Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accurac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-03-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-021-00510-y |