Metadata-Version: 2.4
Name: metatrain
Version: 2025.9.1
Summary: Training and evaluating machine learning models for atomistic systems.
Author: metatrain developers
License-Expression: BSD-3-Clause
Project-URL: homepage, https://metatensor.github.io/metatrain
Project-URL: documentation, https://metatensor.github.io/metatrain
Project-URL: repository, https://github.com/metatensor/metatrain
Project-URL: issues, https://github.com/metatensor/metatrain/issues
Project-URL: changelog, https://metatensor.github.io/metatrain/latest/dev-docs/changelog.html
Keywords: machine learning,molecular modeling
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: ase
Requires-Dist: huggingface_hub
Requires-Dist: metatensor-learn<0.4,>=0.3.2
Requires-Dist: metatensor-operations<0.4,>=0.3.4
Requires-Dist: metatensor-torch<0.8,>=0.7.6
Requires-Dist: metatomic-torch<0.2,>=0.1.2
Requires-Dist: jsonschema
Requires-Dist: omegaconf>=2.3.0
Requires-Dist: python-hostlist
Requires-Dist: vesin
Provides-Extra: soap-bpnn
Requires-Dist: torch-spex<0.2,>=0.1; extra == "soap-bpnn"
Requires-Dist: wigners; extra == "soap-bpnn"
Provides-Extra: deprecated-pet
Requires-Dist: pet-neighbors-convert; extra == "deprecated-pet"
Requires-Dist: torch_geometric; extra == "deprecated-pet"
Requires-Dist: matscipy; extra == "deprecated-pet"
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Requires-Dist: pathos; extra == "deprecated-pet"
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Provides-Extra: pet
Provides-Extra: nanopet
Provides-Extra: gap
Requires-Dist: featomic-torch<0.8,>=0.7; extra == "gap"
Requires-Dist: skmatter; extra == "gap"
Requires-Dist: scipy; extra == "gap"
Dynamic: license-file

<h1 align="center">
    <img src="https://raw.githubusercontent.com/metatensor/metatrain/refs/heads/main/docs/src/logo/metatrain-horizontal-dark.svg" alt="Metatensor logo" width="600"/>
</h1>

<h4 align="center">

[![tests status](https://img.shields.io/github/checks-status/metatensor/metatrain/main)](https://github.com/metatensor/metatrain/actions?query=branch%3Amain)
[![documentation](https://img.shields.io/badge/📚_documentation-latest-sucess)](https://metatensor.github.io/metatrain)
[![coverage](https://codecov.io/gh/metatensor/metatrain/branch/main/graph/badge.svg)](https://codecov.io/gh/metatensor/metatrain)
</h4>

<!-- marker-introduction -->

`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 [metatomic](https://docs.metatensor.org/metatomic) interface.

The idea behind `metatrain` is to have a general hub that provides a homogeneous
environment and user interface, transforming every ML architecture into an end-to-end
model that can be connected to an MD engine. Any custom architecture compatible with
[TorchScript](https://pytorch.org/docs/stable/jit.html) can be integrated into
`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.

<!-- marker-architectures -->

# 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).                                       |
| PET                      | Point Edge Transformer (PET), interatomic machine learning potential                                                                 |
| NanoPET *(experimental)* | Re-implementation of the original PET with slightly improved training and evaluation speed                                           |
| PET *(deprecated)*       | Original implementation of the PET model used for prototyping, now deprecated in favor of the native `metatrain` PET implementation. |
| SOAP BPNN                | A Behler-Parrinello neural network with SOAP features                                                                                |

<!-- marker-documentation -->

# Documentation

For details, tutorials, and examples, please visit our
[documentation](https://metatensor.github.io/metatrain/latest/).

<!-- marker-installation -->

# Installation

Install `metatrain` with pip:

```bash
pip install metatrain
```

Install specific models by specifying the model name. For example, to install the SOAP-BPNN model:

```bash
pip install metatrain[soap-bpnn]
```

We also offer a conda installation:

```bash
conda install -c conda-forge metatrain
```

> ⚠️ The conda installation does not install model-specific dependencies and will only
> work for architectures without optional dependencies such as NanoPET or PET.

After installation, you can use mtt from the command line to train your models!

<!-- marker-quickstart -->

# Quickstart

To train a model, use the following command:

```bash
mtt train options.yaml
```

Where options.yaml is a configuration file specifying training options. For example, the
following configuration trains a *SOAP-BPNN* model on the QM9 dataset:

```yaml
# architecture used to train the model
architecture:
  name: soap_bpnn
training:
  num_epochs: 5  # a very short training run

# Mandatory section defining the parameters for system and target data of the training set
training_set:
  systems: "qm9_reduced_100.xyz"  # file where the positions are stored
  targets:
    energy:
      key: "U0"      # name of the target value
      unit: "eV"     # unit of the target value

test_set: 0.1        # 10% of the training_set are randomly split for test
validation_set: 0.1  # 10% of the training_set are randomly split for validation
```

<!-- marker-shell -->

# Shell Completion

`metatrain` comes with completion definitions for its commands for bash and zsh. You
must manually configure your shell to enable completion support.

To make the completions available, source the definitions in your shell’s startup file
(e.g., `~/.bash_profile`, `~/.zshrc`, or `~/.profile`):

```bash
source $(mtt --shell-completion)
```

<!-- marker-issues -->

# Having problems or ideas?

Having a problem with metatrain? Please let us know by submitting an issue.

Submit new features or bug fixes through a pull request.

<!-- marker-contributing -->

# Contributors

Thanks goes to all people who make metatrain possible:

[![Contributors](https://contrib.rocks/image?repo=metatensor/metatrain)](https://github.com/metatensor/metatrain/graphs/contributors)
