Automated generation of structure datasets for machine learning potentials and alloys
Abstract We propose a strategy for generating unbiased and systematically extendable training data for machine learning interatomic potentials (MLIP) for multicomponent alloys, called Automated Small SYmmetric Structure Training or ASSYST. Based on exploring the full space of random crystal structur...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01669-4 |
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