Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, howe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-12-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-020-00467-4 |