PyAMFF is a set of tools for fitting and using atomistic machine learning potentials.
Webpage: https://pyamff.gitlab.io/pyamff/index.html
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 $PATH/TO/pyamff/pyamff
$ make
To build the PyAMFF potential for EON:
$ cd $PATH/TO/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
Note: This assumes that you’ve compiled the Fortran modules
A trajectory file that contains training data (named train.traj)
A config.ini file. (See the documentation for details on the flags)
These should be located in their own directory, where PyAMFF will write mlff.pyamff
and pyamff.pt
files.
If you’re running PyAMFF on a slurm enabled cluster, you’ll need to create a submission script:
# [SLURM DIRECTIVES]
[load GNU compiler toolchain into environment]
pyamff
On a shell:
$ [load GNU compiler toolchain into environment]
$ pyamff
Also see the ``pyamff/examples/pytorchNN`` directory for a sample of how to use PyAMFF.
For more examples, see Tutorials.
Henkelman Group (UT Austin) Lei Li Group (SUSTC)