Mapping microstructure to shock-induced temperature fields using machine learning

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 result from the interaction of a supersonic wave with the materials’ microstructure and are governed by 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 chemistry and microstructure alone. We demonstrate that deep learning techniques can be trained to predict the resulting temperature fields from large-scale molecular dynamics simulations with the initial microstructure as the only input in complex polymer composite systems. The model accuracy is enough for quantitative prediction of the initiation of chemical reactions.