Microstructure-aware deep learning bridges atomistics to macroscale for shock-to-detonation prediction

Abstract

The shock-to-detonation transition in energetic materials is governed by coupled processes at extreme temperatures, pressures, and strain rates spanning Ångstroms to millimeters and femtoseconds to microseconds. Traditional multiscale models fail due to the lack of equilibrium and scale separation across the phenomena involved. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a convolutional deep neural network. Trained on MD simulations of shock propagation through complex microstructures, MISTnetX captures shock–microstructure interactions, hotspot formation, and the transition to deflagration, supplying critical sub-grid information to FE simulations of mechanics, shocks, thermal transport, and chemistry. Applied to a synthetic but realistic nanostructured plastic-bonded RDX composite, MISTnetX enables parameter-free prediction of the full run-to-detonation transition.