rsspolymlp 0.2.1

A framework for random structure search using polynomial MLPs

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)

@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

conda create -n rsspolymlp
conda activate rsspolymlp
conda install -c conda-forge rsspolymlp

• Install from PyPI

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
  • 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
  • MLP dataset generation
  • DFT dataset division
  • Polynomial MLP development
  • Pareto-optimal MLP detection
• Python API for RSS
• VASP calculation utilities
  • Single-point calculation
  • Local geometry optimizaion
• Matplotlib utilities