Mapping microstructure to shock-induced temperature fields using deep learning

TL;DR

This paper introduces MISTnet, a deep learning model that predicts shock-induced temperature fields in complex materials from microstructure data, outperforming existing methods in accuracy and efficiency.

Contribution

The paper presents a novel deep learning approach, MISTnet, that accurately predicts shock-induced temperature fields using only microstructure information, reducing computational costs.

Findings

MISTnet outperforms current state-of-the-art models in accuracy.

The model significantly reduces computational time.

It effectively captures complex microstructure-temperature relationships.

Abstract

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.