a2c_ase

An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow for predicting crystal emergence from amorphous precursors using machine learning interatomic potentials.

Overview

The a2c workflow predicts crystalline structures from amorphous materials through:

• Melt-quench molecular dynamics simulations
• Systematic subcell extraction and optimization
• Structure validation and space group analysis

Learn more: Workflow Guide

Getting Started

• Installation
• Quick Start
• Examples

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

2. Reference implementation: a2c-workflow