# aenet_env

[![en](https://img.shields.io/badge/lang-en-red.svg)](https://ohara-lab-su.github.io/aenet_env/en/index.html)
[![ja](https://img.shields.io/badge/lang-ja-yellow.svg)](https://ohara-lab-su.github.io/aenet_env/ja/index.html)

[![GitHub](https://img.shields.io/badge/GitHub-ohara--lab--su%2Faenet_env-181717?logo=github)](https://github.com/ohara-lab-su/aenet_env/)
[![GitHub Pages](https://img.shields.io/badge/GitHub%20Pages-Documentation-222222?logo=githubpages&logoColor=white)](https://ohara-lab-su.github.io/aenet_env/en/)
[![Ohara Lab](https://img.shields.io/badge/Ohara%20Lab-Home-4B5563?logo=homeassistant&logoColor=white)](https://ohara-lab-su.github.io/index.en.html)

Python tools for ænet ANN interatomic potentials, including reference-structure preparation, VASP integration,
training-data collection, model training, and computational workflow and environment setup.

## Documentation

```{toctree}
:maxdepth: 2

installation
workflow
training
rmc_design
project_readme
api/index
```