metatomic is a library that defines a common interface between atomistic
machine learning models, and atomistic simulation engines. Our main goal is to
define and train models once, and then be able to re-use them across many
different simulation engines (such as LAMMPS, GROMACS, etc.). We strive to
achieve this goal without imposing any structure on the model itself, and to
allow any model architecture to be used.
For details, tutorials, and examples, please have a look at our documentation.
Thanks goes to all people that make metatomic possible:
We always welcome new contributors. If you want to help us take a look at our contribution guidelines and afterwards you may start with an open issue marked as good first issue.