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.
This library focusses on exporting and importing fully working, already trained models. If you want to train existing architectures with new data or re-use existing trained models, look into the (work in progress!) metatrain project instead.
Why should you use metatomic to define and export your model? What is the point of the interface? How can you use models that follow the interface in your own simulation code?
All of this and more will find answers in this overview!
Learn how to define your own models using metatomic, and how to use these models to run simulation in various simulation engines.
Understand the different outputs a model can have, and what the metadata should be for standardized outputs, such as the potential energy.
Explore the various simulation softwares that can use metatomic models, and what each one of them can do, from running molecular dynamics simulations to interactive dataset exploration.
Python API referenceRead the documentation for all the classes and functions related to atomistic models in Python.
C++ API referenceRead the documentation for all the classes and functions related to atomistic models in C++.