PyAMFF: Python Atom-Centered Machine Learning Force Field

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






  • Using git with ssh:

    $ git clone
  • Using git with HTML:

    $ git clone
  • 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


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

The PyAMFF team

Henkelman Group (UT Austin)

Indices and tables