An automated framework for exploring and learning potential-energy surfaces
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917876153188352 |
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| author | Liu, Yuanbin Morrow, Joe D. Ertural, Christina Fragapane, Natascia L. Gardner, John L. A. Naik, Aakash A. Zhou, Yuxing George, Janine Deringer, Volker L. |
| author_facet | Liu, Yuanbin Morrow, Joe D. Ertural, Christina Fragapane, Natascia L. Gardner, John L. A. Naik, Aakash A. Zhou, Yuxing George, Janine Deringer, Volker L. |
| contents | Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An automated framework for exploring and learning potential-energy surfaces Liu, Yuanbin Morrow, Joe D. Ertural, Christina Fragapane, Natascia L. Gardner, John L. A. Naik, Aakash A. Zhou, Yuxing George, Janine Deringer, Volker L. Computational Physics Materials Science Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science. |
| title | An automated framework for exploring and learning potential-energy surfaces |
| topic | Computational Physics Materials Science |
| url | https://arxiv.org/abs/2412.16736 |