A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
Understanding plastic deformation in metallic glasses is challenging due to their heterogeneous atomic environments. Here the authors propose a machine learning approach generalizable across compositions to predict the structural features from which plastic deformation is initiated in a metallic gla...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13511-9 |