Discrepancies and error evaluation metrics for machine learning interatomic potentials
Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulation...
| Main Authors: | , , |
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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/s41524-023-01123-3 |