Metadata-Version: 2.4
Name: carcara
Version: 25.7.0
Summary: Towards Explainable, Scalable, and Accurate Machine-Learned Interatomic Potentials
Project-URL: Homepage, https://github.com/seixasgroup/carcara
Project-URL: Documentation, https://seixasgroup.github.io/carcara/
Project-URL: Bug Tracker, https://github.com/seixasgroup/carcara/issues
Author-email: Leandro Seixas <leandro.fisica@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Leandro Seixas
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Requires-Dist: ase
Requires-Dist: matplotlib
Requires-Dist: ml-dtypes
Requires-Dist: networkx
Requires-Dist: numpy>=2.0.0
Requires-Dist: pandas
Requires-Dist: pytest
Requires-Dist: pyyaml
Requires-Dist: scipy
Requires-Dist: torch
Requires-Dist: torch-geometric
Description-Content-Type: text/markdown

<h1 align="center" style="margin-top:20px; margin-bottom:50px;">

<a href="https://github.com/seixasgroup/carcara" target="_blank" rel="noopener noreferrer">
  <picture>
    <source srcset="https://raw.githubusercontent.com/seixasgroup/carcara/refs/heads/main/logo/logo_dark.png" media="(prefers-color-scheme: dark)">
    <source srcset="https://raw.githubusercontent.com/seixasgroup/carcara/refs/heads/main/logo/logo_light.png" media="(prefers-color-scheme: light)">
    <img src="https://raw.githubusercontent.com/seixasgroup/carcara/refs/heads/main/logo/logo_light.png" alt="Carcará logo" style="height: auto; width: auto; max-height: 100px;">
  </picture>
</a>
</h1> 

[![License: MIT](https://img.shields.io/github/license/seixasgroup/carcara?color=green&style=for-the-badge)](LICENSE)    [![PyPI](https://img.shields.io/pypi/v/carcara?color=red&style=for-the-badge)](https://pypi.org/project/carcara/)

# Carcará

🚧 **(Under development)** 🚧

Towards Explainable, Scalable, and Accurate Machine-Learned Interatomic Potentials

# Installation

## From Pip
The easiest way to install Carcará is with pip:

```python
pip install carcara
```

# Getting started

## Training

```yaml

model: "MPNN"
name: "my_model"
training_dataset: "training.xyz"
validation_dataset: "validation.xyz"
test_dataset: "test.xyz"
cutoff_radius: 6.0
num_channels: 64
l_max: 1
mp_layers: 2
manybody_correlation: 3
energy_key: "REF_energy"
forces_key: "REF_forces"
energy_weight: 10
forces_weight: 1000
seed: 42
device: cpu

```

## Evaluation

```python

# TODO

```

# License

This is an open source code under [MIT License](LICENSE).

# Acknowledgements

We thank financial support from FAPESP (Grant No. 2022/14549-3), INCT Materials Informatics (Grant No. 406447/2022-5), and CNPq (Grant No. 311324/2020-7).
