| Name | rsspolymlp JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | A framework for random structure search using polynomial MLPs |
| upload_time | 2025-07-14 14:18:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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 >= 3.9
- 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 detection
- [Python API for RSS](docs/api_rsspolymlp.md)
- [VASP calculation utilities](src/rsspolymlp/utils/vasp_util/readme.md)
- Single-point calculation
- Local geometry optimizaion
- [Matplotlib utilities](src/rsspolymlp/utils/matplot_util/readme.md)
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