Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research

Atomistics-consistent continuum models and pore-collapse-generated hotspot temperatures in energetic crystals I: beta-1,3,5,7-tetranitro-1,3,5,7-tetrazocane (beta-HMX)

Hotspot formation due to pore collapse is a key mechanism for initiating detonation of shocked energetic materials. Energy localization at and around the pore collapse site leads to high-temperature hotspots, initiating chemical reactions. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters, and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for beta-HMX are examined in the context of nanoscale shock-induced pore collapse, treating predictions from molecular dynamics (MD) simulations as ground truth. Using MD-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson-Cook (M-JC) strength model form that captures aspects of shear strain and strain-rate dependency not represented by the standard JC form, but which are shown to be critical for accurately describing the nanoscale physics of shock-induced localization in HMX. The study culminates in a fully MD-determined strength model for beta-HMX that produces continuum pore-collapse results well aligned in all aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks