Nature Communications (Mar 2021)

Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

  • Zhao Fan,
  • Evan Ma

DOI
https://doi.org/10.1038/s41467-021-21806-z
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 13

Abstract

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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 static structure.