  We propose a machine learning approach to address a key challenge in
materials science: predicting how fractures propagate in brittle materials
under stress, and how these materials ultimately fail. Our methods use deep
learning and train on simulation data from high-fidelity models, emulating the
results of these models while avoiding the overwhelming computational demands
associated with running a statistically significant sample of simulations. We
employ a graph convolutional network that recognizes features of the fracturing
material and a recurrent neural network that models the evolution of these
features, along with a novel form of data augmentation that compensates for the
modest size of our training data. We simultaneously generate predictions for
qualitatively distinct material properties. Results on fracture damage and
length are within 3% of their simulated values, and results on time to material
failure, which is notoriously difficult to predict even with high-fidelity
models, are within approximately 15% of simulated values. Once trained, our
neural networks generate predictions within seconds, rather than the hours
needed to run a single simulation.
