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Ceram. Soc. Jpn. 131, 762 (2023)](https://www.jstage.jst.go.jp/article/jcersj2/131/10/131_23053/_article/-char/ja/)\n```\n@article{HayatoWakai202323053,\n  title=\"{Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system}\",\n  author={Hayato Wakai and Atsuto Seko and Isao Tanaka},\n  journal={J. Ceram. Soc. Jpn.},\n  volume={131},\n  number={10},\n  pages={762-766},\n  year={2023},\n  doi={10.2109/jcersj2.23053}\n}\n```\n\n## Installation\n\n### Required libraries and python modules\n\n- python \u003e= 3.10\n- scikit-learn\n- joblib\n- pypolymlp\n- spglib\n- symfc\n\n[Optional]\n- matplotlib (if plotting RSS results)\n- seaborn (if plotting RSS results)\n\n### How to install\n- Install from conda-forge\n\n| Name | Downloads | Version | Platforms |\n| --- | --- | --- | --- |\n| [![Conda Recipe](https://img.shields.io/badge/recipe-rsspolymlp-green.svg)](https://anaconda.org/conda-forge/rsspolymlp) | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/rsspolymlp.svg)](https://anaconda.org/conda-forge/rsspolymlp) | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/rsspolymlp.svg)](https://anaconda.org/conda-forge/rsspolymlp) | [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/rsspolymlp.svg)](https://anaconda.org/conda-forge/rsspolymlp) |\n\n```shell\nconda create -n rsspolymlp\nconda activate rsspolymlp\nconda install -c conda-forge rsspolymlp\n```\n\n- Install from PyPI\n```shell\nconda create -n rsspolymlp\nconda activate rsspolymlp\nconda install -c conda-forge scikit-learn joblib pypolymlp spglib symfc\npip install rsspolymlp\n```\n\n## How to use rsspolymlp\n\n - [Workflow of RSS with polynomial MLPs](docs/rsspolymlp.md)\n   - Initial structure generation\n   - Global RSS with polynomial MLPs\n   - Unique structure identification and RSS result summarization\n   - Ghost minimum structure elimination\n   - Phase stability analysis\n - [Development kit for polynomial MLPs](docs/rsspolymlp_devkit.md)\n   - MLP dataset generation\n   - DFT dataset division\n   - Polynomial MLP development\n   - Pareto-optimal MLP selection\n - Python API\n   - [RSS workflow](docs/api_rsspolymlp.md)\n   - [VASP calculation utilities](src/rsspolymlp/utils/vasp_util/readme.md)\n     - Single-point calculation\n     - Local geometry optimization\n   - [Matplotlib utilities](src/rsspolymlp/utils/matplot_util/readme.md)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhytwakai%2Frsspolymlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhytwakai%2Frsspolymlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhytwakai%2Frsspolymlp/lists"}