PyAMFF: Python Atom-Centered Machine Learning Force Field

PyAMFF is a set of tools for fitting and using atomistic machine learning potentials.

Webpage: https://pyamff.gitlab.io/pyamff/index.html

Requirements:

• Python 3.7 or later

• Pytorch 1.4.0

• ASE

• NumPy

• PyYAML

• Pickle

Optional:

• gcc-gfortran

Installation:

• Using git with ssh:

  $ git clone git@gitlab.com:pyamff/pyamff.git
  

• Using git with HTML:

  $ git clone https://gitlab.com/pyamff/pyamff.git
  

• To build fortran modules:

  $ cd pyamff/pyamff/
  $ make
  

• To build the PyAMFF potential for EON:

  $ cd pyamff/pyamff/
  $ make EON=1
  

Once you have compiled the library, copy libAMFF.a to the eon/client/potentials/PyAMFF directory.

• The eon client can be built in eon/client/ using

  $ make PYAMFF_POT=1
  $ cp eonclient ../bin/
  

*Any PyAMFF generated potentials can then be run in eon by setting the potential flag in the EON config.ini file as

potential = pyamff

• Make sure to add /path/to/pyamff/bin to your $PATH and /path/to/pyamff/ to your $PYTHONPATH

Note

After installation, you can go to /pyammf/tests/ and run run_tests.sh to make sure the build was successful

The PyAMFF team

Henkelman Group (UT Austin)

Indices and tables

• Index

• Module Index

• Search Page