Metadata-Version: 2.4
Name: a2c_ase
Version: 0.0.2
Summary: ASE friendly implementation of the a2c workflow with MLIPs
Author-email: Abhijeet Gangan <abhijeetgangan@g.ucla.edu>
License: MIT License
        
        Copyright (c) 2025 Abhijeet Gangan
        
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Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: ase
Requires-Dist: numpy
Requires-Dist: pymatgen
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Provides-Extra: dev
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: ty>=0.0.1a20; extra == "dev"
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Dynamic: license-file

# a2c_ase

[![PyPI](https://img.shields.io/pypi/v/a2c-ase.svg)](https://pypi.org/project/a2c-ase/)
[![CI](https://github.com/abhijeetgangan/a2c_ase/actions/workflows/ci.yml/badge.svg)](https://github.com/abhijeetgangan/a2c_ase/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/abhijeetgangan/a2c_ase/branch/main/graph/badge.svg)](https://codecov.io/gh/abhijeetgangan/a2c_ase)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)

An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow.

## Installation

### From PyPI

**With uv (recommended):**
```bash
uv pip install a2c-ase
```

<details>
<summary>Or with pip</summary>

```bash
pip install a2c-ase
```
</details>

### From Source

**With uv:**
```bash
git clone https://github.com/abhijeetgangan/a2c_ase.git
cd a2c_ase
uv pip install .
```

<details>
<summary>Or with pip</summary>

```bash
git clone https://github.com/abhijeetgangan/a2c_ase.git
cd a2c_ase
pip install .
```
</details>
## Usage
See [example/Si64.py](https://github.com/abhijeetgangan/a2c_ase/blob/main/example/Si64.py) for basic usage.

To use a specific calculator you need to install the corresponding package.

In the example above, MACE is used as the calculator, so you need to install the corresponding package.

```bash
pip install mace-torch
```

## Workflow Overview

1. **Initial Structure**: Generate a random atomic configuration with specified composition and volume.
2. **Melt-Quench**: Run MD simulation to create an amorphous structure.
3. **Subcell Extraction**: Identify potential crystalline motifs within the amorphous structure.
4. **Structure Optimization**: Relax subcells to find stable crystalline phases.
5. **Analysis**: Characterize discovered structures using symmetry analysis.

## Development

Install dev dependencies:
```bash
# with pip
pip install -e ".[dev,test]"
```

Set up pre-commit hooks:
```bash
pre-commit install
```

Run checks:
```bash
ruff check         # lint
ruff format        # format
ty check           # type check
pytest             # test
```

## References

1. Aykol, M., Merchant, A., Batzner, S. et al. Predicting emergence of crystals from amorphous precursors with deep learning potentials. Nat Comput Sci 5, 105–111 (2025). [DOI: 10.1038/s43588-024-00752-y](https://doi.org/10.1038/s43588-024-00752-y)
2. Reference implementation: [a2c-workflow](https://github.com/jax-md/jax-md/blob/main/jax_md/a2c/a2c_workflow.py)
