A generator of polynomial machine learning potentials

pypolymlp is a Python code designed for the development of polynomial machine learning potentials (MLPs) based on datasets generated from density functional theory (DFT) calculations. The code provides functionalities for fitting polynomial models to energy, force, and stress data, enabling the construction of accurate and computationally efficient interatomic potentials. In addition to potential development, pypolymlp allows users to compute various physical properties and perform atomistic simulations using the trained MLPs.

Polynomial machine learning potentials

A polynomial MLP represents the potential energy as a polynomial function of linearly independent polynomial invariants of the O(3) group. Developed polynomial MLPs are available in 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 >= 3.10
numpy != 2.0.*
scipy
pyyaml
eigen3
pybind11
openmp (recommended)

[Optional]

phonopy, phono3py
spglib
symfc
sparse_dot_mkl
ase
joblib
matplotlib, seaborn

How to install pypolymlp

Install from conda-forge (Recommended)

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.

Using pypolymlp for Polynomial MLP Development

To develop polynomial MLPs from datasets obtained from DFT calculations, both the command-line interface and the Python API are available. Several procedures for generating structures used in DFT calculations are also supported.

Tutorials
1. Development of a single on-the-fly MLP
2. Development of a single general-purpose MLP
3. Development of convex hull (Pareto-optimal) MLPs
MLP Developments
Utilities for MLP development
Experimental Features
- Fine tuning of MLP using online learning with Adam

Using pypolymlp for Polynomial MLP Calculations

In version 0.8.0 and earlier, polymlp files are generated in plain text format as polymlp.lammps. Many polynomial MLPs distributed in the Polynomial MLP Repository are also provided in this plain text format. Starting from version 0.9.0, polymlp files are generated in YAML format as polymlp.yaml. Both formats are supported by the command-line interface and the Python API.

The following calculations can be performed using pypolymlp with either polymlp.yaml or polymlp.lammps files.

Notes on hybrid polynomial MLPs
Property calculations and simulation
Experimental features
How to use polymlp in other calculator tools
- LAMMPS
- ASE
- OpenKIM
- phonopy and phonon3py

Theoretical Background

Polynomial Invariants and Potential Models

Metadata

Release files for pypolymlp 0.21.7

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 0.21.7
File	Size	Uploaded
pypolymlp-0.21.7.tar.gz	77.0 MB	Sep 30, 2026	Details

Release files / pypolymlp-0.21.7.tar.gz

Download URL	pypolymlp-0.21.7.tar.gz
Size	77.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`03f8cec0a2aeb97176868c692324882f20d44c30ce55cc1351953d9495d97194`
BLAKE2b-256 checksum How to use checksums	`ebf58a30cfb985df73a95ac852674262f76448e2d61d80433e42834cd5a72f30`
Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
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