  Being able to predict the failure of materials based on structural
information is a fundamental issue with enormous practical and industrial
relevance for the monitoring of devices and components. Thanks to recent
advances in deep learning, accurate failure predictions are becoming possible
even for strongly disordered solids, but the sheer number of parameters used in
the process renders a physical interpretation of the results impossible. Here
we address this issue and use machine learning methods to predict the failure
of simulated two dimensional silica glasses from their initial undeformed
structure. We then exploit Gradient-weighted Class Activation Mapping
(Grad-CAM) to build attention maps associated with the predictions, and we
demonstrate that these maps are amenable to physical interpretation in terms of
topological defects and local potential energies. We show that our predictions
can be transferred to samples with different shape or size than those used in
training, as well as to experimental images. Our strategy illustrates how
artificial neural networks trained with numerical simulation results can
provide interpretable predictions of the behavior of experimentally measured
structures.
