Metadata-Version: 2.5
Name: hydrogen-local-energetics
Version: 0.1.0
Summary: Figure-reproduction tools for local hydrogen energetics and lattice-controlled mobility
Project-URL: Repository, https://github.com/lyullee/hydrogen-local-energetics
Project-URL: Dataset, https://doi.org/10.5281/zenodo.22090127
Author-email: Ugwiyeon Lee <lyullee@kgs.or.kr>
License-Expression: MIT
License-File: LICENSE
Keywords: DFT,hydrogen,machine-learning potential,molecular dynamics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Requires-Dist: matplotlib>=3.7
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Requires-Dist: scipy>=1.10
Description-Content-Type: text/markdown

# hydrogen-local-energetics

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.22090127.svg)](https://doi.org/10.5281/zenodo.22090127)

This lightweight Python package regenerates the manuscript figures from the publicly archived Hydrogen Local Energetics dataset. The DFT, MD and machine-learning-potential result data are preserved separately on Zenodo and are not bundled in the wheel.

## Installation

```bash
python -m pip install hydrogen-local-energetics
```

## Data

Download and extract the archived dataset from https://doi.org/10.5281/zenodo.22090127.

## Reproduce the figures

```bash
hle-figures "C:\path\to\extracted_archive" --output-dir "C:\path\to\figures"
```

The command accepts either the extracted archive root or its `data` directory and writes editable SVG and high-resolution PNG files to the selected output directory.

## Scope

The package reproduces figures from archived numerical results. It does not redistribute MACE-OMat-0 or CHGNet model weights and does not rerun DFT, NEB or molecular-dynamics calculations.

## Citation

Please cite the associated article and the archived dataset: https://doi.org/10.5281/zenodo.22090127.
