Update Ke paper reference

docs/JOSS/paper.bib

@@ -203,20 +203,19 @@ @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 Sb2O5}}},
+  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},
+  publisher = {American Chemical Society},
+  issn = {0897-4756},
+  doi = {10.1021/acs.chemmater.3c03257},
+  urldate = {2024-03-25},
+  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/75HBJQ28/Li et al_2024_Computational Prediction of an Antimony-Based n-Type Transparent Conducting.pdf}
 }
 
 @article{liga_mixed-cation_2023,

docs/JOSS/paper.md

@@ -121,7 +121,7 @@ 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)
@@ -141,13 +141,13 @@ Some key advances of `doped` include:
 
 - **[`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.