  It has been a long-standing materials science challenge to establish
structure-property relations in amorphous solids. Here we introduce a
rotation-variant local structure representation that enables different
predictions for different loading orientations, which is found essential for
high-fidelity prediction of the propensity for stress-driven shear
transformations. This novel structure representation, when combined with
convolutional neural network (CNN), a powerful deep learning algorithm, leads
to unprecedented accuracy for identifying atoms with high propensity for shear
transformations (i.e., plastic susceptibility), solely from the static
structure - the spatial atomic positions - in both two- and three-dimensional
model glasses. The data-driven models trained on samples at one composition and
a given processing history are found transferrable to glass samples with
different processing histories or at different compositions in the same alloy
system. Our analysis of the new structure representation also provides valuable
insight into key atomic packing features that influence the local mechanical
response and its anisotropy in glasses.
