From a202cfebbf2bfa602976b5af5920b32cc033b550 Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 6 Dec 2023 16:43:16 +0100 Subject: [PATCH 01/33] add initial draft --- paper/paper.bib | 95 +++++++++++++++++++++++++++++++++++++++++++++++++ paper/paper.md | 92 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 187 insertions(+) create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..39af72f0 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,95 @@ +@article{dronskowski1993crystal, + title={Crystal orbital Hamilton populations (COHP): energy-resolved visualization of chemical bonding in solids based on density-functional calculations}, + author={Dronskowski, Richard and Bl{\"o}chl, Peter E}, + journal={The Journal of Physical Chemistry}, + volume={97}, + number={33}, + pages={8617--8624}, + year={1993}, + publisher={ACS Publications} +} + +@article{ngo2023dft, + title={DFT-Based Study for the Enhancement of CO2 Adsorption on Metal-Doped Nitrogen-Enriched Polytriazines}, + author={Ngo, Hieu Minh and Pal, Umapada and Kang, Young Soo and Ok, Kang Min}, + journal={ACS omega}, + volume={8}, + number={9}, + pages={8876--8884}, + year={2023}, + publisher={ACS Publications} +} + +@article{naik2023quantumchemical, + title={A Quantum-Chemical Bonding Database for Solid-State Materials}, + author={Aakash Ashok Naik and Christina Ertural and Nidal Dhamrait and Philipp Benner and Janine George}, + year={2023}, + eprint={2304.02726}, + archivePrefix={arXiv}, + primaryClass={cond-mat.mtrl-sci} +} + +@article{das2023strong, + title={Strong Antibonding I (p)--Cu (d) States Lead to Intrinsically Low Thermal Conductivity in CuBiI4}, + author={Das, Anustoop and Pal, Koushik and Acharyya, Paribesh and Das, Subarna and Maji, Krishnendu and Biswas, Kanishka}, + journal={Journal of the American Chemical Society}, + volume={145}, + number={2}, + pages={1349--1358}, + year={2023}, + publisher={ACS Publications} +} + +@article{ertural2022first, + title={First-Principles Plane-Wave-Based Exploration of Cathode and Anode Materials for Li-and Na-Ion Batteries Involving Complex Nitrogen-Based Anions}, + author={Ertural, Christina and Stoffel, Ralf P and Müller, Peter C and Vogt, C Alexander and Dronskowski, Richard}, + journal={Chemistry of Materials}, + volume={34}, + number={2}, + pages={652--668}, + year={2022}, + publisher={ACS Publications} +} + +@article{hu2023mechanism, + title={Mechanism of the low thermal conductivity in novel two-dimensional NaCuSe}, + author={Hu, Chengwei and Zhou, Lang and Hu, Xiaona and Lv, Bing and Gao, Zhibin}, + journal={Applied Surface Science}, + volume={613}, + pages={156064}, + year={2023}, + publisher={Elsevier} +} + +@article{hughbanks1983chains, + title={Chains of trans-edge-sharing molybdenum octahedra: metal-metal bonding in extended systems}, + author={Hughbanks, Timothy and Hoffmann, Roald}, + journal={Journal of the American Chemical Society}, + volume={105}, + number={11}, + pages={3528--3537}, + year={1983}, + publisher={ACS Publications} +} + +@article{müller2021crystal, + title={Crystal orbital bond index: Covalent bond orders in solids}, + author={Müller, Peter C and Ertural, Christina and Hempelmann, Jan and Dronskowski, Richard}, + journal={The Journal of Physical Chemistry C}, + volume={125}, + number={14}, + pages={7959--7970}, + year={2021}, + publisher={ACS Publications} +} + +@article{george2022automated, + title={Automated Bonding Analysis with Crystal Orbital Hamilton Populations}, + author={George, Janine and Petretto, Guido and Naik, Aakash and Esters, Marco and Jackson, Adam J and Nelson, Ryky and Dronskowski, Richard and Rignanese, Gian-Marco and Hautier, Geoffroy}, + journal={ChemPlusChem}, + volume={87}, + number={11}, + pages={e202200123}, + year={2022}, + publisher={Wiley Online Library} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..269dcbcc --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,92 @@ +--- +title: 'LobsterPy: Package to automatically analyze Lobster runs' +tags: + - Python + - Automation + - Bonding analysis + - Machine learning +authors: + - name: Aakash Ashok Naik + orcid: 0000-0002-6071-6786 + affiliation: "1 , 2" + - name: Katharina Ueltzen + orcid: 0009-0003-2967-1182 + affiliation: 1 + - name: Christina Ertural + orcid: 0000-0002-7696-5824 + affiliation: 1 + - name: Adam J. Jackson + orcid: 0000-0001-5272-6530 + affiliation: 3 + - name: Janine George + orcid: 0000-0001-8907-0336 + affiliation: "1, 2" +affiliations: + - name: Federal Institute for Materials Research and Testing, Department Materials Chemistry, Berlin, 12205, Germany + index: 1 + - name: Friedrich Schiller University Jena, Institute of Condensed Matter Theory and Solid-State Optics, Jena, 07743, Germany + index: 2 + - name: Science and Technology Facilities Council, Didcot, Oxfordshire, GB + index: 3 +date: August 2023 +bibliography: paper.bib + +--- +# Summary +_Lobsterpy_ is a Python package developed to systematically analyze, +describe, and visualize LOBSTER computations results. Alongside its python +interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of +the computations and generates a summary of results and publication-ready +figures. Since its first release, its capabilities have been extended significantly. +Unlike earlier versions which could only automatically analyze Crystal Orbital Hamilton +Populations (COHPs)[@dronskowski1993crystal], Lobsterpy can now also +analyze crystal orbital overlap populations (COOP)[@hughbanks1983chains] and Crystal orbital +bond index (COBI)[@müller2021crystal] to extract summarized bonding information +comprising of electronic-structure based coordination environments, bond strengths, +most relevant bonds, and their corresponding bonding and anti-bonding contributions. +Furthermore, one can now also extract the most relevant orbital interaction information. +Additionally, featurize and structure graphs modules provide a pathway to generate features to be used further for machine learning studies. +The features section comprehensively overviews the functionalities of this package. + +_Lobsterpy_ was used to produce the results in [@ngo2023dft, @naik2023quantumchemical] + +# Statement of need +Although the idea of "chemical bonding" might seem perplexing from a +physical standpoint, it has been employed several times to explain +various chemical phenomena and material properties.[@das2023strong, @ertural2022first, +@hu2023mechanism] With the recent +advances in automation frameworks for high-throughput computational +investigations, bonding analysis for thousands of crystalline materials +could be performed with few lines of code.[@george2022automated] This +automation helps reduce the common mistakes inexperienced users make +while performing bonding analysis. However, it is essential to systematically +generate inputs and post-process the output files consistently to have +reliable and reproducible results. Furthermore, +having data from high-throughput calculations ready to utilize as inputs +would benefit data-driven material science research. _Lobsterpy_ fulfills +this missing link. + +# Features +- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs, COBIs and COOPs +- JSONs and textual description of LOBSTER calculation quality +- Static and interactive plots of most relevant COHPs, COBIs and COOPs +- Generate inputs for bonding analysis calculations +- Generate features to be used for ML studies + + +# Availability +Lobsterpy can be found on GitHub and is also available from PyPI. +Detailed software documentation and installation instructions are provided. +The package also comes with several Jupyter Notebook and CLI tutorials +illustrating the usage and features. + +# Acknowledgements +The authors would like to acknowledge the Gauss Centre for Super +computing e.V. (www.gauss-centre.eu) for funding this project by +providing generous computing time on the GCS Supercomputer +SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de) +(project pn73da) that enabled rigorous testing of this +package on a diverse set of compounds. We also acknowledge +the maintainers of pymatgen. + +# References From 65769b51b94116a3b05dfc91be72067e0a60b077 Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 6 Dec 2023 16:48:29 +0100 Subject: [PATCH 02/33] fix pyproject.toml --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 387e4c8b..9360660f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,6 +5,9 @@ requires = [ ] build-backend = "setuptools.build_meta" +[tool.setuptools] +packages = ["lobsterpy"] + [project] name = "lobsterpy" description = "Package for automatic bonding analysis with Lobster/VASP" From 80086deb5bb00cbba906c94d961b171673f749fa Mon Sep 17 00:00:00 2001 From: anaik Date: Wed, 6 Dec 2023 16:57:15 +0100 Subject: [PATCH 03/33] attempt to fix doc tests failures --- docs/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 3334f4ac..8cfb8077 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -59,6 +59,8 @@ exclude_patterns = [ "test*.py", "test", + "paper", + "paper/*.md", "Thumbs.db", ".DS_Store", ] From 938b4e3b1cf5fe26234ad7d54398326a669cdd2a Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Wed, 6 Dec 2023 17:54:36 +0100 Subject: [PATCH 04/33] Update pyproject.toml --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9360660f..2d5b0524 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,8 +5,9 @@ requires = [ ] build-backend = "setuptools.build_meta" -[tool.setuptools] -packages = ["lobsterpy"] +[tool.setuptools.packages.find] +include = ["lobsterpy*"] +exclude = ["docs*", "tests*", "paper*"] [project] name = "lobsterpy" From ff4bc1dbe226ce7f859e0ee394947f4bfa52fc6a Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 7 Dec 2023 14:39:25 +0100 Subject: [PATCH 05/33] update text of summary and bib --- paper/paper.bib | 11 +++++++++++ paper/paper.md | 37 +++++++++++++++++-------------------- 2 files changed, 28 insertions(+), 20 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 39af72f0..ea64b83a 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -93,3 +93,14 @@ @article{george2022automated year={2022}, publisher={Wiley Online Library} } + +@article{morgan2023structures, + title={Structures of LaH 10, EuH 9, and UH 8 superhydrides rationalized by electron counting and Jahn--Teller distortions in a covalent cluster model}, + author={Morgan, Harry WT and Alexandrova, Anastassia N}, + journal={Chemical science}, + volume={14}, + number={24}, + pages={6679--6687}, + year={2023}, + publisher={Royal Society of Chemistry} +} diff --git a/paper/paper.md b/paper/paper.md index 269dcbcc..725f8b3a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,22 +33,19 @@ bibliography: paper.bib --- # Summary -_Lobsterpy_ is a Python package developed to systematically analyze, -describe, and visualize LOBSTER computations results. Alongside its python -interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of -the computations and generates a summary of results and publication-ready -figures. Since its first release, its capabilities have been extended significantly. -Unlike earlier versions which could only automatically analyze Crystal Orbital Hamilton -Populations (COHPs)[@dronskowski1993crystal], Lobsterpy can now also -analyze crystal orbital overlap populations (COOP)[@hughbanks1983chains] and Crystal orbital -bond index (COBI)[@müller2021crystal] to extract summarized bonding information -comprising of electronic-structure based coordination environments, bond strengths, -most relevant bonds, and their corresponding bonding and anti-bonding contributions. -Furthermore, one can now also extract the most relevant orbital interaction information. -Additionally, featurize and structure graphs modules provide a pathway to generate features to be used further for machine learning studies. -The features section comprehensively overviews the functionalities of this package. +The LOBSTER software helps extract chemical bonding information from materials by post-processing the density functional theory +computational data by projecting the wave functions onto an atomic orbital basis. _Lobsterpy_ is a Python package that provides +convenient tools to systematically analyze, describe, and visualize these LOBSTER computations results. Since its first release, +its capabilities have been extended significantly. Unlike earlier versions which could only automatically analyze Crystal Orbital +Hamilton Populations (COHPs)[@dronskowski1993crystal], LobsterPy can now also analyze crystal orbital overlap populations +(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@müller2021crystal] to extract summarized bonding +information (includes information on coordination environments, bond strengths, most relevant bonds, bonding and anti-bonding +contributions). Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, +featurize and structure graphs utility sub-packages provide a pathway to engineer features to be used further for machine learning +studies. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis +of the computations and generates a summary of results and publication-ready figures. -_Lobsterpy_ was used to produce the results in [@ngo2023dft, @naik2023quantumchemical] +_Lobsterpy_ was used to produce the results in [@ngo2023dft, @morgan2023structures, @naik2023quantumchemical] # Statement of need Although the idea of "chemical bonding" might seem perplexing from a @@ -63,22 +60,22 @@ while performing bonding analysis. However, it is essential to systematically generate inputs and post-process the output files consistently to have reliable and reproducible results. Furthermore, having data from high-throughput calculations ready to utilize as inputs -would benefit data-driven material science research. _Lobsterpy_ fulfills +would benefit data-driven material science research. _Lobsterpy_ aims to fulfill this missing link. # Features -- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs, COBIs and COOPs +- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs) and COOPs (ICOOPs) - JSONs and textual description of LOBSTER calculation quality - Static and interactive plots of most relevant COHPs, COBIs and COOPs - Generate inputs for bonding analysis calculations - Generate features to be used for ML studies +- Command line interface for automatic bonding analysis and plotting. # Availability Lobsterpy can be found on GitHub and is also available from PyPI. -Detailed software documentation and installation instructions are provided. -The package also comes with several Jupyter Notebook and CLI tutorials -illustrating the usage and features. +Detailed software documentation including [implementation details](https://jageo.github.io/LobsterPy/fundamentals/index.html) and [installation instructions](https://jageo.github.io/LobsterPy/installation/index.html) are provided. +The package also comes with [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating the usage and features. # Acknowledgements The authors would like to acknowledge the Gauss Centre for Super From fdc88e492c52b27a74269dcb69a0cd82511ae69e Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 08:14:56 +0100 Subject: [PATCH 06/33] refne text --- paper/paper.md | 47 +++++++++++++++++++++-------------------------- 1 file changed, 21 insertions(+), 26 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 725f8b3a..ff5b9d5c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,37 +45,32 @@ featurize and structure graphs utility sub-packages provide a pathway to enginee studies. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_Lobsterpy_ was used to produce the results in [@ngo2023dft, @morgan2023structures, @naik2023quantumchemical] +_Lobsterpy_ has been used to produce the results in [@ngo2023dft, @morgan2023structures, @naik2023quantumchemical] # Statement of need -Although the idea of "chemical bonding" might seem perplexing from a -physical standpoint, it has been employed several times to explain -various chemical phenomena and material properties.[@das2023strong, @ertural2022first, -@hu2023mechanism] With the recent -advances in automation frameworks for high-throughput computational -investigations, bonding analysis for thousands of crystalline materials -could be performed with few lines of code.[@george2022automated] This -automation helps reduce the common mistakes inexperienced users make -while performing bonding analysis. However, it is essential to systematically -generate inputs and post-process the output files consistently to have -reliable and reproducible results. Furthermore, -having data from high-throughput calculations ready to utilize as inputs -would benefit data-driven material science research. _Lobsterpy_ aims to fulfill -this missing link. +Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various +chemical phenomena and material properties.[@das2023strong, @ertural2022first, @hu2023mechanism] With the recent advances in +automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials +could be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced +users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process +the output files consistently to have reliable and reproducible results. Furthermore, transforming the data from these high-throughput +bonding analysis calculations into a format suitable for ML studies should benefit data-driven material science research. +_Lobsterpy_ aims to fulfill this need. # Features -- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs) and COOPs (ICOOPs) -- JSONs and textual description of LOBSTER calculation quality -- Static and interactive plots of most relevant COHPs, COBIs and COOPs -- Generate inputs for bonding analysis calculations -- Generate features to be used for ML studies -- Command line interface for automatic bonding analysis and plotting. - +- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs), and COOPs (ICOOPs) +- Automatic generation of static and interactive plots of the most relevant COHPs, COBIs, and COOPs +- Customizable plotters for COHPs (ICOHPs), COBIs (ICOBIs), COOPs (ICOOPs) and DOS +- Benchmarking and extracting LOBSTER calculation quality summary JSONs and text descriptions +- Create inputs for LOBSTER calculations from VASP files +- Extract features from LOBSTER calculation files to be used for ML studies +- Command line interface for automatic bonding analysis and plotting # Availability -Lobsterpy can be found on GitHub and is also available from PyPI. -Detailed software documentation including [implementation details](https://jageo.github.io/LobsterPy/fundamentals/index.html) and [installation instructions](https://jageo.github.io/LobsterPy/installation/index.html) are provided. -The package also comes with [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating the usage and features. +LobsterPy can also be found [PyPI](https://pypi.org/project/lobsterpy/). Detailed software documentation, +including [installation instructions](https://jageo.github.io/LobsterPy/installation/index.html) and +[implementation details](https://jageo.github.io/LobsterPy/fundamentals/index.html) are provided. The package +also includes [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating all the functionalities. # Acknowledgements The authors would like to acknowledge the Gauss Centre for Super @@ -84,6 +79,6 @@ providing generous computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de) (project pn73da) that enabled rigorous testing of this package on a diverse set of compounds. We also acknowledge -the maintainers of pymatgen. +the maintainers of pymatgen and LOBSTER program developers. # References From 85eef40d280a71ffec58d8c18cc79548ac263cdf Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 08:16:33 +0100 Subject: [PATCH 07/33] paper build workflow --- .github/workflows/draft-pdf.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 00000000..eecf0d13 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf From 49d342dcfdde2c12aa6b6d04ac94c056b958fda0 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 08:26:34 +0100 Subject: [PATCH 08/33] =?UTF-8?q?remove=20=C3=BC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- paper/paper.bib | 4 ++-- paper/paper.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index ea64b83a..969c2cbe 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -72,9 +72,9 @@ @article{hughbanks1983chains publisher={ACS Publications} } -@article{müller2021crystal, +@article{mueller2021crystal, title={Crystal orbital bond index: Covalent bond orders in solids}, - author={Müller, Peter C and Ertural, Christina and Hempelmann, Jan and Dronskowski, Richard}, + author={M\"uller, Peter C and Ertural, Christina and Hempelmann, Jan and Dronskowski, Richard}, journal={The Journal of Physical Chemistry C}, volume={125}, number={14}, diff --git a/paper/paper.md b/paper/paper.md index ff5b9d5c..63860718 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,7 +38,7 @@ computational data by projecting the wave functions onto an atomic orbital basis convenient tools to systematically analyze, describe, and visualize these LOBSTER computations results. Since its first release, its capabilities have been extended significantly. Unlike earlier versions which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], LobsterPy can now also analyze crystal orbital overlap populations -(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@müller2021crystal] to extract summarized bonding +(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@muller2021crystal] to extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, bonding and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, featurize and structure graphs utility sub-packages provide a pathway to engineer features to be used further for machine learning From 8122d267945cda280bbc108b0b4ba1d07c3bf468 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 08:42:40 +0100 Subject: [PATCH 09/33] fix ref --- paper/paper.bib | 15 +++++++++------ paper/paper.md | 2 +- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 969c2cbe..9bda50a6 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -21,12 +21,15 @@ @article{ngo2023dft } @article{naik2023quantumchemical, - title={A Quantum-Chemical Bonding Database for Solid-State Materials}, - author={Aakash Ashok Naik and Christina Ertural and Nidal Dhamrait and Philipp Benner and Janine George}, - year={2023}, - eprint={2304.02726}, - archivePrefix={arXiv}, - primaryClass={cond-mat.mtrl-sci} + title={A Quantum-Chemical Bonding Database for Solid-State Materials}, + author={Naik, Aakash Ashok, and Ertural, Christina, and Dhamrait, Nidal and Benner, Philipp and George, Janine}, + volume={10}, + issn={2052-4463}, + language={en}, + number={1}, + journal={Scientific Data}, + year={2023}, + pages={610}, } @article{das2023strong, diff --git a/paper/paper.md b/paper/paper.md index 63860718..cc69ba63 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,7 +38,7 @@ computational data by projecting the wave functions onto an atomic orbital basis convenient tools to systematically analyze, describe, and visualize these LOBSTER computations results. Since its first release, its capabilities have been extended significantly. Unlike earlier versions which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], LobsterPy can now also analyze crystal orbital overlap populations -(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@muller2021crystal] to extract summarized bonding +(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@mueller2021crystal] to extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, bonding and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, featurize and structure graphs utility sub-packages provide a pathway to engineer features to be used further for machine learning From 6da78cc0e37341b010ffbad85ab338addacc109f Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 08:51:18 +0100 Subject: [PATCH 10/33] fix citation --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index cc69ba63..4aec51dd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,11 +45,11 @@ featurize and structure graphs utility sub-packages provide a pathway to enginee studies. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_Lobsterpy_ has been used to produce the results in [@ngo2023dft, @morgan2023structures, @naik2023quantumchemical] +_Lobsterpy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] # Statement of need Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various -chemical phenomena and material properties.[@das2023strong, @ertural2022first, @hu2023mechanism] With the recent advances in +chemical phenomena and material properties.[@das2023strong; @ertural2022first; @hu2023mechanism] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials could be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process From cf33c01352e399b6fdf306bb29a7c329f99ae3f8 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 10:49:53 +0100 Subject: [PATCH 11/33] fix capitalization errors --- paper/paper.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 4aec51dd..2f1bbe1e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'LobsterPy: Package to automatically analyze Lobster runs' +title: 'LobsterPy: Package to automatically analyze LOBSTER runs' tags: - Python - Automation @@ -34,18 +34,18 @@ bibliography: paper.bib --- # Summary The LOBSTER software helps extract chemical bonding information from materials by post-processing the density functional theory -computational data by projecting the wave functions onto an atomic orbital basis. _Lobsterpy_ is a Python package that provides +computational data by projecting the wave functions onto an atomic orbital basis. _LobsterPy_ is a Python package that provides convenient tools to systematically analyze, describe, and visualize these LOBSTER computations results. Since its first release, its capabilities have been extended significantly. Unlike earlier versions which could only automatically analyze Crystal Orbital -Hamilton Populations (COHPs)[@dronskowski1993crystal], LobsterPy can now also analyze crystal orbital overlap populations -(COOP)[@hughbanks1983chains] and Crystal orbital bond index (COBI)[@mueller2021crystal] to extract summarized bonding +Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze Crystal Orbital Overlap Populations +(COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, bonding and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, featurize and structure graphs utility sub-packages provide a pathway to engineer features to be used further for machine learning studies. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_Lobsterpy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] +_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] # Statement of need Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various @@ -55,7 +55,7 @@ could be performed with few lines of code.[@george2022automated] This automation users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process the output files consistently to have reliable and reproducible results. Furthermore, transforming the data from these high-throughput bonding analysis calculations into a format suitable for ML studies should benefit data-driven material science research. -_Lobsterpy_ aims to fulfill this need. +_LobsterPy_ aims to fulfill this need. # Features - Automatic summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs), and COOPs (ICOOPs) @@ -67,10 +67,10 @@ _Lobsterpy_ aims to fulfill this need. - Command line interface for automatic bonding analysis and plotting # Availability -LobsterPy can also be found [PyPI](https://pypi.org/project/lobsterpy/). Detailed software documentation, +_LobsterPy_ can also be found on [PyPI](https://pypi.org/project/lobsterpy/). Detailed software documentation, including [installation instructions](https://jageo.github.io/LobsterPy/installation/index.html) and [implementation details](https://jageo.github.io/LobsterPy/fundamentals/index.html) are provided. The package -also includes [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating all the functionalities. +also includes [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating all the basic and advanced functionalities. # Acknowledgements The authors would like to acknowledge the Gauss Centre for Super From 66459edc53e66523172730c189fd1ea1875de357 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 10:50:14 +0100 Subject: [PATCH 12/33] add name to the workflow --- .github/workflows/draft-pdf.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index eecf0d13..0f6ced43 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,3 +1,5 @@ +name: compile-paper + on: [push] jobs: From c9ee352cdbe07be8bc1da92f7385984c85da44d2 Mon Sep 17 00:00:00 2001 From: anaik Date: Fri, 8 Dec 2023 17:38:31 +0100 Subject: [PATCH 13/33] simplify text --- paper/paper.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 2f1bbe1e..acf93340 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,17 +33,17 @@ bibliography: paper.bib --- # Summary -The LOBSTER software helps extract chemical bonding information from materials by post-processing the density functional theory -computational data by projecting the wave functions onto an atomic orbital basis. _LobsterPy_ is a Python package that provides -convenient tools to systematically analyze, describe, and visualize these LOBSTER computations results. Since its first release, -its capabilities have been extended significantly. Unlike earlier versions which could only automatically analyze Crystal Orbital -Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze Crystal Orbital Overlap Populations -(COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to extract summarized bonding -information (includes information on coordination environments, bond strengths, most relevant bonds, bonding and anti-bonding -contributions). Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, -featurize and structure graphs utility sub-packages provide a pathway to engineer features to be used further for machine learning -studies. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis -of the computations and generates a summary of results and publication-ready figures. +The LOBSTER software aids in extracting chemical bonding information from materials. It does this by projecting the +density functional theory wave functions onto an atomic orbital basis. _LobsterPy_ is a Python package +that provides convenient tools to systematically analyze, describe, and visualize such LOBSTER computations results. +Since its first release, its capabilities have been extended significantly. Unlike earlier versions which could only +automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze +Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to +extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, +bonding and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the +relevant bonds. Additionally, featurize and structure graphs utility sub-packages provide a pathway to engineer features to be +used further for machine learning (ML) studies. Alongside its Python interface, it also provides an easy-to-use command line +interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] From 9d338ed147fb29f630e3dece962eeae76b0505ff Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 14 Dec 2023 08:20:40 +0100 Subject: [PATCH 14/33] address comments from Katharina,Christina and refine features section text --- paper/paper.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index acf93340..37cdae2c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'LobsterPy: Package to automatically analyze LOBSTER runs' +title: 'LobsterPy: A package to automatically analyze LOBSTER runs' tags: - Python - Automation @@ -34,37 +34,37 @@ bibliography: paper.bib --- # Summary The LOBSTER software aids in extracting chemical bonding information from materials. It does this by projecting the -density functional theory wave functions onto an atomic orbital basis. _LobsterPy_ is a Python package +plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. _LobsterPy_ is a Python package that provides convenient tools to systematically analyze, describe, and visualize such LOBSTER computations results. -Since its first release, its capabilities have been extended significantly. Unlike earlier versions which could only +Since its first release, its capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, -bonding and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the -relevant bonds. Additionally, featurize and structure graphs utility sub-packages provide a pathway to engineer features to be -used further for machine learning (ML) studies. Alongside its Python interface, it also provides an easy-to-use command line +bonding, and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the +relevant bonds. Additionally, bonding-based features for ML studies can be engineered via the sub-packages +"featurize" and "structuregraphs". Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] +_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical]. # Statement of need Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various chemical phenomena and material properties.[@das2023strong; @ertural2022first; @hu2023mechanism] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials -could be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced +can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process the output files consistently to have reliable and reproducible results. Furthermore, transforming the data from these high-throughput bonding analysis calculations into a format suitable for ML studies should benefit data-driven material science research. _LobsterPy_ aims to fulfill this need. # Features -- Automatic summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs), and COOPs (ICOOPs) -- Automatic generation of static and interactive plots of the most relevant COHPs, COBIs, and COOPs -- Customizable plotters for COHPs (ICOHPs), COBIs (ICOBIs), COOPs (ICOOPs) and DOS -- Benchmarking and extracting LOBSTER calculation quality summary JSONs and text descriptions +- Generate summarized bonding analysis JSONs and text descriptions based on COHPs (ICOHPs), COBIs (ICOBIs), and COOPs (ICOOPs) +- Generate static and interactive plots of the most relevant COHPs, COBIs, and COOPs +- Customizable plotters for visualization of COHPs (ICOHPs), COBIs (ICOBIs), COOPs (ICOOPs) and DOS +- Benchmark LOBSTER calculation quality and generate corresponding JSONs and text descriptions - Create inputs for LOBSTER calculations from VASP files - Extract features from LOBSTER calculation files to be used for ML studies -- Command line interface for automatic bonding analysis and plotting +- Perform automatic bonding analysis and plotting via inherent command line interface app. # Availability _LobsterPy_ can also be found on [PyPI](https://pypi.org/project/lobsterpy/). Detailed software documentation, From 3e1e084c8a0bb73121361696e28c32ab3a76716e Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 2 Jan 2024 11:57:14 +0100 Subject: [PATCH 15/33] address review comments --- paper/paper.bib | 25 +++++++++++++++++++++++++ paper/paper.md | 10 ++++++---- 2 files changed, 31 insertions(+), 4 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 9bda50a6..6acd77b9 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,3 +1,10 @@ +@article{burdett1995chemical, + title={Chemical bonding in solids}, + author={Burdett, Jeremy K}, + journal={(No Title)}, + year={1995} +} + @article{dronskowski1993crystal, title={Crystal orbital Hamilton populations (COHP): energy-resolved visualization of chemical bonding in solids based on density-functional calculations}, author={Dronskowski, Richard and Bl{\"o}chl, Peter E}, @@ -9,6 +16,13 @@ @article{dronskowski1993crystal publisher={ACS Publications} } +@book{dronskowski2023chemical, + title={Chemical Bonding: From Plane Waves via Atomic Orbitals}, + author={Dronskowski, Richard}, + year={2023}, + publisher={Walter de Gruyter GmbH \& Co KG} +} + @article{ngo2023dft, title={DFT-Based Study for the Enhancement of CO2 Adsorption on Metal-Doped Nitrogen-Enriched Polytriazines}, author={Ngo, Hieu Minh and Pal, Umapada and Kang, Young Soo and Ok, Kang Min}, @@ -54,6 +68,17 @@ @article{ertural2022first publisher={ACS Publications} } +@article{hoffmann1987chemistry, + title={How chemistry and physics meet in the solid state}, + author={Hoffmann, Roald}, + journal={Angewandte Chemie International Edition in English}, + volume={26}, + number={9}, + pages={846--878}, + year={1987}, + publisher={Wiley Online Library} +} + @article{hu2023mechanism, title={Mechanism of the low thermal conductivity in novel two-dimensional NaCuSe}, author={Hu, Chengwei and Zhou, Lang and Hu, Xiaona and Lv, Bing and Gao, Zhibin}, diff --git a/paper/paper.md b/paper/paper.md index 37cdae2c..07b5b5cc 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,7 +33,7 @@ bibliography: paper.bib --- # Summary -The LOBSTER software aids in extracting chemical bonding information from materials. It does this by projecting the +The LOBSTER software aids in extracting quantum-chemical bonding information from materials. It does this by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. _LobsterPy_ is a Python package that provides convenient tools to systematically analyze, describe, and visualize such LOBSTER computations results. Since its first release, its capabilities have been extended significantly. Unlike earlier versions, which could only @@ -41,15 +41,17 @@ automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1 Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, bonding, and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the -relevant bonds. Additionally, bonding-based features for ML studies can be engineered via the sub-packages +relevant bonds. Additionally, bonding-based features for machine-learning (ML) studies can be engineered via the sub-packages "featurize" and "structuregraphs". Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical]. +_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and also is part of +[atomate2](https://github.com/materialsproject/atomate2/blob/bd434a695600a37188db73d23e156f3b90e326b0/src/atomate2/lobster/schemas.py) +bonding analysis workflow task-document generation. # Statement of need Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various -chemical phenomena and material properties.[@das2023strong; @ertural2022first; @hu2023mechanism] With the recent advances in +chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process From d2a4a9f50b19e98d97106a85bff5e18c1cf950da Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 2 Jan 2024 13:02:47 +0100 Subject: [PATCH 16/33] address more review comments --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 07b5b5cc..5ce98da9 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -50,7 +50,7 @@ _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023str bonding analysis workflow task-document generation. # Statement of need -Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed several times to explain various +Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed routinely to explain various chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced From be4a2b8b127ecca3d0d9b975f204c90d8cb22f21 Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 2 Jan 2024 15:02:30 +0100 Subject: [PATCH 17/33] address comments --- paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 5ce98da9..f0bf8923 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -47,11 +47,11 @@ interface (CLI) that runs automatic analysis of the computations and generates a _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and also is part of [atomate2](https://github.com/materialsproject/atomate2/blob/bd434a695600a37188db73d23e156f3b90e326b0/src/atomate2/lobster/schemas.py) -bonding analysis workflow task-document generation. +bonding analysis workflow data generation. # Statement of need -Although "bonds" might seem perplexing from a physicist's standpoint, it has been employed routinely to explain various -chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in +Although notion of "bonds" might seem unusual from a physicist's point of view, Chemists have been employing it routinely to +explain various chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process From 81398869e831da234b084e7c17266a664cbd4aa0 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Wed, 3 Jan 2024 16:21:11 +0100 Subject: [PATCH 18/33] Update paper.bib Fix missing publisher --- paper/paper.bib | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 6acd77b9..9c5fdc50 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,8 +1,8 @@ @article{burdett1995chemical, title={Chemical bonding in solids}, author={Burdett, Jeremy K}, - journal={(No Title)}, - year={1995} + year={1995}, + publisher={Oxford University Press} } @article{dronskowski1993crystal, From 5075422b0f4896c9eaf98ed0d6d413c3c71447d3 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Wed, 3 Jan 2024 16:25:29 +0100 Subject: [PATCH 19/33] fix citation type to book --- paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 9c5fdc50..e4a629e4 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,4 +1,4 @@ -@article{burdett1995chemical, +@book{burdett1995chemical, title={Chemical bonding in solids}, author={Burdett, Jeremy K}, year={1995}, From e74373527a7a3f5ad2005f0ef93ebe1cf0480046 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Wed, 3 Jan 2024 16:52:50 +0100 Subject: [PATCH 20/33] Minor text update --- paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index f0bf8923..fbfb21cc 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -39,8 +39,8 @@ that provides convenient tools to systematically analyze, describe, and visualiz Since its first release, its capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to -extract summarized bonding information (includes information on coordination environments, bond strengths, most relevant bonds, -bonding, and anti-bonding contributions). Optionally, users can further extract the most important orbitals contributing to the +extract summarized bonding information. The latter includes information on coordination environments, bond strengths, most relevant bonds, +bonding, and anti-bonding contributions. Optionally, users can further extract the most important orbitals contributing to the relevant bonds. Additionally, bonding-based features for machine-learning (ML) studies can be engineered via the sub-packages "featurize" and "structuregraphs". Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. @@ -50,7 +50,7 @@ _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023str bonding analysis workflow data generation. # Statement of need -Although notion of "bonds" might seem unusual from a physicist's point of view, Chemists have been employing it routinely to +Although notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to explain various chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced From 6b9582335a6724124b51ee72bfcc35aa7a01e9aa Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Mon, 8 Jan 2024 09:17:22 +0100 Subject: [PATCH 21/33] Add missing doi --- paper/paper.bib | 40 +++++++++++++++++++++++++++++----------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index e4a629e4..e269458d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,3 +1,9 @@ +@misc{atomate2, +title = {Atomate2}, +year = {2023}, +url = {https://github.com/materialsproject/atomate2} +} + @book{burdett1995chemical, title={Chemical bonding in solids}, author={Burdett, Jeremy K}, @@ -13,14 +19,16 @@ @article{dronskowski1993crystal number={33}, pages={8617--8624}, year={1993}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/j100135a014}, } @book{dronskowski2023chemical, title={Chemical Bonding: From Plane Waves via Atomic Orbitals}, author={Dronskowski, Richard}, year={2023}, - publisher={Walter de Gruyter GmbH \& Co KG} + publisher={Walter de Gruyter GmbH \& Co KG}, + doi={10.1515/9783111167213}, } @article{ngo2023dft, @@ -31,7 +39,8 @@ @article{ngo2023dft number={9}, pages={8876--8884}, year={2023}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/acsomega.3c00395}, } @article{naik2023quantumchemical, @@ -44,6 +53,7 @@ @article{naik2023quantumchemical journal={Scientific Data}, year={2023}, pages={610}, + doi={10.1038/s41597-023-02477-5}, } @article{das2023strong, @@ -54,7 +64,8 @@ @article{das2023strong number={2}, pages={1349--1358}, year={2023}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/jacs.2c11908}, } @article{ertural2022first, @@ -65,7 +76,8 @@ @article{ertural2022first number={2}, pages={652--668}, year={2022}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/acs.chemmater.1c03349}, } @article{hoffmann1987chemistry, @@ -76,7 +88,8 @@ @article{hoffmann1987chemistry number={9}, pages={846--878}, year={1987}, - publisher={Wiley Online Library} + publisher={Wiley Online Library}, + doi={10.1002/anie.198708461}, } @article{hu2023mechanism, @@ -86,7 +99,8 @@ @article{hu2023mechanism volume={613}, pages={156064}, year={2023}, - publisher={Elsevier} + publisher={Elsevier}, + doi={10.1016/j.apsusc.2022.156064}, } @article{hughbanks1983chains, @@ -97,7 +111,8 @@ @article{hughbanks1983chains number={11}, pages={3528--3537}, year={1983}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/ja00349a027}, } @article{mueller2021crystal, @@ -108,7 +123,8 @@ @article{mueller2021crystal number={14}, pages={7959--7970}, year={2021}, - publisher={ACS Publications} + publisher={ACS Publications}, + doi={10.1021/acs.jpcc.1c00718}, } @article{george2022automated, @@ -119,7 +135,8 @@ @article{george2022automated number={11}, pages={e202200123}, year={2022}, - publisher={Wiley Online Library} + publisher={Wiley Online Library}, + doi={10.1002/cplu.202200246}, } @article{morgan2023structures, @@ -130,5 +147,6 @@ @article{morgan2023structures number={24}, pages={6679--6687}, year={2023}, - publisher={Royal Society of Chemistry} + publisher={Royal Society of Chemistry}, + doi={10.1039/D3SC00900A}, } From a3999d494ecf46ce7be816980944da043356a807 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Mon, 8 Jan 2024 09:19:00 +0100 Subject: [PATCH 22/33] Update atomate2 link --- paper/paper.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index fbfb21cc..554f9656 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -46,8 +46,7 @@ relevant bonds. Additionally, bonding-based features for machine-learning (ML) s interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and also is part of -[atomate2](https://github.com/materialsproject/atomate2/blob/bd434a695600a37188db73d23e156f3b90e326b0/src/atomate2/lobster/schemas.py) -bonding analysis workflow data generation. +[@atomate2] bonding analysis workflow as part of data generation. # Statement of need Although notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to From 5ad5941bc2cea5b90232cd4039656a4570342846 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Mon, 8 Jan 2024 09:28:25 +0100 Subject: [PATCH 23/33] Fix article link --- paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index e269458d..b30a70fe 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -136,7 +136,7 @@ @article{george2022automated pages={e202200123}, year={2022}, publisher={Wiley Online Library}, - doi={10.1002/cplu.202200246}, + doi={10.1002/cplu.202200123}, } @article{morgan2023structures, From ac09c652c894408319a71022ce5dbd64aa684d3d Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Mon, 8 Jan 2024 09:38:32 +0100 Subject: [PATCH 24/33] Update paper.md --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 554f9656..1e7d762e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,8 +45,8 @@ relevant bonds. Additionally, bonding-based features for machine-learning (ML) s "featurize" and "structuregraphs". Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and also is part of -[@atomate2] bonding analysis workflow as part of data generation. +_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and is also part of +[@atomate2] bonding analysis workflow for generating bonding analysis summaries. # Statement of need Although notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to From 04b800fab3c20bcd339a2e6bd57808a58eaab19f Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Mon, 8 Jan 2024 11:44:23 +0100 Subject: [PATCH 25/33] Update paper.md --- paper/paper.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 1e7d762e..be15c10d 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -79,7 +79,6 @@ computing e.V. (www.gauss-centre.eu) for funding this project by providing generous computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de) (project pn73da) that enabled rigorous testing of this -package on a diverse set of compounds. We also acknowledge -the maintainers of pymatgen and LOBSTER program developers. +package on a diverse set of compounds. The authors thank Jonas Grandel for reviewing the docstrings and testing package functionalities and tutorials. The authors would also like to acknowledge the maintainers of pymatgen and LOBSTER program developers. # References From 9b31c83af667329319f14fb451930573a1ac5042 Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 9 Jan 2024 16:23:56 +0100 Subject: [PATCH 26/33] update summary text, bib and Adam affiliation --- paper/paper.bib | 22 ++++++++++++++++++++++ paper/paper.md | 31 +++++++++++++++++-------------- 2 files changed, 39 insertions(+), 14 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index b30a70fe..fe1101f9 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -4,6 +4,17 @@ @misc{atomate2 url = {https://github.com/materialsproject/atomate2} } +@article{ong2013python, + title={Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis}, + author={Ong, Shyue Ping and Richards, William Davidson and Jain, Anubhav and Hautier, Geoffroy and Kocher, Michael and Cholia, Shreyas and Gunter, Dan and Chevrier, Vincent L and Persson, Kristin A and Ceder, Gerbrand}, + journal={Computational Materials Science}, + volume={68}, + pages={314--319}, + year={2013}, + publisher={Elsevier} + doi={10.1016/j.commatsci.2012.10.028}, +} + @book{burdett1995chemical, title={Chemical bonding in solids}, author={Burdett, Jeremy K}, @@ -150,3 +161,14 @@ @article{morgan2023structures publisher={Royal Society of Chemistry}, doi={10.1039/D3SC00900A}, } + +@article{materialsproject, + title={Commentary: The Materials Project: A materials genome approach to accelerating materials innovation}, + author={Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin A}, + journal={APL materials}, + volume={1}, + number={1}, + year={2013}, + publisher={AIP Publishing}, + doi={10.1063/1.4812323}, +} diff --git a/paper/paper.md b/paper/paper.md index be15c10d..79f3a2a5 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -26,7 +26,7 @@ affiliations: index: 1 - name: Friedrich Schiller University Jena, Institute of Condensed Matter Theory and Solid-State Optics, Jena, 07743, Germany index: 2 - - name: Science and Technology Facilities Council, Didcot, Oxfordshire, GB + - name: Scientific Computing Department, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, 0X11 0QX, UK index: 3 date: August 2023 bibliography: paper.bib @@ -34,19 +34,20 @@ bibliography: paper.bib --- # Summary The LOBSTER software aids in extracting quantum-chemical bonding information from materials. It does this by projecting the -plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. _LobsterPy_ is a Python package -that provides convenient tools to systematically analyze, describe, and visualize such LOBSTER computations results. -Since its first release, its capabilities have been extended significantly. Unlike earlier versions, which could only -automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], _LobsterPy_ can now also analyze -Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal] to -extract summarized bonding information. The latter includes information on coordination environments, bond strengths, most relevant bonds, -bonding, and anti-bonding contributions. Optionally, users can further extract the most important orbitals contributing to the -relevant bonds. Additionally, bonding-based features for machine-learning (ML) studies can be engineered via the sub-packages -"featurize" and "structuregraphs". Alongside its Python interface, it also provides an easy-to-use command line -interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. +plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. [LobsterEnv](https://github.com/materialsproject/pymatgen/blob/master/pymatgen/io/lobster/lobsterenv.py), +which is implemented in pymatgen[[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding +information obtained from LOBSTER to identify neighbors and coordination environments. _LobsterPy_ is a Python package that offers a set of convenient tools +to analyze further and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the +estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both _LobsterPy_ and _LobsterEnv_ capabilities +have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], +both can now also analyze Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal]. +Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. +Additionally, bonding-based features for machine-learning (ML) studies can be engineered via the sub-packages "featurize" and "structuregraphs". +Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the +computations and generates a summary of results and publication-ready figures. _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and is also part of -[@atomate2] bonding analysis workflow for generating bonding analysis summaries. +[@atomate2] bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project[@materialsproject] API. # Statement of need Although notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to @@ -71,7 +72,8 @@ _LobsterPy_ aims to fulfill this need. _LobsterPy_ can also be found on [PyPI](https://pypi.org/project/lobsterpy/). Detailed software documentation, including [installation instructions](https://jageo.github.io/LobsterPy/installation/index.html) and [implementation details](https://jageo.github.io/LobsterPy/fundamentals/index.html) are provided. The package -also includes [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating all the basic and advanced functionalities. +also includes [tutorials](https://jageo.github.io/LobsterPy/tutorial/index.html) illustrating all the basic and +advanced functionalities. # Acknowledgements The authors would like to acknowledge the Gauss Centre for Super @@ -79,6 +81,7 @@ computing e.V. (www.gauss-centre.eu) for funding this project by providing generous computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de) (project pn73da) that enabled rigorous testing of this -package on a diverse set of compounds. The authors thank Jonas Grandel for reviewing the docstrings and testing package functionalities and tutorials. The authors would also like to acknowledge the maintainers of pymatgen and LOBSTER program developers. +package on a diverse set of compounds. The authors thank Jonas Grandel for reviewing the docstrings and testing package functionalities +and tutorials. The authors would also like to acknowledge the maintainers of pymatgen and LOBSTER program developers. # References From 359f440e7e4024dd4a786bcf2ebf98c4209574a5 Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 9 Jan 2024 16:25:50 +0100 Subject: [PATCH 27/33] remove extra [ --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 79f3a2a5..6547a572 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,7 +35,7 @@ bibliography: paper.bib # Summary The LOBSTER software aids in extracting quantum-chemical bonding information from materials. It does this by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. [LobsterEnv](https://github.com/materialsproject/pymatgen/blob/master/pymatgen/io/lobster/lobsterenv.py), -which is implemented in pymatgen[[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding +which is implemented in pymatgen[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER to identify neighbors and coordination environments. _LobsterPy_ is a Python package that offers a set of convenient tools to analyze further and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both _LobsterPy_ and _LobsterEnv_ capabilities From 1b77ee548397f92faaff52ad4a6185dba15abe03 Mon Sep 17 00:00:00 2001 From: anaik Date: Tue, 9 Jan 2024 16:27:16 +0100 Subject: [PATCH 28/33] fix bib --- paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index fe1101f9..557ea28d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -11,7 +11,7 @@ @article{ong2013python volume={68}, pages={314--319}, year={2013}, - publisher={Elsevier} + publisher={Elsevier}, doi={10.1016/j.commatsci.2012.10.028}, } From eeace52e381ab4d1db1bb748576dd0e53cd80d06 Mon Sep 17 00:00:00 2001 From: anaik Date: Thu, 11 Jan 2024 08:47:07 +0100 Subject: [PATCH 29/33] address some comments from Christina and Katha --- paper/paper.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 6547a572..36485bcd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,11 +33,11 @@ bibliography: paper.bib --- # Summary -The LOBSTER software aids in extracting quantum-chemical bonding information from materials. It does this by projecting the +The LOBSTER software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. [LobsterEnv](https://github.com/materialsproject/pymatgen/blob/master/pymatgen/io/lobster/lobsterenv.py), which is implemented in pymatgen[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding -information obtained from LOBSTER to identify neighbors and coordination environments. _LobsterPy_ is a Python package that offers a set of convenient tools -to analyze further and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the +information obtained from LOBSTER calculations to identify neighbors and coordination environments. _LobsterPy_ is a Python package that offers a set of convenient tools +to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both _LobsterPy_ and _LobsterEnv_ capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs)[@dronskowski1993crystal], both can now also analyze Crystal Orbital Overlap Populations (COOP)[@hughbanks1983chains] and Crystal Orbital Bond Index (COBI)[@mueller2021crystal]. @@ -50,8 +50,8 @@ _LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023str [@atomate2] bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project[@materialsproject] API. # Statement of need -Although notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to -explain various chemical phenomena and material properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in +Although the notion of "bonds" might seem unusual from a physicist's point of view, chemists have been employing it routinely to +explain various chemical phenomena and materials properties.[@hoffmann1987chemistry; @burdett1995chemical; @das2023strong; @ertural2022first; @hu2023mechanism; @dronskowski2023chemical] With the recent advances in automation frameworks for high-throughput computational investigations, bonding analysis for thousands of crystalline materials can be performed with few lines of code.[@george2022automated] This automation helps reduce the common mistakes inexperienced users make while performing bonding analysis. However, it is also essential to systematically generate inputs and post-process From 7cacd55e2f848c469d70d5651c9f9afceb310a57 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Thu, 11 Jan 2024 09:33:53 +0100 Subject: [PATCH 30/33] Update paper.md --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 36485bcd..9cf1a04f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,7 +35,7 @@ bibliography: paper.bib # Summary The LOBSTER software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. [LobsterEnv](https://github.com/materialsproject/pymatgen/blob/master/pymatgen/io/lobster/lobsterenv.py), -which is implemented in pymatgen[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding +a module implemented in pymatgen[@ong2013python] by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. _LobsterPy_ is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both _LobsterPy_ and _LobsterEnv_ capabilities From 92f615967a7b0a40819125b9be29af26bff40ca3 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Fri, 19 Jan 2024 14:02:39 +0100 Subject: [PATCH 31/33] Update citation --- paper/paper.bib | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 557ea28d..2414cb6c 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -150,16 +150,14 @@ @article{george2022automated doi={10.1002/cplu.202200123}, } -@article{morgan2023structures, - title={Structures of LaH 10, EuH 9, and UH 8 superhydrides rationalized by electron counting and Jahn--Teller distortions in a covalent cluster model}, - author={Morgan, Harry WT and Alexandrova, Anastassia N}, - journal={Chemical science}, - volume={14}, - number={24}, - pages={6679--6687}, - year={2023}, - publisher={Royal Society of Chemistry}, - doi={10.1039/D3SC00900A}, +@article{chen2024insights, + title={Insights into the Heterogeneous Nuclei of an Ultrafast-Crystallizing Glassy Solid}, + author={Chen, Bin and Li, Junhua and Wang, Xu and Shi, Mengchao and Sun, Tulai and Xia, Mengjiao and Ding, Keyuan and Liu, Jie and Li, Jixue and Tian, He and others}, + journal={Advanced Functional Materials}, + pages={2314565}, + year={2024}, + publisher={Wiley Online Library}, + doi={10.1002/adfm.202314565}, } @article{materialsproject, From 5b90d0f9fa458ce02e4191ab030b23f791ab0628 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Fri, 19 Jan 2024 14:03:02 +0100 Subject: [PATCH 32/33] Update paper.md --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 9cf1a04f..bce7671a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -46,7 +46,7 @@ Additionally, bonding-based features for machine-learning (ML) studies can be en Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. -_LobsterPy_ has been used to produce the results in [@ngo2023dft; @morgan2023structures; @naik2023quantumchemical] and is also part of +_LobsterPy_ has been used to produce the results in [@ngo2023dft; @chen2024insights; @naik2023quantumchemical] and is also part of [@atomate2] bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project[@materialsproject] API. # Statement of need From 1b42dcd20dc8f3d5a331629312486486a09faf14 Mon Sep 17 00:00:00 2001 From: Aakash Ashok Naik <91958822+naik-aakash@users.noreply.github.com> Date: Fri, 19 Jan 2024 14:22:02 +0100 Subject: [PATCH 33/33] Delete .github/workflows/draft-pdf.yml --- .github/workflows/draft-pdf.yml | 25 ------------------------- 1 file changed, 25 deletions(-) delete mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml deleted file mode 100644 index 0f6ced43..00000000 --- a/.github/workflows/draft-pdf.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: compile-paper - -on: [push] - -jobs: - paper: - runs-on: ubuntu-latest - name: Paper Draft - steps: - - name: Checkout - uses: actions/checkout@v4 - - name: Build draft PDF - uses: openjournals/openjournals-draft-action@master - with: - journal: joss - # This should be the path to the paper within your repo. - paper-path: paper/paper.md - - name: Upload - uses: actions/upload-artifact@v1 - with: - name: paper - # This is the output path where Pandoc will write the compiled - # PDF. Note, this should be the same directory as the input - # paper.md - path: paper/paper.pdf