pypolymlp 0.4.2

This is the pypolymlp module.

A generator of polynomial machine learning potentials

Polynomial machine learning potentials

• Polynomial machine learning potential repository

Citation of pypolymlp

“Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems”, A. Seko, J. Appl. Phys. 133, 011101 (2023)

@article{pypolymlp,
    author = {Seko, Atsuto},
    title = "{"Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems"}",
    journal = {J. Appl. Phys.},
    volume = {133},
    number = {1},
    pages = {011101},
    year = {2023},
    month = {01},
}

Required libraries and python modules

• python >= 3.9
• numpy != 2.0.*
• scipy
• pyyaml
• setuptools
• eigen3
• pybind11
• openmp (recommended)

[Optional]

• phonopy (if using phonon datasets and/or computing force constants)
• phono3py (if using phonon datasets and/or computing force constants)
• symfc (if computing force constants)
• sparse_dot_mkl (if computing force constants)
• spglib

How to install pypolymlp

• Install from conda-forge

Version	Last Update	Downloads	Platform	License

conda create -n pypolymlp-env
conda activate pypolymlp-env
conda install -c conda-forge pypolymlp

• Install from PyPI

conda create -n pypolymlp-env
conda activate pypolymlp-env
conda install -c conda-forge numpy scipy pybind11 eigen cmake cxx-compiler
pip install pypolymlp

Building C++ codes in pypolymlp may require a significant amount of time.

• Install from GitHub

git clone https://github.com/sekocha/pypolymlp.git
cd pypolymlp
conda create -n pypolymlp-env
conda activate pypolymlp-env
conda install -c conda-forge numpy scipy pybind11 eigen cmake cxx-compiler
pip install . -vvv

Building C++ codes in pypolymlp may require a significant amount of time.

How to use pypolymlp

• Polynomial MLP development
• Property calculators
  • Energy, forces on atoms, and stress tensor
  • Force constants
  • Elastic constants
  • Equation of states
  • Structural features (Polynomial invariants)
  • Local geometry optimization
  • Phonon properties, Quasi-harmonic approximation
  • Self-consistent phonon calculations
• Utilities
  • Random structure generation
  • Estimation of computational costs
  • Enumeration of optimal MLPs
  • Compression of vasprun.xml files
  • Automatic division of DFT dataset
  • Atomic energies
• Python API (MLP development)
• Python API (Property calculations)