Metatensor is a specialized data storage format for all your atomistic machine learning needs, and more. Think numpy ndarray or pytorch Tensor equipped with extra metadata for atomic β€” and other particles β€” systems.

πŸš€ Getting started

Install the right version of metatensor for your programming language! The core of this library is written in Rust and we provide API for C, C++, and Python.

πŸ’‘ What is metatensor

Learn about the core goals of metatensor, and what the library is about:

  • an exchange format for ML data;

  • a prototyping tool for new models;

  • an interface for atomistic simulations.

πŸ› οΈ Core classes

Explore the core types of metatensor: TensorMap, TensorBlock and Labels, and discover how to used them.

πŸ“ˆ Operations

Use operations to manipulate the core types of metatensor and write new algorithms operating on metatensor’s sparse data.

πŸ”₯ TorchScript interface

Learn about the TorchScript version of metatensor, used to export and execute custom models inside non-Python software.

πŸ§‘β€πŸ’» Learning utilities

Use the utility class with the same API as torch or scikit-learn to train models using metatensor!

βš›οΈ Running atomistic simulations

Learn about the facilities provided to define atomistic models, and use them to run molecular dynamics simulations and more!