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
pymatgen
ase

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)
- Phonon properties, Quasi-harmonic approximation
- Local geometry optimization
- Molecular dynamics
- Thermodynamic integration using MD
DFT structure generator
- Random atomic displacements with constant magnitude
- Random atomic displacements with sequential magnitudes and volume changes
- Random atomic displacements, cell expansion, and distortion
Utilities
- Compression of vasprun.xml files
- Automatic division of DFT dataset
- Atomic energies
- Enumeration of optimal MLPs
- Estimation of computational costs
Python API (MLP development)
Python API (Property calculations)
- Energy, forces on atoms, and stress tensor
- Force constants
- Elastic constants
- Equation of states
- Structural features (Polynomial invariants)
- Phonon properties, Quasi-harmonic approximation
- Local geometry optimization
- Molecular dynamics
- Thermodynamic integration using MD
- Self-consistent phonon calculations
How to use polymlp in other calculator tools
- LAMMPS
- Phonopy
- ASE

Metadata

Release files for pypolymlp 0.11.4.post26

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pypolymlp 0.11.4.post26
File	Size	Uploaded
pypolymlp-0.11.4.post26.tar.gz	41.8 MB	May 23, 2025	Details

Release files / pypolymlp-0.11.4.post26.tar.gz

Download URL	pypolymlp-0.11.4.post26.tar.gz
Size	41.8 MB
Tags	Source
SHA-256 checksum How to use checksums	`a127abe7371f3c420c10a0bd1b45928339d4cd55a5f3433924171f011c85e893`
BLAKE2b-256 checksum How to use checksums	`9aeed7112d3440f8baccc9974eae8b0b772b402c4a194cb53789b8c27f508c3a`
Upload date	May 23, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.9`