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An automated framework for exploring and learning potential-energy surfaces
Yuanbin Liu1, Joe D Morrow1, Christina Ertural2
1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford, UK.
Nature Communications
|August 18, 2025
Summary
Developing machine-learned interatomic potentials is accelerated by autoplex, an automated framework for exploring and fitting potential-energy surfaces. This innovation streamlines data generation, overcoming a key bottleneck in computational materials science.
Area of Science:
- Computational Materials Science
- Materials Modelling
- Machine Learning
Background:
- Machine learning (ML) is integral to materials modeling, enabling large-scale atomistic simulations with quantum-mechanical accuracy.
- Developing accurate ML interatomic potentials necessitates high-quality training data, but manual data generation and curation present a significant bottleneck.
Purpose of the Study:
- Introduce an automated framework, autoplex, for efficient exploration and fitting of potential-energy surfaces.
- Enhance the speed and accessibility of atomistic machine learning in computational materials science.
Main Methods:
- Developed an open-source software package, autoplex, for automated potential-energy surface exploration and fitting.
- Focused on interoperability with existing software architectures and user-friendly computational workflows.
Main Results:
- Demonstrated autoplex's capability across diverse systems: titanium-oxygen, SiO2, crystalline and liquid water, and phase-change memory materials.
- Successfully automated the generation and curation of training data for machine-learned interatomic potentials.
Conclusions:
- Automation significantly accelerates the development of machine-learned interatomic potentials.
- autoplex provides a versatile and efficient solution to the data bottleneck in atomistic machine learning for materials science.
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