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It has been a long-standing materials science challenge to establishstructure-property relations in amorphous solids. Here we introduce arotation-variant local structure representation that enables differentpredictions for different loading orientations, which is found essential forhigh-fidelity prediction of the propensity for stress-driven sheartransformations. This novel structure representation, when combined withconvolutional neural network (CNN), a powerful deep learning algorithm, leadsto unprecedented accuracy for identifying atoms with high propensity for sheartransformations (i.e., plastic susceptibility), solely from the staticstructure - the spatial atomic positions - in both two- and three-dimensionalmodel glasses. The data-driven models trained on samples at one composition anda given processing history are found transferrable to glass samples withdifferent processing histories or at different compositions in the same alloysystem. Our analysis of the new structure representation also provides valuableinsight into key atomic packing features that influence the local mechanicalresponse and its anisotropy in glasses.

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