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Generator of polynomial machine learning potentials.

Project description

A generator of polynomial machine learning potentials

Polynomial machine learning potentials

Required libraries and python modules

  • numpy
  • scipy
  • pyyaml
  • Eigen3
  • pybind11
  • 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)
  • spglib (optional)
  • joblib (optional)

Installing pypolymlp using pip package

  • Intel Linux
  • Compatible with python 3.9, 3.10, and 3.11
> pip install $(pypolymlp)/dist/pypolymlp-0.1-py3-none-any.whl

Manually installing pypolymlp

  1. Conda package management system
> conda create -n pypolymlp
> conda activate pypolymlp

> conda install numpy scipy pyyaml pybind11 eigen cmake
(optional)
> conda install spglib
> conda install phono3py
> conda install joblib
  1. Building a shared library (libmlpcpp)

The process of building libmlpcpp may take approximately five minutes to one hour.

> cd $(pypolymlp)/src/pypolymlp/cxx
> cmake -S . -B build
> cmake --build build
> cmake --install build

or

> cd $(pypolymlp)/src/pypolymlp/cxx
> make

If necessary, the stack size may need to be set to unlimited.

ulimit -s unlimited
  1. Install pypolymlp using pip
> cd $(pypolymlp)
> pip install .

How to use pypolymlp

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