Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-21806-z |