy+/pNl42FV69OihwMBA+/Grr76qcePGKTU11eF9p06dUu/eve1/Nhkt+3Sj3bt367PPPtOiRYtUvXp19e3bV3/99VeW6501a1aapXQCAwPVvXv3LN8P16tYsaK+/vprWSwWSVdDgZdffllt27bVpk2bstTHihUr1KRJE/3vf/9TcnKyHnnkEYd7Ybj7dQvXuffee+2Pz58/r6effjrN5vbXW7t2bY5nMgFAZrw8XQAAIG+wJSYqasBAJfzzS0ziX3/pWN++qjhtmizXfXsKAAAULGfPntXevXuduufKlSuKj49XdHS0du/erV9//VXr16+/6UOuzp07a/jw4Zn25+vrq6VLl6pjx476/fffJUnx8fF66aWX9Pbbb+uBBx5Qq1atVLNmTRUrVkxJSUk6ffq0Dhw4oBUrVmjlypVpvrFbsmTJm0INVwgNDdW4cePsMykMw9DIkSM1bdo0Pf7442rSpInKli0rX19fXbx4UXv27NGvv/6qZcuW2WcHVKlSRRMnTtSDDz6Y6Xjjx4+3fziYmJioL774Ql988YVuueUWtW3bVi1atFCZMmUUGhoqPz8/JSQkKDo6Wn/99ZcWL16sHTt2pOnv/fffV2hoqIv/VNJKTk52+vXkSOnSpQvc0kz333+/vv76a/Xo0UNJSUmSpJUrV+qOO+5Q48aNdf/996tBgwYqX768/Pz8dO7cOZ08eVJr167VTz/9pP3796f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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "end_time": "2024-04-09T22:46:01.616168Z", + "start_time": "2024-04-09T22:46:01.406578Z" } - ], + }, "source": [ "f,ax = plt.subplots()\n", "\n", - "temps = np.linspace(1, 1300, num=1000)\n", + "temps = np.linspace(1, 1600, num=1000)\n", "ax.plot([x[1] for x in belas_data], [x[0] for x in belas_data], \n", " label = \"Belas (2014)\", marker=\"o\", markersize=4)\n", "ax.plot(temps, belas_linear_fit(temps), color=\"C3\", ls=\"--\", label=\"Belas Linear Fit\")\n", @@ -1174,7 +1185,26 @@ "ax.set_title(\"CdTe Experimental Bandgap T Dependence\")\n", "ax.legend()\n", "plt.show()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now we use this function to set our `delta_gap` parameter in the `get_quenched_fermi_level_and_concentrations()` method, to account for the temperature-dependent band gap shift:", + "id": "8dddf4e1c3451348" }, { "cell_type": "markdown", @@ -1204,11 +1234,11 @@ "for anneal_temp in anneal_temperatures:\n", " band_gap_shift = belas_linear_fit(anneal_temp) - 1.5 # 1.5 eV is our DFT (and room temp) gap\n", " fermi_level, e_conc, h_conc, conc_df = CdTe_thermo.get_quenched_fermi_level_and_concentrations(\n", - " fermi_dost=fermi_dos, limit=\"Te-rich\", annealing_temperature=anneal_temp, delta_gap=band_gap_shift,\n", + " bulk_dos_vr=fermi_dos, limit=\"Te-rich\", annealing_temperature=anneal_temp, delta_gap=band_gap_shift,\n", " ) # Note that we now use `delta_gap` which specifies the change in gap from the original DFT DOS to the anneal temp\n", " scissored_dos = scissor_dos(delta_gap=band_gap_shift, dos=fermi_dos, verbose=False) # symmetrically-renormalised DOS -> to get the Fermi level & e/h concentrations at the annealing temperature:\n", " annealing_fermi_level, annealing_e_conc, annealing_h_conc = CdTe_thermo.get_equilibrium_fermi_level(\n", - " fermi_dos=scissored_dos, limit=\"Te-rich\", temperature=anneal_temp, return_concs=True,\n", + " bulk_dos_vr=scissored_dos, limit=\"Te-rich\", temperature=anneal_temp, return_concs=True,\n", " )\n", " \n", " annealing_dict[anneal_temp] = {\n", @@ -1405,7 +1435,7 @@ }, "outputs": [], "source": [ - "wienecke_data = np.array([\n", + "wienecke_data = np.array([ # Wienecke et al. 1993\n", "[675.644735186816, 15.19509584755584],\n", "[774.64775443452, 15.983458618047331],\n", "[773.2859479179771, 15.780402388747808],\n", @@ -1418,7 +1448,7 @@ "[1077.6449867907913, 17.335494943226077],\n", "[1082.4820732167568, 17.165318826904443],\n", " ])\n", - "emanuelsson_data = np.array([[750 + 273.15, np.log10(1.2e17)]])\n", + "emanuelsson_data = np.array([[750 + 273.15, np.log10(1.2e17)]]) # Emanuelsson et al. 1993\n", "expt_data = np.append(wienecke_data, emanuelsson_data, axis=0)" ] }, @@ -1577,12 +1607,12 @@ " for anneal_temp in anneal_temperatures:\n", " band_gap_shift = belas_linear_fit(anneal_temp) - 1.5 # 1.5 eV is our DFT (and room temp) gap\n", " fermi_level, e_conc, h_conc, conc_df = CdTe_thermo.get_quenched_fermi_level_and_concentrations(\n", - " fermi_dos=fermi_dos, chempots=relative_chempots, \n", + " bulk_dos_vr=fermi_dos, chempots=relative_chempots, \n", " annealing_temperature=anneal_temp, delta_gap=band_gap_shift,\n", " ) # Note that we now specify `chempots` rather than `limit` here!\n", " scissored_dos = scissor_dos(delta_gap=band_gap_shift, dos=fermi_dos, verbose=False) # symmetrically-renormalised DOS -> to get the Fermi level & e/h concentrations at the annealing temperature:\n", " annealing_fermi_level, annealing_e_conc, annealing_h_conc = CdTe_thermo.get_equilibrium_fermi_level(\n", - " fermi_dos=scissored_dos, chempots=relative_chempots, temperature=anneal_temp, return_concs=True,\n", + " bulk_dos_vr=scissored_dos, chempots=relative_chempots, temperature=anneal_temp, return_concs=True,\n", " )\n", " \n", " conc_dict = {\n", diff --git a/pyproject.toml b/pyproject.toml index 0334a0cf..2e991e5c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "doped" -version = "2.4.0" +version = "2.4.1" description = "Python package to setup, process and analyse solid-state defect calculations with VASP" authors = [{name = "Seán Kavanagh", email = "skavanagh@seas.harvard.edu"}] readme = "README.md" diff --git a/tests/data/CdTe/CdTe_prim_k101010_dos_vr.xml.gz b/tests/data/CdTe/CdTe_prim_k101010_dos_vr.xml.gz new file mode 100755 index 00000000..b86c1862 Binary files /dev/null and b/tests/data/CdTe/CdTe_prim_k101010_dos_vr.xml.gz differ diff --git a/tests/data/remote_baseline_plots/CdTe_v_cd_m2_eigenvalue_plot.png b/tests/data/remote_baseline_plots/CdTe_v_cd_m2_eigenvalue_plot.png index 51c248d3..1b428e6e 100644 Binary files a/tests/data/remote_baseline_plots/CdTe_v_cd_m2_eigenvalue_plot.png and b/tests/data/remote_baseline_plots/CdTe_v_cd_m2_eigenvalue_plot.png differ diff --git a/tests/data/remote_baseline_plots/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png b/tests/data/remote_baseline_plots/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png index 059f9c04..c4c06fdf 100644 Binary files a/tests/data/remote_baseline_plots/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png and b/tests/data/remote_baseline_plots/Cu2SiSe3_v_Cu_0_eigenvalue_plot.png differ diff --git a/tests/dope_potcar_tests.ipynb b/tests/dope_potcar_tests.ipynb index f4495e05..6d3c6e72 100644 --- a/tests/dope_potcar_tests.ipynb +++ b/tests/dope_potcar_tests.ipynb @@ -13,13 +13,26 @@ }, { "cell_type": "code", - "execution_count": 1, + "source": [ + "from pymatgen.core.structure import Structure\n", + "from doped.generation import DefectsGenerator\n", + "\n", + "relaxed_primitive_CdTe = Structure.from_file(\"../examples/CdTe/relaxed_primitive_POSCAR\")\n", + "defect_gen = DefectsGenerator(relaxed_primitive_CdTe)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T20:34:05.619409Z", + "start_time": "2024-04-10T20:33:41.367733Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generating DefectEntry objects: 100.0%|██████████| [00:01, 70.27it/s]" + "Generating DefectEntry objects: 100.0%|██████████| [00:11, 8.67it/s] " ] }, { @@ -27,23 +40,23 @@ "output_type": "stream", "text": [ "Vacancies Guessed Charges Conv. Cell Coords Wyckoff\n", - "----------- --------------- ------------------- ---------\n", - "v_Cd [-2,-1,0,+1] [0.000,0.000,0.000] 4a\n", - "v_Te [-1,0,+1,+2] [0.250,0.250,0.250] 4c\n", + "----------- ----------------- ------------------- ---------\n", + "v_Cd [+1,0,-1,-2] [0.000,0.000,0.000] 4a\n", + "v_Te [+2,+1,0,-1] [0.250,0.250,0.250] 4c\n", "\n", - "Substitutions Guessed Charges Conv. Cell Coords Wyckoff\n", + "Substitutions Guessed Charges Conv. Cell Coords Wyckoff\n", "--------------- --------------------- ------------------- ---------\n", - "Cd_Te [0,+1,+2,+3,+4] [0.250,0.250,0.250] 4c\n", - "Te_Cd [-4,-3,-2,-1,0,+1,+2] [0.000,0.000,0.000] 4a\n", + "Cd_Te [+4,+3,+2,+1,0] [0.250,0.250,0.250] 4c\n", + "Te_Cd [+2,+1,0,-1,-2,-3,-4] [0.000,0.000,0.000] 4a\n", "\n", - "Interstitials Guessed Charges Conv. Cell Coords Wyckoff\n", + "Interstitials Guessed Charges Conv. Cell Coords Wyckoff\n", "--------------- --------------------- ------------------- ---------\n", - "Cd_i_C3v [0,+1,+2] [0.625,0.625,0.625] 16e\n", - "Cd_i_Td_Cd2.83 [0,+1,+2] [0.750,0.750,0.750] 4d\n", - "Cd_i_Td_Te2.83 [0,+1,+2] [0.500,0.500,0.500] 4b\n", - "Te_i_C3v [-2,-1,0,+1,+2,+3,+4] [0.625,0.625,0.625] 16e\n", - "Te_i_Td_Cd2.83 [-2,-1,0,+1,+2,+3,+4] [0.750,0.750,0.750] 4d\n", - "Te_i_Td_Te2.83 [-2,-1,0,+1,+2,+3,+4] [0.500,0.500,0.500] 4b\n", + "Cd_i_C3v [+2,+1,0] [0.625,0.625,0.625] 16e\n", + "Cd_i_Td_Cd2.83 [+2,+1,0] [0.750,0.750,0.750] 4d\n", + "Cd_i_Td_Te2.83 [+2,+1,0] [0.500,0.500,0.500] 4b\n", + "Te_i_C3v [+4,+3,+2,+1,0,-1,-2] [0.625,0.625,0.625] 16e\n", + "Te_i_Td_Cd2.83 [+4,+3,+2,+1,0,-1,-2] [0.750,0.750,0.750] 4d\n", + "Te_i_Td_Te2.83 [+4,+3,+2,+1,0,-1,-2] [0.500,0.500,0.500] 4b\n", "\n", "The number in the Wyckoff label is the site multiplicity/degeneracy of that defect in the conventional ('conv.') unit cell, which comprises 4 formula unit(s) of CdTe.\n", "Note that Wyckoff letters can depend on the ordering of elements in the conventional standard structure, for which doped uses the spglib convention.\n" @@ -57,43 +70,52 @@ ] } ], - "source": [ - "from pymatgen.core.structure import Structure\n", - "from doped.generation import DefectsGenerator\n", - "\n", - "relaxed_primitive_CdTe = Structure.from_file(\"../examples/CdTe/relaxed_primitive_POSCAR\")\n", - "defect_gen = DefectsGenerator(relaxed_primitive_CdTe)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-07T09:26:09.819686Z", - "start_time": "2023-09-07T09:26:04.393988Z" - } - } + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { - "end_time": "2023-09-07T09:26:09.885533Z", - "start_time": "2023-09-07T09:26:09.820782Z" + "end_time": "2024-04-10T20:34:05.767167Z", + "start_time": "2024-04-10T20:34:05.621443Z" } }, - "outputs": [], "source": [ "from doped.vasp import DefectsSet\n", "ds = DefectsSet(defect_gen, user_incar_settings={\"Whoops\": \"lol\"})\n", "# doped should warn me if I put in a whack INCAR tag (or misspell)" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n", + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n", + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n", + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n", + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n", + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n" + ] + } + ], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, + "source": [ + "ds.defect_sets[\"v_Cd_-2\"].vasp_std" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T20:34:05.792944Z", + "start_time": "2024-04-10T20:34:05.767970Z" + } + }, "outputs": [ { "name": "stderr", @@ -104,51 +126,63 @@ }, { "data": { - "text/plain": "DefectDictSet" + "text/plain": [ + "doped DefectDictSet with supercell composition Cd26 Te27. Available attributes:\n", + "{'charge_state', 'auto_ismear', 'international_monoclinic', 'prev_vasprun', 'nelect', 'user_potcar_functional', 'potcar_symbols', 'incar', 'constrain_total_magmom', 'user_potcar_settings', 'sort_structure', 'user_kpoints_settings', 'sym_prec', 'config_dict', 'inherit_incar', 'incar_updates', 'potcar', 'poscar_comment', 'vdw', 'prev_incar', 'standardize', 'potcar_functional', 'validate_magmom', 'user_incar_settings', 'bandgap_tol', 'files_to_transfer', 'prev_outcar', 'poscar', 'force_gamma', 'bandgap', 'auto_kpar', 'kpoints', 'structure', 'use_structure_charge', 'reduce_structure', 'kpoints_updates', 'prev_kpoints'}\n", + "\n", + "Available methods:\n", + "{'get_input_set', 'validate_monty_v2', 'calculate_ng', 'override_from_prev_calc', 'validate_monty_v1', 'from_dict', 'unsafe_hash', 'write_input', 'estimate_nbands', 'get_vasp_input', 'as_dict', 'from_prev_calc', 'to_json'}" + ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ds.defect_sets[\"v_Cd_-2\"].vasp_std" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-07T09:26:09.891001Z", - "start_time": "2023-09-07T09:26:09.885731Z" - } - } + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, - "outputs": [], "source": [ "ds.defect_sets[\"v_Cd_-2\"].write_gam()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:26:10.244340Z", - "start_time": "2023-09-07T09:26:09.890477Z" + "end_time": "2024-04-10T20:34:05.905512Z", + "start_time": "2024-04-10T20:34:05.793997Z" } - } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cannot find Whoops from your user_incar_settings in the list of INCAR flags\n" + ] + } + ], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { - "end_time": "2023-09-07T09:26:10.967828Z", - "start_time": "2023-09-07T09:26:10.247355Z" + "end_time": "2024-04-10T20:34:06.629257Z", + "start_time": "2024-04-10T20:34:05.908187Z" } }, + "source": [ + "!grep PAW_PBE v_Cd_-2/vasp_gam/POTCAR\n", + "!grep ZVAL v_Cd_-2/vasp_gam/POTCAR\n", + "print(f\"NELECT should be: {31*12 + 32*6 + 2}\")\n", + "!grep NELECT v_Cd_-2/vasp_gam/INCAR\n", + "!grep NUP v_Cd_-2/vasp_gam/INCAR\n", + "!grep EDIFF v_Cd_-2/vasp_gam/INCAR" + ], "outputs": [ { "name": "stdout", @@ -161,180 +195,190 @@ " POMASS = 112.411; ZVAL = 12.000 mass and valenz\r\n", " POMASS = 127.600; ZVAL = 6.000 mass and valenz\r\n", "NELECT should be: 566\n", - "NELECT = 566.0\r\n", - "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN = Typical variable parameters\r\n", + "NELECT = 476.0\r\n", + "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM = Typical variable parameters\r\n", "NUPDOWN = 0\r\n", "EDIFF = 1e-05\r\n", "EDIFFG = -0.01\r\n" ] } ], - "source": [ - "!grep PAW_PBE v_Cd_-2/vasp_gam/POTCAR\n", - "!grep ZVAL v_Cd_-2/vasp_gam/POTCAR\n", - "print(f\"NELECT should be: {31*12 + 32*6 + 2}\")\n", - "!grep NELECT v_Cd_-2/vasp_gam/INCAR\n", - "!grep NUP v_Cd_-2/vasp_gam/INCAR\n", - "!grep EDIFF v_Cd_-2/vasp_gam/INCAR" - ] + "execution_count": 5 }, { "cell_type": "code", - "execution_count": 15, - "outputs": [], "source": [ "ds = DefectsSet(defect_gen, user_potcar_settings={\"POTCAR\":{\"Cd\": \"Cd_sv_GW\"}})" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:26:38.112748Z", - "start_time": "2023-09-07T09:26:38.109647Z" + "end_time": "2024-04-10T20:34:55.318684Z", + "start_time": "2024-04-10T20:34:55.171175Z" } - } + }, + "outputs": [], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": 16, - "outputs": [], "source": [ "ds.defect_sets[\"v_Cd_-2\"].write_gam()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:26:38.675503Z", - "start_time": "2023-09-07T09:26:38.248394Z" + "end_time": "2024-04-10T20:34:56.021623Z", + "start_time": "2024-04-10T20:34:55.971166Z" } - } + }, + "outputs": [], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 17, + "source": [ + "!grep PAW_PBE v_Cd_-2/vasp_gam/POTCAR\n", + "!grep ZVAL v_Cd_-2/vasp_gam/POTCAR\n", + "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", + "!grep NELECT v_Cd_-2/vasp_gam/INCAR\n", + "!grep NUP v_Cd_-2/vasp_gam/INCAR\n", + "!grep EDIFF v_Cd_-2/vasp_gam/INCAR" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T20:34:57.184342Z", + "start_time": "2024-04-10T20:34:56.453901Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + " PAW_PBE Cd_sv_GW 16Apr2014 \r\n", + " TITEL = PAW_PBE Cd_sv_GW 16Apr2014\r\n", " PAW_PBE Te 08Apr2002 \r\n", " TITEL = PAW_PBE Te 08Apr2002\r\n", " POMASS = 112.411; ZVAL = 20.000 mass and valenz\r\n", " POMASS = 127.600; ZVAL = 6.000 mass and valenz\r\n", "NELECT should be: 814\n", - "NELECT = 814.0\r\n", - "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN = Typical variable parameters\r\n", + "NELECT = 684.0\r\n", + "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM = Typical variable parameters\r\n", "NUPDOWN = 0\r\n", "EDIFF = 1e-05\r\n", "EDIFFG = -0.01\r\n" ] } ], + "execution_count": 8 + }, + { + "cell_type": "code", "source": [ - "!grep PAW_PBE v_Cd_-2/vasp_gam/POTCAR\n", - "!grep ZVAL v_Cd_-2/vasp_gam/POTCAR\n", - "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", - "!grep NELECT v_Cd_-2/vasp_gam/INCAR\n", - "!grep NUP v_Cd_-2/vasp_gam/INCAR\n", - "!grep EDIFF v_Cd_-2/vasp_gam/INCAR" + "ds.defect_sets[\"v_Cd_-2\"].write_nkred_std()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:26:39.825864Z", - "start_time": "2023-09-07T09:26:39.169833Z" + "end_time": "2024-04-10T20:34:58.657044Z", + "start_time": "2024-04-10T20:34:58.621384Z" } - } + }, + "outputs": [], + "execution_count": 9 }, { "cell_type": "code", - "execution_count": 18, - "outputs": [], "source": [ - "ds.defect_sets[\"v_Cd_-2\"].write_nkred_std()" + "!grep PAW_PBE v_Cd_-2/vasp_nkred_std/POTCAR\n", + "!grep ZVAL v_Cd_-2/vasp_nkred_std/POTCAR\n", + "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", + "!grep NELECT v_Cd_-2/vasp_nkred_std/INCAR\n", + "!grep NUP v_Cd_-2/vasp_nkred_std/INCAR\n", + "!grep EDIFF v_Cd_-2/vasp_nkred_std/INCAR\n", + "!grep NKRED v_Cd_-2/vasp_nkred_std/INCAR\n", + "!grep KPAR v_Cd_-2/vasp_nkred_std/INCAR" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:27:01.046362Z", - "start_time": "2023-09-07T09:27:00.774243Z" + "end_time": "2024-04-10T20:34:59.749020Z", + "start_time": "2024-04-10T20:34:58.825714Z" } - } - }, - { - "cell_type": "code", - "execution_count": 20, + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + " PAW_PBE Cd_sv_GW 16Apr2014 \r\n", + " TITEL = PAW_PBE Cd_sv_GW 16Apr2014\r\n", " PAW_PBE Te 08Apr2002 \r\n", " TITEL = PAW_PBE Te 08Apr2002\r\n", " POMASS = 112.411; ZVAL = 20.000 mass and valenz\r\n", " POMASS = 127.600; ZVAL = 6.000 mass and valenz\r\n", "NELECT should be: 814\n", - "NELECT = 814.0\r\n", - "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN = Typical variable parameters\r\n", + "NELECT = 684.0\r\n", + "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM = Typical variable parameters\r\n", "NUPDOWN = 0\r\n", "EDIFF = 1e-05\r\n", "EDIFFG = -0.01\r\n", "NKRED = 2\r\n", - "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN = Typical variable parameters\r\n", - "KPAR = 2\r\n" + "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM = Typical variable parameters\r\n", + "KPAR = 4 # 2 or >=4 k-points in at least two directions\r\n" ] } ], + "execution_count": 10 + }, + { + "cell_type": "code", "source": [ - "!grep PAW_PBE v_Cd_-2/vasp_nkred_std/POTCAR\n", - "!grep ZVAL v_Cd_-2/vasp_nkred_std/POTCAR\n", - "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", - "!grep NELECT v_Cd_-2/vasp_nkred_std/INCAR\n", - "!grep NUP v_Cd_-2/vasp_nkred_std/INCAR\n", - "!grep EDIFF v_Cd_-2/vasp_nkred_std/INCAR\n", - "!grep NKRED v_Cd_-2/vasp_nkred_std/INCAR\n", - "!grep KPAR v_Cd_-2/vasp_nkred_std/INCAR" + "ds = DefectsSet(defect_gen, user_kpoints_settings={\"reciprocal_density\": 1000})" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:27:33.403880Z", - "start_time": "2023-09-07T09:27:32.489830Z" + "end_time": "2024-04-10T20:35:00.480618Z", + "start_time": "2024-04-10T20:35:00.449080Z" } - } + }, + "outputs": [], + "execution_count": 11 }, { "cell_type": "code", - "execution_count": 23, - "outputs": [], "source": [ - "ds = DefectsSet(defect_gen, user_kpoints_settings={\"reciprocal_density\": 1000})" + "ds.defect_sets[\"v_Cd_-2\"].write_ncl()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:29:07.643677Z", - "start_time": "2023-09-07T09:29:07.638480Z" + "end_time": "2024-04-10T20:35:00.631771Z", + "start_time": "2024-04-10T20:35:00.604420Z" } - } + }, + "outputs": [], + "execution_count": 12 }, { "cell_type": "code", - "execution_count": 24, - "outputs": [], "source": [ - "ds.defect_sets[\"v_Cd_-2\"].write_ncl()" + "!grep PAW_PBE v_Cd_-2/vasp_ncl/POTCAR\n", + "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", + "!grep NELECT v_Cd_-2/vasp_ncl/INCAR\n", + "!grep NUP v_Cd_-2/vasp_ncl/INCAR\n", + "!cat v_Cd_-2/vasp_ncl/KPOINTS" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-07T09:29:08.423270Z", - "start_time": "2023-09-07T09:29:08.135259Z" + "end_time": "2024-04-10T20:35:01.702925Z", + "start_time": "2024-04-10T20:35:01.116045Z" } - } - }, - { - "cell_type": "code", - "execution_count": 25, + }, "outputs": [ { "name": "stdout", @@ -345,30 +389,17 @@ " PAW_PBE Te 08Apr2002 \r\n", " TITEL = PAW_PBE Te 08Apr2002\r\n", "NELECT should be: 814\n", - "NELECT = 566.0\r\n", - "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN = Typical variable parameters\r\n", + "NELECT = 476.0\r\n", + "# May want to change NCORE, KPAR, AEXX, ENCUT, IBRION, LREAL, NUPDOWN, ISPIN, MAGMOM = Typical variable parameters\r\n", "NUPDOWN = 0\r\n", "KPOINTS from doped, with reciprocal_density = 1000/Å⁻³\r\n", "0\r\n", "Gamma\r\n", - "4 4 4\r\n" + "5 5 5\r\n" ] } ], - "source": [ - "!grep PAW_PBE v_Cd_-2/vasp_ncl/POTCAR\n", - "print(f\"NELECT should be: {31*20 + 32*6 + 2}\")\n", - "!grep NELECT v_Cd_-2/vasp_ncl/INCAR\n", - "!grep NUP v_Cd_-2/vasp_ncl/INCAR\n", - "!cat v_Cd_-2/vasp_ncl/KPOINTS" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-07T09:30:05.088638Z", - "start_time": "2023-09-07T09:30:04.583849Z" - } - } + "execution_count": 13 }, { "cell_type": "code", @@ -392,16 +423,19 @@ }, { "cell_type": "code", - "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { - "end_time": "2023-09-07T09:33:23.990277Z", - "start_time": "2023-09-07T09:33:23.924556Z" + "end_time": "2024-04-10T20:35:04.621195Z", + "start_time": "2024-04-10T20:35:04.571784Z" } }, + "source": [ + "from shakenbreak.input import Distortions\n", + "Dist = Distortions(defect_gen)" + ], "outputs": [ { "name": "stdout", @@ -411,14 +445,20 @@ ] } ], - "source": [ - "from shakenbreak.input import Distortions\n", - "Dist = Distortions(defect_gen)" - ] + "execution_count": 14 }, { "cell_type": "code", - "execution_count": 28, + "source": [ + "defects_dict, distortion_metadata = Dist.write_vasp_files()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-04-10T20:35:26.152399Z", + "start_time": "2024-04-10T20:35:08.086314Z" + } + }, "outputs": [ { "name": "stdout", @@ -430,152 +470,136 @@ "Defect: v_Cd\u001B[0m\n", "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect v_Cd in charge state: -2. Number of distorted neighbours: 0\n", - "\n", - "Defect v_Cd in charge state: -1. Number of distorted neighbours: 1\n", + "Defect v_Cd in charge state: +1. Number of distorted neighbours: 3\n", "\n", "Defect v_Cd in charge state: 0. Number of distorted neighbours: 2\n", "\n", - "Defect v_Cd in charge state: +1. Number of distorted neighbours: 3\n", + "Defect v_Cd in charge state: -1. Number of distorted neighbours: 1\n", + "\n", + "Defect v_Cd in charge state: -2. Number of distorted neighbours: 0\n", "\u001B[1m\n", "Defect: v_Te\u001B[0m\n", "\u001B[1mNumber of extra electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect v_Te in charge state: -1. Number of distorted neighbours: 3\n", - "\n", - "Defect v_Te in charge state: 0. Number of distorted neighbours: 2\n", + "Defect v_Te in charge state: +2. Number of distorted neighbours: 0\n", "\n", "Defect v_Te in charge state: +1. Number of distorted neighbours: 1\n", "\n", - "Defect v_Te in charge state: +2. Number of distorted neighbours: 0\n", + "Defect v_Te in charge state: 0. Number of distorted neighbours: 2\n", + "\n", + "Defect v_Te in charge state: -1. Number of distorted neighbours: 3\n", "\u001B[1m\n", "Defect: Cd_Te\u001B[0m\n", "\u001B[1mNumber of extra electrons in neutral state: 4\u001B[0m\n", "\n", - "Defect Cd_Te in charge state: 0. Number of distorted neighbours: 4\n", + "Defect Cd_Te in charge state: +4. Number of distorted neighbours: 0\n", "\n", - "Defect Cd_Te in charge state: +1. Number of distorted neighbours: 3\n", + "Defect Cd_Te in charge state: +3. Number of distorted neighbours: 1\n", "\n", "Defect Cd_Te in charge state: +2. Number of distorted neighbours: 2\n", "\n", - "Defect Cd_Te in charge state: +3. Number of distorted neighbours: 1\n", + "Defect Cd_Te in charge state: +1. Number of distorted neighbours: 3\n", "\n", - "Defect Cd_Te in charge state: +4. Number of distorted neighbours: 0\n", + "Defect Cd_Te in charge state: 0. Number of distorted neighbours: 4\n", "\u001B[1m\n", "Defect: Te_Cd\u001B[0m\n", "\u001B[1mNumber of missing electrons in neutral state: 4\u001B[0m\n", "\n", - "Defect Te_Cd in charge state: -4. Number of distorted neighbours: 0\n", + "Defect Te_Cd in charge state: +2. Number of distorted neighbours: 2\n", "\n", - "Defect Te_Cd in charge state: -3. Number of distorted neighbours: 1\n", + "Defect Te_Cd in charge state: +1. Number of distorted neighbours: 3\n", "\n", - "Defect Te_Cd in charge state: -2. Number of distorted neighbours: 2\n", + "Defect Te_Cd in charge state: 0. Number of distorted neighbours: 4\n", "\n", "Defect Te_Cd in charge state: -1. Number of distorted neighbours: 3\n", "\n", - "Defect Te_Cd in charge state: 0. Number of distorted neighbours: 4\n", + "Defect Te_Cd in charge state: -2. Number of distorted neighbours: 2\n", "\n", - "Defect Te_Cd in charge state: +1. Number of distorted neighbours: 3\n", + "Defect Te_Cd in charge state: -3. Number of distorted neighbours: 1\n", "\n", - "Defect Te_Cd in charge state: +2. Number of distorted neighbours: 2\n", + "Defect Te_Cd in charge state: -4. Number of distorted neighbours: 0\n", "\u001B[1m\n", "Defect: Cd_i_C3v\u001B[0m\n", "\u001B[1mNumber of extra electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Cd_i_C3v in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Cd_i_C3v in charge state: +2. Number of distorted neighbours: 0\n", "\n", "Defect Cd_i_C3v in charge state: +1. Number of distorted neighbours: 1\n", "\n", - "Defect Cd_i_C3v in charge state: +2. Number of distorted neighbours: 0\n", + "Defect Cd_i_C3v in charge state: 0. Number of distorted neighbours: 2\n", "\u001B[1m\n", "Defect: Cd_i_Td_Cd2.83\u001B[0m\n", "\u001B[1mNumber of extra electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Cd_i_Td_Cd2.83 in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Cd_i_Td_Cd2.83 in charge state: +2. Number of distorted neighbours: 0\n", "\n", "Defect Cd_i_Td_Cd2.83 in charge state: +1. Number of distorted neighbours: 1\n", "\n", - "Defect Cd_i_Td_Cd2.83 in charge state: +2. Number of distorted neighbours: 0\n", + "Defect Cd_i_Td_Cd2.83 in charge state: 0. Number of distorted neighbours: 2\n", "\u001B[1m\n", "Defect: Cd_i_Td_Te2.83\u001B[0m\n", "\u001B[1mNumber of extra electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Cd_i_Td_Te2.83 in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Cd_i_Td_Te2.83 in charge state: +2. Number of distorted neighbours: 0\n", "\n", "Defect Cd_i_Td_Te2.83 in charge state: +1. Number of distorted neighbours: 1\n", "\n", - "Defect Cd_i_Td_Te2.83 in charge state: +2. Number of distorted neighbours: 0\n", + "Defect Cd_i_Td_Te2.83 in charge state: 0. Number of distorted neighbours: 2\n", "\u001B[1m\n", "Defect: Te_i_C3v\u001B[0m\n", "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Te_i_C3v in charge state: -2. Number of distorted neighbours: 0\n", + "Defect Te_i_C3v in charge state: +4. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_C3v in charge state: -1. Number of distorted neighbours: 1\n", + "Defect Te_i_C3v in charge state: +3. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_C3v in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Te_i_C3v in charge state: +2. Number of distorted neighbours: 4\n", "\n", "Defect Te_i_C3v in charge state: +1. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_C3v in charge state: +2. Number of distorted neighbours: 4\n", + "Defect Te_i_C3v in charge state: 0. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_C3v in charge state: +3. Number of distorted neighbours: 3\n", + "Defect Te_i_C3v in charge state: -1. Number of distorted neighbours: 1\n", "\n", - "Defect Te_i_C3v in charge state: +4. Number of distorted neighbours: 2\n", + "Defect Te_i_C3v in charge state: -2. Number of distorted neighbours: 0\n", "\u001B[1m\n", "Defect: Te_i_Td_Cd2.83\u001B[0m\n", "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: -2. Number of distorted neighbours: 0\n", + "Defect Te_i_Td_Cd2.83 in charge state: +4. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: -1. Number of distorted neighbours: 1\n", + "Defect Te_i_Td_Cd2.83 in charge state: +3. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Te_i_Td_Cd2.83 in charge state: +2. Number of distorted neighbours: 4\n", "\n", "Defect Te_i_Td_Cd2.83 in charge state: +1. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: +2. Number of distorted neighbours: 4\n", + "Defect Te_i_Td_Cd2.83 in charge state: 0. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: +3. Number of distorted neighbours: 3\n", + "Defect Te_i_Td_Cd2.83 in charge state: -1. Number of distorted neighbours: 1\n", "\n", - "Defect Te_i_Td_Cd2.83 in charge state: +4. Number of distorted neighbours: 2\n", + "Defect Te_i_Td_Cd2.83 in charge state: -2. Number of distorted neighbours: 0\n", "\u001B[1m\n", "Defect: Te_i_Td_Te2.83\u001B[0m\n", "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: -2. Number of distorted neighbours: 0\n", + "Defect Te_i_Td_Te2.83 in charge state: +4. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: -1. Number of distorted neighbours: 1\n", + "Defect Te_i_Td_Te2.83 in charge state: +3. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: 0. Number of distorted neighbours: 2\n", + "Defect Te_i_Td_Te2.83 in charge state: +2. Number of distorted neighbours: 4\n", "\n", "Defect Te_i_Td_Te2.83 in charge state: +1. Number of distorted neighbours: 3\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: +2. Number of distorted neighbours: 4\n", + "Defect Te_i_Td_Te2.83 in charge state: 0. Number of distorted neighbours: 2\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: +3. Number of distorted neighbours: 3\n", + "Defect Te_i_Td_Te2.83 in charge state: -1. Number of distorted neighbours: 1\n", "\n", - "Defect Te_i_Td_Te2.83 in charge state: +4. Number of distorted neighbours: 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "input.py:306: UserWarning: A previously-generated defect folder v_Cd_-2 exists in tests, and the Unperturbed defect structure could not be matched to the current defect species: v_Cd_-2. These are assumed to be inequivalent defects, so the previous v_Cd_-2 will be renamed to v_Cda_-2 and ShakeNBreak files for the current defect will be saved to v_Cdb_-2, to prevent overwriting.\n" + "Defect Te_i_Td_Te2.83 in charge state: -2. Number of distorted neighbours: 0\n" ] } ], - "source": [ - "defects_dict, distortion_metadata = Dist.write_vasp_files()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-09-07T09:37:17.796027Z", - "start_time": "2023-09-07T09:33:42.000335Z" - } - } + "execution_count": 15 }, { "cell_type": "code", diff --git a/tests/test_analysis.py b/tests/test_analysis.py index 6093ac9b..98aefa41 100644 --- a/tests/test_analysis.py +++ b/tests/test_analysis.py @@ -15,7 +15,6 @@ import pytest from monty.serialization import dumpfn, loadfn from pymatgen.core.structure import Structure -from test_generation import _potcars_available from test_thermodynamics import custom_mpl_image_compare from doped.analysis import ( @@ -611,13 +610,13 @@ def test_DefectsParser_corrections_errors_warning(self): "Estimated error in the Freysoldt (FNV) ", "Estimated error in the Kumagai (eFNV) ", "charge correction for certain defects is greater than the `error_tolerance` (= " - "0.001 eV):", - "v_Cd_-2: 0.011 eV", - "v_Cd_-1: 0.008 eV", - "Int_Te_3_1: 0.003 eV", - "Te_Cd_+1: 0.002 eV", - "Int_Te_3_Unperturbed_1: 0.005 eV", - "Int_Te_3_2: 0.012 eV", + "1.00e-03 eV):", + "v_Cd_-2: 1.13e-02 eV", + "v_Cd_-1: 7.91e-03 eV", + "Int_Te_3_1: 3.10e-03 eV", + "Te_Cd_+1: 2.02e-03 eV", + "Int_Te_3_Unperturbed_1: 4.91e-03 eV", + "Int_Te_3_2: 1.24e-02 eV", "You may want to check the accuracy of the corrections by", "(using `defect_entry.get_freysoldt_correction()` with `plot=True`)", "(using `defect_entry.get_kumagai_correction()` with `plot=True`)", @@ -772,8 +771,27 @@ def test_extrinsic_Sb2Se3(self): for warn in w ) + # spot check: + assert np.isclose(Sb2Se3_O_thermo.get_formation_energy("O_Se_Cs_Sb2.02_-2"), -1.84684, atol=1e-3) + return Sb2Se3_O_thermo.plot(chempots={"O": -8.9052, "Se": -5}) # example chempots + def test_extrinsic_Sb2Se3_parsing_with_single_defect_dir(self): + with warnings.catch_warnings(record=True) as w: # no warning about negative corrections with + # strong anisotropic dielectric: + Sb2Se3_O_dp = DefectsParser( + output_path=f"{self.Sb2Se3_DATA_DIR}/defect/O_-2", + bulk_path=f"{self.Sb2Se3_DATA_DIR}/bulk", + dielectric=self.Sb2Se3_dielectric, + ) + print([warn.message for warn in w]) # for debugging + assert not w # no warnings + self._check_DefectsParser(Sb2Se3_O_dp) + Sb2Se3_O_thermo = Sb2Se3_O_dp.get_defect_thermodynamics() + assert np.isclose(Sb2Se3_O_thermo.get_formation_energy("O_Se_Cs_Sb2.02_-2"), -1.84684, atol=1e-3) + + assert len(Sb2Se3_O_thermo.defect_entries) == 1 # only the one specified defect parsed + @custom_mpl_image_compare(filename="Sb2Si2Te6_v_Sb_-3_eFNV_plot_no_intralayer.png") def test_sb2si2te6_eFNV(self): with warnings.catch_warnings(record=True) as w: @@ -1051,8 +1069,7 @@ def test_CaO_symmetry_determination(self): dp = DefectsParser( output_path=self.CaO_DATA_DIR, skip_corrections=True, - parse_projected_eigen=bool(_potcars_available()), - ) # only test projected eigenvalues if POTCARs available (i.e. locally) to save time + ) print([str(warning.message) for warning in w]) # for debugging assert not w @@ -1915,8 +1932,8 @@ def test_extrinsic_interstitial_parsing_and_kumagai(self): ) assert ( f"Estimated error in the Kumagai (eFNV) charge correction for defect " - f"{int_F_minus1_ent.name} is 0.003 eV (i.e. which is greater than the `error_tolerance`: " - f"0.001 eV). You may want to check the accuracy of the correction by plotting the site " + f"{int_F_minus1_ent.name} is 2.58e-03 eV (i.e. which is greater than the `error_tolerance`: " + f"1.00e-03 eV). You may want to check the accuracy of the correction by plotting the site " f"potential differences (using `defect_entry.get_kumagai_correction()` with " f"`plot=True`). Large errors are often due to unstable or shallow defect charge states (" f"which can't be accurately modelled with the supercell approach; see " @@ -2010,8 +2027,8 @@ def test_extrinsic_substitution_parsing_and_freysoldt_and_kumagai(self): print([str(warn.message) for warn in w]) assert any( f"Estimated error in the Freysoldt (FNV) charge correction for defect {F_O_1_ent.name} is " - f"0.000 eV (i.e. which is greater than the `error_tolerance`: 0.000 eV). You may want to " - f"check the accuracy of the correction by plotting the site potential differences (using " + f"3.54e-04 eV (i.e. which is greater than the `error_tolerance`: 1.00e-05 eV). You may want " + f"to check the accuracy of the correction by plotting the site potential differences (using " f"`defect_entry.get_freysoldt_correction()` with `plot=True`). Large errors are often due " f"to unstable or shallow defect charge states (which can't be accurately modelled with " f"the supercell approach; see " @@ -2254,53 +2271,6 @@ def _remove_metadata_keys_from_dict(d: dict) -> dict: return d -def _compare_band_edge_states_dicts(d1, d2, orb_diff_tol: float = 0.1): - """ - Compare two dictionaries of band edge states, removing metadata keys and - allowing a slight difference in the ``vbm/cbm_orbital_diff`` values to - account for rounding errors with ``PROCAR``s. - """ - if isinstance(d1, str): - d1 = loadfn(d1) - if isinstance(d2, str): - d2 = loadfn(d2) - - d1 = d1.as_dict() - d2 = d2.as_dict() - - cbm_orbital_diffs1 = [subdict.pop("cbm_orbital_diff") for subdict in d1["states"]] - cbm_orbital_diffs2 = [subdict.pop("cbm_orbital_diff") for subdict in d2["states"]] - for i, j in zip(cbm_orbital_diffs1, cbm_orbital_diffs2): - print(f"cbm_orbital_diffs: {i:.3f} vs {j:.3f}") - assert np.isclose(i, j, atol=orb_diff_tol) - vbm_orbital_diffs1 = [subdict.pop("vbm_orbital_diff") for subdict in d1["states"]] - vbm_orbital_diffs2 = [subdict.pop("vbm_orbital_diff") for subdict in d2["states"]] - for i, j in zip(vbm_orbital_diffs1, vbm_orbital_diffs2): - print(f"vbm_orbital_diffs: {i:.3f} vs {j:.3f}") - assert np.isclose(i, j, atol=orb_diff_tol) - - orb_infos_orbitals1 = [ - subdict["vbm_info"]["orbital_info"].pop("orbitals") for subdict in d1["states"] - ] + [subdict["cbm_info"]["orbital_info"].pop("orbitals") for subdict in d1["states"]] - orb_infos_orbitals2 = [ - subdict["vbm_info"]["orbital_info"].pop("orbitals") for subdict in d2["states"] - ] + [subdict["cbm_info"]["orbital_info"].pop("orbitals") for subdict in d2["states"]] - for i, j in zip(orb_infos_orbitals1, orb_infos_orbitals2): - print(f"orbital_info_orbitals: {i} vs {j}") - for k, v in i.items(): - assert np.allclose(v, j[k], atol=orb_diff_tol) - - participation_ratio1 = [ - subdict["vbm_info"]["orbital_info"].pop("participation_ratio") for subdict in d1["states"] - ] + [subdict["cbm_info"]["orbital_info"].pop("participation_ratio") for subdict in d1["states"]] - participation_ratio2 = [ - subdict["vbm_info"]["orbital_info"].pop("participation_ratio") for subdict in d2["states"] - ] + [subdict["cbm_info"]["orbital_info"].pop("participation_ratio") for subdict in d2["states"]] - assert np.allclose(participation_ratio1, participation_ratio2, atol=orb_diff_tol) - - assert _remove_metadata_keys_from_dict(d1) == _remove_metadata_keys_from_dict(d2) - - class DopedParsingFunctionsTestCase(unittest.TestCase): def setUp(self): DopedParsingTestCase.setUp(self) # get attributes from DopedParsingTestCase @@ -2723,6 +2693,80 @@ def test_eigenvalues_parsing_and_warnings(self): Print statements added because dict comparison doesn't give verbose output on exact location of failure. """ + + def _compare_band_edge_states_dicts(d1, d2, orb_diff_tol: float = 0.1): + """ + Compare two dictionaries of band edge states, removing metadata + keys and allowing a slight difference in the + ``vbm/cbm_orbital_diff`` values to account for rounding errors with + ``PROCAR``s. + """ + if isinstance(d1, str): + d1 = loadfn(d1) + if isinstance(d2, str): + d2 = loadfn(d2) + + d1 = d1.as_dict() + d2 = d2.as_dict() + + cbm_orbital_diffs1 = [subdict.pop("cbm_orbital_diff") for subdict in d1["states"]] + cbm_orbital_diffs2 = [subdict.pop("cbm_orbital_diff") for subdict in d2["states"]] + for i, j in zip(cbm_orbital_diffs1, cbm_orbital_diffs2): + print(f"cbm_orbital_diffs: {i:.3f} vs {j:.3f}") + assert np.isclose(i, j, atol=orb_diff_tol) + vbm_orbital_diffs1 = [subdict.pop("vbm_orbital_diff") for subdict in d1["states"]] + vbm_orbital_diffs2 = [subdict.pop("vbm_orbital_diff") for subdict in d2["states"]] + for i, j in zip(vbm_orbital_diffs1, vbm_orbital_diffs2): + print(f"vbm_orbital_diffs: {i:.3f} vs {j:.3f}") + assert np.isclose(i, j, atol=orb_diff_tol) + + orb_infos_orbitals1 = [ + subdict["vbm_info"]["orbital_info"].pop("orbitals") for subdict in d1["states"] + ] + [subdict["cbm_info"]["orbital_info"].pop("orbitals") for subdict in d1["states"]] + orb_infos_orbitals2 = [ + subdict["vbm_info"]["orbital_info"].pop("orbitals") for subdict in d2["states"] + ] + [subdict["cbm_info"]["orbital_info"].pop("orbitals") for subdict in d2["states"]] + for i, j in zip(orb_infos_orbitals1, orb_infos_orbitals2): + print(f"orbital_info_orbitals: {i} vs {j}") + for k, v in i.items(): + assert np.allclose(v, j[k], atol=orb_diff_tol) + + participation_ratio1 = ( + [ + subdict["vbm_info"]["orbital_info"].pop("participation_ratio") + for subdict in d1["states"] + ] + + [ + subdict["cbm_info"]["orbital_info"].pop("participation_ratio") + for subdict in d1["states"] + ] + + [ + subsubdict.pop("participation_ratio") + for subdict in d1["states"] + for subsubdict in subdict["localized_orbitals"] + ] + ) + + participation_ratio2 = ( + [ + subdict["vbm_info"]["orbital_info"].pop("participation_ratio") + for subdict in d2["states"] + ] + + [ + subdict["cbm_info"]["orbital_info"].pop("participation_ratio") + for subdict in d2["states"] + ] + + [ + subsubdict.pop("participation_ratio") + for subdict in d2["states"] + for subsubdict in subdict["localized_orbitals"] + ] + ) + print(f"participation_ratio: {participation_ratio1} vs {participation_ratio2}") + assert np.allclose(participation_ratio1, participation_ratio2, atol=orb_diff_tol) + + assert _remove_metadata_keys_from_dict(d1) == _remove_metadata_keys_from_dict(d2) + # Test loading of MgO using vasprun.xml defect_entry = DefectParser.from_paths( f"{self.MgO_EXAMPLE_DIR}/Defects/Mg_O_+1/vasp_std", @@ -2735,7 +2779,7 @@ def test_eigenvalues_parsing_and_warnings(self): bes, fig = defect_entry.get_eigenvalue_analysis() # Test plotting KS Mg_O_1_bes_path = f"{self.MgO_EXAMPLE_DIR}/Defects/Mg_O_1_band_edge_states.json" # dumpfn(bes, Mg_O_1_bes_path) # for saving test data - _compare_band_edge_states_dicts(bes, Mg_O_1_bes_path, orb_diff_tol=0.001) + _compare_band_edge_states_dicts(bes, Mg_O_1_bes_path, orb_diff_tol=0.01) assert bes.has_occupied_localized_state assert not any( [bes.has_acceptor_phs, bes.has_donor_phs, bes.has_unoccupied_localized_state, bes.is_shallow] @@ -2771,7 +2815,7 @@ def test_eigenvalues_parsing_and_warnings(self): bes = dp.defect_dict["Si_i_-1"].get_eigenvalue_analysis(plot=False) Si_i_m1_bes_path = f"{self.Cu2SiSe3_EXAMPLE_DIR}/Cu2SiSe3_int_band_edge_states.json" # dumpfn(bes, Si_i_m1_bes_path) # for saving test data - _compare_band_edge_states_dicts(bes, Si_i_m1_bes_path, orb_diff_tol=0.001) + _compare_band_edge_states_dicts(bes, Si_i_m1_bes_path, orb_diff_tol=0.01) assert bes.has_occupied_localized_state assert not any( [bes.has_acceptor_phs, bes.has_donor_phs, bes.has_unoccupied_localized_state, bes.is_shallow] @@ -2853,7 +2897,7 @@ def test_eigenvalues_parsing_and_warnings(self): bes = defect_entry.get_eigenvalue_analysis(plot=False) Te_i_1_SOC_bes_path = f"{self.CdTe_EXAMPLE_DIR}/CdTe_test_soc_band_edge_states.json" # dumpfn(bes, Te_i_1_SOC_bes_path) # for saving test data - _compare_band_edge_states_dicts(bes, Te_i_1_SOC_bes_path, orb_diff_tol=0.001) + _compare_band_edge_states_dicts(bes, Te_i_1_SOC_bes_path, orb_diff_tol=0.02) assert bes.has_unoccupied_localized_state assert not any( [bes.has_acceptor_phs, bes.has_donor_phs, bes.has_occupied_localized_state, bes.is_shallow] @@ -2870,7 +2914,7 @@ def test_eigenvalues_parsing_and_warnings(self): ) print([str(warning.message) for warning in w]) # for debugging - assert any("with projected orbitals in path" in str(warning.message) for warning in w) + assert any("Could not parse eigenvalue data" in str(warning.message) for warning in w) # Test no warning for no projected orbitals with default ``parse_projected_eigen=None`` (attempt # but don't warn): Sb2Se3 data diff --git a/tests/test_chemical_potentials.py b/tests/test_chemical_potentials.py index ed75bceb..3042a4a1 100644 --- a/tests/test_chemical_potentials.py +++ b/tests/test_chemical_potentials.py @@ -63,9 +63,13 @@ def test_cpa_csv(self): assert len(stable_cpa.elemental) == 2 assert len(self.ext_cpa.elemental) == 3 - assert any(entry["formula"] == "O2" for entry in stable_cpa.data) + assert any(entry["Formula"] == "O2" for entry in stable_cpa.data) assert np.isclose( - next(entry["energy_per_fu"] for entry in self.ext_cpa.data if entry["formula"] == "La2Zr2O7"), + next( + entry["DFT Energy (eV/fu)"] + for entry in self.ext_cpa.data + if entry["Formula"] == "La2Zr2O7" + ), -119.619571095, ) @@ -88,7 +92,7 @@ def test_cpa_chempots(self): ) self.ext_cpa.from_csv(self.csv_path_ext) chempot_df = self.ext_cpa.calculate_chempots() - assert next(iter(chempot_df["La_limiting_phase"])) == "La2Zr2O7" + assert next(iter(chempot_df["La-Limiting Phase"])) == "La2Zr2O7" assert np.isclose(next(iter(chempot_df["La"])), -9.46298748) def test_ext_cpa_chempots(self): @@ -107,8 +111,8 @@ def test_sort_by(self): stable_cpa = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) stable_cpa.from_csv(self.csv_path) chempot_df = stable_cpa.calculate_chempots(sort_by="Zr") - assert np.isclose(next(iter(chempot_df["Zr"])), -0.199544) - assert np.isclose(list(chempot_df["Zr"])[1], -10.975428439999998) + assert np.isclose(next(iter(chempot_df["Zr"])), -0.199544, atol=1e-4) + assert np.isclose(list(chempot_df["Zr"])[1], -10.975428439999998, atol=1e-4) with pytest.raises(KeyError): stable_cpa.calculate_chempots(sort_by="M") @@ -119,7 +123,7 @@ def test_vaspruns(self): path = self.path / "ZrO2" cpa.from_vaspruns(path=path, folder="relax", csv_path=self.csv_path) assert len(cpa.elemental) == 2 - assert cpa.data[0]["formula"] == "O2" + assert cpa.data[0]["Formula"] == "O2" cpa_no = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) with pytest.raises(FileNotFoundError): @@ -134,7 +138,7 @@ def test_vaspruns(self): assert len(ext_cpa.elemental) == 3 # sorted by num_species, then alphabetically, then by num_atoms_in_fu, then by # formation_energy - assert [entry["formula"] for entry in ext_cpa.data] == [ + assert [entry["Formula"] for entry in ext_cpa.data] == [ "La", "O2", "Zr", @@ -171,18 +175,21 @@ def test_vaspruns(self): # -278.46392292, 'formation_energy': -10.951109995000003}, {'formula': 'La2Zr2O7', # 'kpoints': '3x3x3', 'energy_per_fu': -119.619571095, 'energy_per_atom': # -10.874506463181818, 'energy': -239.23914219, 'formation_energy': -40.87683184}] - assert np.isclose(ext_cpa.data[0]["energy_per_fu"], -5.00458616) - assert np.isclose(ext_cpa.data[0]["energy_per_atom"], -5.00458616) - assert np.isclose(ext_cpa.data[0]["energy"], -20.01834464) - assert np.isclose(ext_cpa.data[0]["formation_energy"], 0.0) - assert np.isclose(ext_cpa.data[-1]["energy_per_fu"], -119.619571095) - assert np.isclose(ext_cpa.data[-1]["energy_per_atom"], -10.874506463181818) - assert np.isclose(ext_cpa.data[-1]["energy"], -239.23914219) - assert np.isclose(ext_cpa.data[-1]["formation_energy"], -40.87683184) - assert np.isclose(ext_cpa.data[6]["energy_per_fu"], -42.524204305) - assert np.isclose(ext_cpa.data[6]["energy_per_atom"], -10.63105107625) - assert np.isclose(ext_cpa.data[6]["energy"], -85.04840861) - assert np.isclose(ext_cpa.data[6]["formation_energy"], -5.986573519999993) + assert np.isclose(ext_cpa.data[0]["DFT Energy (eV/fu)"], -5.00458616) + assert np.isclose(ext_cpa.data[0]["DFT Energy (eV/atom)"], -5.00458616) + assert np.isclose(ext_cpa.data[0]["DFT Energy (eV)"], -20.01834464) + assert np.isclose(ext_cpa.data[0]["Formation Energy (eV/fu)"], 0.0) + assert np.isclose(ext_cpa.data[0]["Formation Energy (eV/atom)"], 0.0) + assert np.isclose(ext_cpa.data[-1]["DFT Energy (eV/fu)"], -119.619571095) + assert np.isclose(ext_cpa.data[-1]["DFT Energy (eV/atom)"], -10.874506463181818) + assert np.isclose(ext_cpa.data[-1]["DFT Energy (eV)"], -239.23914219) + assert np.isclose(ext_cpa.data[-1]["Formation Energy (eV/fu)"], -40.87683184) + assert np.isclose(ext_cpa.data[-1]["Formation Energy (eV/atom)"], -40.87683184 / 11) + assert np.isclose(ext_cpa.data[6]["DFT Energy (eV/fu)"], -42.524204305) + assert np.isclose(ext_cpa.data[6]["DFT Energy (eV/atom)"], -10.63105107625) + assert np.isclose(ext_cpa.data[6]["DFT Energy (eV)"], -85.04840861) + assert np.isclose(ext_cpa.data[6]["Formation Energy (eV/fu)"], -5.986573519999993) + assert np.isclose(ext_cpa.data[6]["Formation Energy (eV/atom)"], -5.986573519999993 / 4) # check if it works from a list all_paths = [] @@ -247,13 +254,13 @@ def test_cplap_input(self): # assert these lines are in the file: for i in [ "2 # number of elements in bulk\n", - "1 Zr 2 O -10.975428440000002 # num_atoms, element, formation_energy (bulk)\n", + "1 Zr 2 O -10.975428440000002 # number of atoms, element, formation energy (bulk)\n", "O # dependent variable (element)\n", "2 # number of bordering phases\n", "1 # number of elements in phase:\n", - "2 O 0.0 # num_atoms, element, formation_energy\n", + "2 O 0.0 # number of atoms, element, formation energy\n", "2 # number of elements in phase:\n", - "3 Zr 1 O -5.986573519999993 # num_atoms, element, formation_energy\n", + "3 Zr 1 O -5.986573519999993 # number of atoms, element, formation energy\n", ]: assert i in contents @@ -270,28 +277,42 @@ def test_latex_table(self): string = cpa.to_LaTeX_table(splits=1) assert ( string[28:209] - == "\\caption{Formation energies ($\\Delta E_f$) per formula unit of \\ce{ZrO2} and all " + == "\\caption{Formation energies per formula unit ($\\Delta E_f$) of \\ce{ZrO2} and all " "competing phases, with k-meshes used in calculations. Only the lowest energy polymorphs " "are included" ) - assert len(string) == 586 - assert string.split("hline")[1] == "\nFormula & k-mesh & $\\Delta E_f$ (eV) \\\\ \\" + assert len(string) == 589 + assert string.split("hline")[1] == "\nFormula & k-mesh & $\\Delta E_f$ (eV/fu) \\\\ \\" assert string.split("hline")[2][2:45] == "\\ce{ZrO2} & 3$\\times$3$\\times$3 & -10.975 \\" string = cpa.to_LaTeX_table(splits=2) assert ( string[28:209] - == "\\caption{Formation energies ($\\Delta E_f$) per formula unit of \\ce{ZrO2} and all " + == "\\caption{Formation energies per formula unit ($\\Delta E_f$) of \\ce{ZrO2} and all " "competing phases, with k-meshes used in calculations. Only the lowest energy polymorphs " "are included" ) assert ( string.split("hline")[1] - == "\nFormula & k-mesh & $\\Delta E_f$ (eV) & Formula & k-mesh & $\\Delta E_f$ (eV)\\\\ \\" + == "\nFormula & k-mesh & $\\Delta E_f$ (eV/fu) & Formula & k-mesh & $\\Delta E_f$ (" + "eV/fu) \\\\ \\" ) assert string.split("hline")[2][2:45] == "\\ce{ZrO2} & 3$\\times$3$\\times$3 & -10.975 &" - assert len(string) == 579 + assert len(string) == 586 + + # test without kpoints: + for entry_dict in cpa.data: + entry_dict.pop("k-points") + string = cpa.to_LaTeX_table(splits=1) + assert ( + string[28:173] + == "\\caption{Formation energies per formula unit ($\\Delta E_f$) of \\ce{ZrO2} and all " + "competing phases. Only the lowest energy polymorphs are included" + ) + assert len(string) == 433 + assert string.split("hline")[1] == "\nFormula & $\\Delta E_f$ (eV/fu) \\\\ \\" + assert string.split("hline")[2][2:23] == "\\ce{ZrO2} & -10.975 \\" cpa_csv = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) cpa_csv.from_csv(self.csv_path) @@ -308,7 +329,10 @@ def test_to_csv(self): reloaded_cpa = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) reloaded_cpa.from_csv("competing_phases.csv") reloaded_cpa_data = reloaded_cpa._get_and_sort_formation_energy_data() - assert pd.DataFrame(stable_cpa_data).equals(pd.DataFrame(reloaded_cpa_data)) + print( + pd.DataFrame(stable_cpa_data).to_dict(), pd.DataFrame(reloaded_cpa_data).to_dict() + ) # for debugging + assert pd.DataFrame(stable_cpa_data).round(4).equals(pd.DataFrame(reloaded_cpa_data).round(4)) # check chem limits the same: _compare_chempot_dicts(stable_cpa.chempots, reloaded_cpa.chempots) @@ -324,7 +348,9 @@ def test_to_csv(self): reloaded_ext_cpa.from_csv("competing_phases.csv") reloaded_ext_cpa._get_and_sort_formation_energy_data() - assert reloaded_ext_cpa.formation_energy_df.equals(self.ext_cpa.formation_energy_df) + assert reloaded_ext_cpa.formation_energy_df.round(4).equals( + self.ext_cpa.formation_energy_df.round(4) + ) # test pruning: self.ext_cpa.to_csv("competing_phases.csv", prune_polymorphs=True) @@ -332,7 +358,7 @@ def test_to_csv(self): assert len(reloaded_ext_cpa.data) == 8 # polymorphs pruned assert len(self.ext_cpa.data) == 11 - formulas = [i["formula"] for i in reloaded_ext_cpa.data] + formulas = [i["Formula"] for i in reloaded_ext_cpa.data] assert len(formulas) == len(set(formulas)) # no polymorphs reloaded_cpa.to_csv("competing_phases.csv", prune_polymorphs=True) @@ -356,9 +382,11 @@ def test_to_csv(self): assert len(reloaded_ext_cpa.data) == 8 # polymorphs pruned assert reloaded_ext_cpa_energy_sorted.data != reloaded_ext_cpa.data # different order - sorted_data = sorted(reloaded_ext_cpa.data, key=lambda x: x["formation_energy"], reverse=True) + sorted_data = sorted( + reloaded_ext_cpa.data, key=lambda x: x["Formation Energy (eV/fu)"], reverse=True + ) chemical_potentials._move_dict_to_start( - sorted_data, "formula", self.ext_cpa.bulk_composition.reduced_formula + sorted_data, "Formula", self.ext_cpa.bulk_composition.reduced_formula ) assert reloaded_ext_cpa_energy_sorted.data == sorted_data # energy sorted data @@ -374,21 +402,22 @@ def test_from_csv_minimal(self): formation_energy_df = pd.DataFrame(formation_energy_data) # drop all but the formula and energy_per_fu columns: - for i in ["energy_per_fu", "energy_per_atom"]: - minimal_formation_energy_df = formation_energy_df[["formula", i]] + for i in ["DFT Energy (eV/fu)", "DFT Energy (eV/atom)"]: + minimal_formation_energy_df = formation_energy_df[["Formula", i]] minimal_formation_energy_df.to_csv("competing_phases.csv", index=False) reloaded_cpa = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) reloaded_cpa.from_csv("competing_phases.csv") - assert not cpa.formation_energy_df.equals( - reloaded_cpa.formation_energy_df + assert not cpa.formation_energy_df.round(4).equals( + reloaded_cpa.formation_energy_df.round(4) ) # no kpoints or raw energy, but should have formula, energy_per_fu, energy_per_atom, # elemental amounts (i.e. Zr and O here) and formation_energy: minimal_columns = [ - "formula", - "energy_per_fu", - "energy_per_atom", - "formation_energy", + "Formula", + "DFT Energy (eV/fu)", + "DFT Energy (eV/atom)", + "Formation Energy (eV/fu)", + "Formation Energy (eV/atom)", "Zr", # ordered by appearance in bulk composition "O", ] @@ -399,13 +428,15 @@ def test_from_csv_minimal(self): trimmed_df = trimmed_df.round(5) # round to avoid slight numerical differences reloaded_cpa.formation_energy_df = reloaded_cpa.formation_energy_df.round(5) - assert trimmed_df.equals(reloaded_cpa.formation_energy_df) + print(trimmed_df, reloaded_cpa.formation_energy_df) + print(trimmed_df.columns, reloaded_cpa.formation_energy_df.columns) + assert trimmed_df.round(4).equals(reloaded_cpa.formation_energy_df.round(4)) # check chem limits the same: _compare_chempot_dicts(cpa.chempots, reloaded_cpa.chempots) # test ValueError without energy_per_fu/energy_per_atom column: - too_minimal_formation_energy_df = formation_energy_df[["formula"]] + too_minimal_formation_energy_df = formation_energy_df[["Formula"]] too_minimal_formation_energy_df.to_csv("competing_phases.csv", index=False) reloaded_cpa = chemical_potentials.CompetingPhasesAnalyzer(self.stable_system) with pytest.raises(ValueError) as exc: @@ -429,50 +460,50 @@ def test_fe(self): elemental = {"O": -7.006602065, "Zr": -9.84367624} data = [ { - "formula": "O2", - "energy_per_fu": -14.01320413, - "energy_per_atom": -7.006602065, - "energy": -14.01320413, + "Formula": "O2", + "DFT Energy (eV/fu)": -14.01320413, + "DFT Energy (eV/atom)": -7.006602065, + "DFT Energy (eV)": -14.01320413, }, { - "formula": "Zr", - "energy_per_fu": -9.84367624, - "energy_per_atom": -9.84367624, - "energy": -19.68735248, + "Formula": "Zr", + "DFT Energy (eV/fu)": -9.84367624, + "DFT Energy (eV/atom)": -9.84367624, + "DFT Energy (eV)": -19.68735248, }, { - "formula": "Zr3O", - "energy_per_fu": -42.524204305, - "energy_per_atom": -10.63105107625, - "energy": -85.04840861, + "Formula": "Zr3O", + "DFT Energy (eV/fu)": -42.524204305, + "DFT Energy (eV/atom)": -10.63105107625, + "DFT Energy (eV)": -85.04840861, }, { - "formula": "ZrO2", - "energy_per_fu": -34.5391058, - "energy_per_atom": -11.5130352, - "energy": -138.156423, + "Formula": "ZrO2", + "DFT Energy (eV/fu)": -34.5391058, + "DFT Energy (eV/atom)": -11.5130352, + "DFT Energy (eV)": -138.156423, }, { - "formula": "ZrO2", - "energy_per_fu": -34.83230881, - "energy_per_atom": -11.610769603333331, - "energy": -139.32923524, + "Formula": "ZrO2", + "DFT Energy (eV/fu)": -34.83230881, + "DFT Energy (eV/atom)": -11.610769603333331, + "DFT Energy (eV)": -139.32923524, }, { - "formula": "Zr2O", - "energy_per_fu": -32.42291351666667, - "energy_per_atom": -10.807637838888889, - "energy": -194.5374811, + "Formula": "Zr2O", + "DFT Energy (eV/fu)": -32.42291351666667, + "DFT Energy (eV/atom)": -10.807637838888889, + "DFT Energy (eV)": -194.5374811, }, ] formation_energy_df = chemical_potentials._calculate_formation_energies(data, elemental) - assert formation_energy_df["formula"][0] == "O2" # definite order - assert formation_energy_df["formation_energy"][0] == 0 - assert formation_energy_df["formula"][1] == "Zr" - assert formation_energy_df["formation_energy"][1] == 0 + assert formation_energy_df["Formula"][0] == "O2" # definite order + assert formation_energy_df["Formation Energy (eV/fu)"][0] == 0 + assert formation_energy_df["Formula"][1] == "Zr" + assert formation_energy_df["Formation Energy (eV/fu)"][1] == 0 # lowest energy ZrO2: - assert np.isclose(formation_energy_df["formation_energy"][4], -10.975428440000002) + assert np.isclose(formation_energy_df["Formation Energy (eV/fu)"][4], -10.975428440000002) class CombineExtrinsicTestCase(unittest.TestCase): diff --git a/tests/test_thermodynamics.py b/tests/test_thermodynamics.py index 78f1050a..d8ddd13f 100644 --- a/tests/test_thermodynamics.py +++ b/tests/test_thermodynamics.py @@ -21,6 +21,7 @@ from doped.generation import _sort_defect_entries from doped.thermodynamics import DefectThermodynamics, scissor_dos +from doped.utils.parsing import get_vasprun from doped.utils.symmetry import _get_sga, point_symmetry # for pytest-mpl: @@ -2078,6 +2079,36 @@ def test_calculated_fermi_levels(self): return f + def test_calculated_fermi_level_k10(self): + """ + Test calculating the Fermi level using a 10x10x10 k-point mesh DOS + calculation for primitive CdTe; specifying just the path to the DOS + vasprun.xml. + """ + k10_dos_vr_path = os.path.join(self.module_path, "data/CdTe/CdTe_prim_k101010_dos_vr.xml.gz") + + for i, bulk_dos_vr in enumerate([k10_dos_vr_path, get_vasprun(k10_dos_vr_path, parse_dos=True)]): + print(f"Testing k10 DOS with thermo for {'str input' if i == 0 else 'DOS object input'}") + quenched_fermi_levels = [] + for anneal_temp in self.anneal_temperatures: + gap_shift = belas_linear_fit(anneal_temp) - 1.5 + ( + fermi_level, + e_conc, + h_conc, + conc_df, + ) = self.defect_thermo.get_quenched_fermi_level_and_concentrations( + # quenching to 300K (default) + bulk_dos_vr=bulk_dos_vr, + limit="Te-rich", + annealing_temperature=anneal_temp, + delta_gap=gap_shift, + ) + quenched_fermi_levels += [fermi_level] + + # (approx) same result as with k181818 NKRED=2 (0.319104 eV with this dos) + assert np.isclose(np.mean(quenched_fermi_levels[12:16]), 0.318674, atol=1e-3) + @custom_mpl_image_compare(filename="CdTe_LZ_Te_rich_concentrations.png") def test_calculated_concentrations(self): annealing_n = np.array( diff --git a/tests/test_vasp.py b/tests/test_vasp.py index 8ccb4e39..279395a5 100644 --- a/tests/test_vasp.py +++ b/tests/test_vasp.py @@ -150,7 +150,6 @@ def _general_defect_dict_set_check(self, dds, struct, incar_check=True, **dds_kw potcar_settings[el_symbol] for el_symbol in dds.structure.symbol_set } else: - assert not dds.potcars with pytest.raises(ValueError) as e: _test_pop = dds.potcar assert _check_no_potcar_available_warning_error(dds.potcar_symbols[0], e.value) @@ -192,6 +191,7 @@ def _general_defect_dict_set_check(self, dds, struct, incar_check=True, **dds_kw ) def _check_potcar_nupdown_dds_warnings(self, w, dds): + print("Testing:", [str(warning.message) for warning in w]) # for debugging assert any(_check_potcar_dir_not_setup_warning_error(dds, warning.message) for warning in w) assert any(_check_nupdown_neutral_cell_warning(warning.message) for warning in w) assert any( @@ -289,7 +289,9 @@ def _check_dds(self, dds, struct, **kwargs): ) def _generate_and_check_dds(self, struct, incar_check=True, **dds_kwargs): - dds = DefectDictSet(struct, **dds_kwargs) # fine for bulk prim input as well + with warnings.catch_warnings(record=True) as w: + dds = DefectDictSet(struct, **dds_kwargs) # fine for bulk prim input as well + print([str(warning.message) for warning in w]) # for debugging self._check_dds(dds, struct, incar_check=incar_check, **dds_kwargs) return dds @@ -301,8 +303,6 @@ def kpts_nelect_nupdown_check(self, dds, kpt, nelect, nupdown): if _potcars_available(): assert dds.incar["NELECT"] == nelect assert dds.incar["NUPDOWN"] == nupdown - else: - assert not dds.potcars def test_neutral_defect_dict_set(self): dds = self._generate_and_check_dds(self.prim_cdte.copy()) # fine for bulk prim input as well @@ -516,7 +516,10 @@ def _write_and_check_dds_files(self, dds, **kwargs): assert any(comment_string in line for line in incar_lines) if dds.incar.get("ALGO", "normal").lower() == "normal": # ALGO = Normal default, has comment - assert any("change to all if zhegv, fexcp/f or zbrent" in line for line in incar_lines) + assert any( + "change to all if zhegv, fexcp/f or zbrent, or poor electronic convergence" in line + for line in incar_lines + ) else: assert not os.path.exists(f"{output_path}/INCAR") @@ -590,7 +593,10 @@ def tearDown(self): if_present_rm("CdTe_bulk") for i in os.listdir(): - if os.path.isdir(i) and any(j in i for j in ["Mg_", "O_", "v_", "MgO"]): + if os.path.isdir(i) and ( + "MgO" in i + or any("vasp_" in j and os.path.isdir(os.path.join(i, j)) for j in os.listdir(i)) + ): if_present_rm(i) if_present_rm("MgO_defects_generator.json") @@ -1244,7 +1250,8 @@ def test_CdTe_files(self): for folder in os.listdir("."): if os.path.isdir(folder) and "bulk" not in folder: for subfolder in os.listdir(folder): - assert not os.path.exists(f"{folder}/{subfolder}/POSCAR") + if "vasp" in subfolder: + assert not os.path.exists(f"{folder}/{subfolder}/POSCAR") else: with pytest.raises(ValueError):