ASE friendly implementation of the a2c workflow with MLIPs
Project description
a2c_ase
An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow.
Installation
From PyPI
With uv (recommended):
uv pip install a2c-ase
Or with pip
pip install a2c-ase
From Source
With uv:
git clone https://github.com/abhijeetgangan/a2c_ase.git
cd a2c_ase
uv pip install .
Or with pip
git clone https://github.com/abhijeetgangan/a2c_ase.git
cd a2c_ase
pip install .
Usage
See 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.
pip install mace-torch
Workflow Overview
- Initial Structure: Generate a random atomic configuration with specified composition and volume.
- Melt-Quench: Run MD simulation to create an amorphous structure.
- Subcell Extraction: Identify potential crystalline motifs within the amorphous structure.
- Structure Optimization: Relax subcells to find stable crystalline phases.
- Analysis: Characterize discovered structures using symmetry analysis.
Development
Install dev dependencies:
# with pip
pip install -e ".[dev,test]"
Set up pre-commit hooks:
pre-commit install
Run checks:
ruff check # lint
ruff format # format
ty check # type check
pytest # test
Citation
If you use this software in your research, please cite it: DOI:https://doi.org/10.5281/zenodo.17355689
References
- 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
- Reference implementation: a2c-workflow
Project details
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