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...
Main Authors: | Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-09-01
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Series: | npj Computational Materials |
Online Access: | https://doi.org/10.1038/s41524-023-01104-6 |
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