  The response of materials to dynamical, or shock, loading is important to
planetary science, aerospace engineering, and energetic materials.
Thermal-activated processes, including chemical reactions and phase
transitions, are significantly accelerated by the localization of the energy
deposited into hotspots. These results from the interaction of a supersonic
wave with the materials microstructure and are governed by complex, coupled
processes, including the collapse of porosity, interfacial friction, and
localized plastic deformation. These mechanisms are not fully understood and
today we lack predictive models to, for example, predict the shock to
detonation transition from chemistry and microstructure alone. We demonstrate
that deep learning techniques can be used to predict the resulting
shock-induced temperature fields in complex composite materials obtained from
large-scale molecular dynamics simulations with the initial microstructure as
the only input. The accuracy of the Microstructure-Informed Shock-induced
Temperature net (MISTnet) model is higher than the current state of the art at
a fraction of the computation cost.
