Systematic assessment of various universal machine-learning interatomic potentials
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929428246822912 |
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| author | Yu, Haochen Giantomassi, Matteo Materzanini, Giuliana Wang, Junjie Rignanese, Gian-Marco |
| author_facet | Yu, Haochen Giantomassi, Matteo Materzanini, Giuliana Wang, Junjie Rignanese, Gian-Marco |
| contents | Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate five different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_05729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Systematic assessment of various universal machine-learning interatomic potentials Yu, Haochen Giantomassi, Matteo Materzanini, Giuliana Wang, Junjie Rignanese, Gian-Marco Materials Science Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate five different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science. |
| title | Systematic assessment of various universal machine-learning interatomic potentials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2403.05729 |