pymatgen-analysis-defects
pymatgen-analysis-defects: A Python package for analyzing point defects in crystalline materials - Published in JOSS (2024)
https://github.com/materialsproject/pymatgen-analysis-defects
Science Score: 95.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
3 of 10 committers (30.0%) from academic institutions -
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords from Contributors
Scientific Fields
Repository
Defect analysis modules for pymatgen
Basic Info
- Host: GitHub
- Owner: materialsproject
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://materialsproject.github.io/pymatgen-analysis-defects
- Size: 258 MB
Statistics
- Stars: 54
- Watchers: 8
- Forks: 13
- Open Issues: 5
- Releases: 63
Metadata Files
README.md
pymatgen-analysis-defects
This package is an extension to pymatgen for performing defect
analysis. The package is designed to work with VASP inputs and output
files and is meant to be used as a namespace package extension to the
main pymatgen library. The new module has been redesigned to work
closely with atomate2.
While the atomate2 automation framework is not required for this code
to be useful, users are strongly encouraged to to adopt the atomate2
framework as it contains codified \"best practices\" for running defect
calculations as well as orchestrating the running of calculations and
storing the results.
The package serves as an object-oriented interface to defect physics and is capable of generating a list of non-equivalent defect objects directly from the Materials Project API.
python
from pymatgen.analysis.defects.generators import ChargeInterstitialGenerator, generate_all_native_defects
from pymatgen.ext.matproj import MPRester
with MPRester() as mpr:
chgcar = mpr.get_charge_density_from_material_id("mp-804")
for defect in generate_all_native_defects(chgcar):
print(defect)
Non-exhaustive list of features:
Reproducible definition of defects
Defects are defined based on the physical concept they represent, independent of the calculation details such as simulation cell size. As an example, a Vacancy defect is defined by the primitive cell of the pristine material plus a single site that represents the vacancy site in the unit cell.
Formation energy calculations
The formation energy diagram is a powerful tool for understanding the thermodynamics of defects. This package provides a simple interface for calculating the formation energy diagram from first-principles results. This package handles the energy accounting of the chemical species for the chemical potential calculations, which determines the y-offset of the formation energy. This package also performs finite-size corrections for the formation energy which is required when studying charged defects in periodic simulation cells.
Defect Position
Identification of the defect positions in a simulation cell after atomic relaxation is not trivial since the many atoms can collectively shift in response to the creation of the defect. Yet the exact location of the defect is required for the calculation of finite-size corrections as well as other physical properties. We devised a method based on calculating a SOAP-based distortion field that can be used to identify the defect position in a simulation cell. Note, this method only requires the reference pristine supercell and does not need prior knowledge of how the defect was created.
Defect Complexes
Multiple defects can be composed into defect complexes. The complex is can be treated as a normal defect object for subsequent analysis.
Defect Interactions
Simulation of defect-photon and defect-phonon interactions under the independent particle approximation.
Previous versions of the defects code
This package replaces the older pymatgen.analysis.defects modules. The
previous module was used by pyCDT code which will continue to work
with version 2022.7.8 of pymatgen.
Contributing
The source code can be downloaded from the GitHub repository at
bash
$ git clone https://github.com/materialsproject/pymatgen-analysis-defects.git
All code contributions are welcome. Please submit a pull request on GitHub. To make maintenance easier, please use a workflow similar to the automated CI workflow.
Specifically, please make sure to run the following commands for linting:
bash
$ pip install -e .[strict]
$ pip install -e .[dev]
$ pre-commit install
$ pre-commit run --all-files
And run these commands for testing:
bash
$ pip install -e .[strict]
$ pip install -e .[tests]
$ pytest --cov=pymatgen
$ pytest --nbmake ./docs/source/content
For more details about what is actually installed with each of the
pip install .[arg] commands, please inspect the pyproject.toml file.
Contributors
- Lead developer: Dr. Jimmy-Xuan Shen
- This code contains contributions from the original defects analysis
module of
pymatgenfrom Dr. Danny Broberg and Dr. Shyam Dwaraknath.
Owner
- Name: Materials Project
- Login: materialsproject
- Kind: organization
- Email: feedback@materialsproject.org
- Location: 1 Cyclotron Rd, Berkeley CA 94720
- Website: https://www.materialsproject.org
- Repositories: 51
- Profile: https://github.com/materialsproject
JOSS Publication
pymatgen-analysis-defects: A Python package for analyzing point defects in crystalline materials
Authors
Lawrence Livermore National Laboratory, Livermore, California 94550, United States
Lawrence Livermore National Laboratory, Livermore, California 94550, United States
Editor
Rachel Kurchin
Tags
python materials science point defects finite-size corrections database buildingGitHub Events
Total
- Create event: 4
- Issues event: 3
- Release event: 2
- Watch event: 13
- Delete event: 4
- Issue comment event: 19
- Push event: 84
- Pull request review event: 5
- Pull request review comment event: 8
- Pull request event: 20
- Fork event: 3
Last Year
- Create event: 4
- Issues event: 3
- Release event: 2
- Watch event: 13
- Delete event: 4
- Issue comment event: 19
- Push event: 84
- Pull request review event: 5
- Pull request review comment event: 8
- Pull request event: 20
- Fork event: 3
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| @jmmshn | j****n@g****m | 677 |
| nwinner | n****r@b****u | 48 |
| dependabot[bot] | 4****] | 38 |
| pre-commit-ci[bot] | 6****] | 36 |
| Seán Kavanagh | 5****e | 11 |
| Patrick Huck | p****k@l****v | 4 |
| github-actions[bot] | 4****] | 3 |
| Janosh Riebesell | j****l@g****m | 2 |
| Kyle Niemeyer | k****r@f****m | 1 |
| Jinzhe Zeng | j****g@r****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 21
- Total pull requests: 135
- Average time to close issues: about 1 month
- Average time to close pull requests: 21 days
- Total issue authors: 15
- Total pull request authors: 9
- Average comments per issue: 2.95
- Average comments per pull request: 1.21
- Merged pull requests: 108
- Bot issues: 0
- Bot pull requests: 61
Past Year
- Issues: 3
- Pull requests: 10
- Average time to close issues: N/A
- Average time to close pull requests: about 2 months
- Issue authors: 3
- Pull request authors: 4
- Average comments per issue: 1.0
- Average comments per pull request: 1.6
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 5
Top Authors
Issue Authors
- misaraty (3)
- ireaml (2)
- kavanase (2)
- njzjz (2)
- AlexHeustc (1)
- mwswift (1)
- kinziefarnell (1)
- msehabibur (1)
- yliu1240 (1)
- Luftalian (1)
- kayahans (1)
- whyydsforever (1)
- JiQi535 (1)
- Andrew-S-Rosen (1)
- yayapa (1)
Pull Request Authors
- jmmshn (58)
- dependabot[bot] (39)
- pre-commit-ci[bot] (21)
- nwinner (8)
- kavanase (7)
- github-actions[bot] (5)
- janosh (2)
- tschaume (1)
- kyleniemeyer (1)
- njzjz (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 240,484 last-month
- Total dependent packages: 10
- Total dependent repositories: 2
- Total versions: 54
- Total maintainers: 1
pypi.org: pymatgen-analysis-defects
Pymatgen extension for defects analysis
- Documentation: https://pymatgen-analysis-defects.readthedocs.io/
- License: modified BSD
-
Latest release: 2025.1.18
published 12 months ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pypa/gh-action-pypi-publish release/v1 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v1 composite
- peaceiris/actions-gh-pages v3 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- peter-evans/create-pull-request v3 composite
- jupyter-book *
- matplotlib *
- numpy *
- pymatgen >=2022.10.22
- scikit-image >=0.19.3