Welcome to metatensor-models!¶
What is metatensor-models?¶
The idea behind metatensor-models is to have a general hub
that provide an homogeneous enviroment and user interface to
train, export and evaluate ML models and to
connect those models with various MD engines (e.g. LAMMPS, i-PI, ASE …).
metatensor-models is the tool that transforms every ML architecture in an
end-to-end model. Any custom ML architecture compatible with TorchScript
can be integrated in metatensor-models, gaining automatic access to
a training and evaluation interface, as well as compatibility with various MD engines.
Note: metatensor-models does not provide per se mathematical functionalities but
relies on external models that implement the various architectures.
Features¶
Custom ML Architecture: Integrate any TorchScriptable ML model to explore innovative architectures.
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. It uses the
hydramodule to easy management of folder and files.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 metatensor-models supports the following architectures for building an atomistic model.
Name |
Description |
|---|---|
SOAP BPNN |
A Behler-Parrinello neural network with SOAP features |
Alchemical Model |
A Behler-Parrinello neural network with SOAP features and Alchemical Compression of the composition space |