Welcome to metatrain!

What is metatrain?

metatrain is a command line interface (cli) to train and evaluate atomistic models of various architectures. It features a common yaml option inputs to configure training and evaluation. Trained models are exported as standalone files that can be used directly in various molecular dynamics (MD) engines (e.g. LAMMPS, i-PI, ASE …) using the metatensor atomistic interface.

The idea behind metatrain is to have a general hub that provide an homogeneous environment and user interface transforms every ML architecture in an end-to-end model that can be connected to an MD engine. Any custom architecture compatible with TorchScript can be integrated in metatrain, gaining automatic access to a training and evaluation interface, as well as compatibility with various MD engines.

Note: metatrain does not provide mathematical functionalities per se but relies on external models that implement the various architectures.

Features

• Custom ML Architecture: Integrate any TorchScriptable ML model

• MD Engine Compatibility: Supports various MD engines for diverse research and application needs.

• Streamlined Training: Automated process leveraging MD-generated data to optimize ML models with minimal effort.

• HPC Compatibility: Efficient in HPC environments for extensive simulations.

• Future-Proof: Extensible to accommodate advancements in ML and MD fields.

List of Implemented Architectures

Currently metatrain supports the following architectures for building an atomistic model.

Name	Description
GAP	Sparse Gaussian Approximation Potential (GAP) using Smooth Overlap of Atomic Positions (SOAP).
SOAP BPNN	A Behler-Parrinello neural network with SOAP features
PET	Point Edge Transformer (PET), interatomic machine learning potential