Hyperactive learning for data-driven interatomic potentials
Abstract Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database. To aid this process hyperactive learni...
Furkejuvvon:
| Váldodahkkit: | , , , , |
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| Materiálatiipa: | Artigo |
| Giella: | Inglês |
| Almmustuhtton: |
Nature Portfolio
2023-09-01
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| Ráidu: | npj Computational Materials |
| Liŋkkat: | https://doi.org/10.1038/s41524-023-01104-6 |
| Fáddágilkorat: |
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