https://github.com/hytwakai/rsspolymlp
A framework for random structure search using polynomial MLPs
https://github.com/hytwakai/rsspolymlp
computational-physics material-science materials-informatics physics
Last synced: 5 months ago
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A framework for random structure search using polynomial MLPs
- Host: GitHub
- URL: https://github.com/hytwakai/rsspolymlp
- Owner: hytwakai
- License: mit
- Created: 2025-03-09T12:32:03.000Z (over 1 year ago)
- Default Branch: develop
- Last Pushed: 2025-11-16T06:47:19.000Z (7 months ago)
- Last Synced: 2025-12-20T11:42:41.447Z (6 months ago)
- Topics: computational-physics, material-science, materials-informatics, physics
- Language: Python
- Homepage:
- Size: 36.8 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# A framework for random structure search (RSS) using polynomial MLPs
## Citation of rsspolymlp
If you use `rsspolymlp` in your study, please cite the following articles.
“Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system”, [J. Ceram. Soc. Jpn. 131, 762 (2023)](https://www.jstage.jst.go.jp/article/jcersj2/131/10/131_23053/_article/-char/ja/)
```
@article{HayatoWakai202323053,
title="{Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system}",
author={Hayato Wakai and Atsuto Seko and Isao Tanaka},
journal={J. Ceram. Soc. Jpn.},
volume={131},
number={10},
pages={762-766},
year={2023},
doi={10.2109/jcersj2.23053}
}
```
## Installation
### Required libraries and python modules
- python >= 3.10
- scikit-learn
- joblib
- pypolymlp
- spglib
- symfc
[Optional]
- matplotlib (if plotting RSS results)
- seaborn (if plotting RSS results)
### How to install
- Install from conda-forge
| Name | Downloads | Version | Platforms |
| --- | --- | --- | --- |
| [](https://anaconda.org/conda-forge/rsspolymlp) | [](https://anaconda.org/conda-forge/rsspolymlp) | [](https://anaconda.org/conda-forge/rsspolymlp) | [](https://anaconda.org/conda-forge/rsspolymlp) |
```shell
conda create -n rsspolymlp
conda activate rsspolymlp
conda install -c conda-forge rsspolymlp
```
- Install from PyPI
```shell
conda create -n rsspolymlp
conda activate rsspolymlp
conda install -c conda-forge scikit-learn joblib pypolymlp spglib symfc
pip install rsspolymlp
```
## How to use rsspolymlp
- [Workflow of RSS with polynomial MLPs](docs/rsspolymlp.md)
- Initial structure generation
- Global RSS with polynomial MLPs
- Unique structure identification and RSS result summarization
- Ghost minimum structure elimination
- Phase stability analysis
- [Development kit for polynomial MLPs](docs/rsspolymlp_devkit.md)
- MLP dataset generation
- DFT dataset division
- Polynomial MLP development
- Pareto-optimal MLP selection
- Python API
- [RSS workflow](docs/api_rsspolymlp.md)
- [VASP calculation utilities](src/rsspolymlp/utils/vasp_util/readme.md)
- Single-point calculation
- Local geometry optimization
- [Matplotlib utilities](src/rsspolymlp/utils/matplot_util/readme.md)