Establishing the structure-property-performance linkage of pressed energetic materials using physics-aware recurrent convolutional neural networks (PARC)

Establishing the structure-property-performance (SPP) linkages is a vital task for the design of energetic materials (EM). However, the current approaches for establishing SPP linkages are limited by time-consuming and costly physical and numerical experiments, rendering practical challenges in exploring the vast design space of EM microstructures. In this work, we propose a novel deep learning method called physics-aware recurrent convolutional neural network (PARC), which can assimilate the thermo-mechanics of hotspot ignition and growth in shocked heterogeneous EM microstructures. PARC is designed to predict the time evolution of temperature and pressure fields by modeling and solving the governing differential equations using convolutional neural networks (CNN). In contrast to other machine learning approaches, the unique recurrent convolutional architecture modeling the governing differential equations makes PARC highly interpretable and “physics-aware.” The validation results show that PARC can predict the thermomechanical behavior of shock-induced EM microstructures with high accuracy (within 5% error) compared to direct numerical simulation (DNS) results, despite a dramatic reduction of computation time (up to 3000 times). Furthermore, we also show that the interpretable architecture of PARC provides additional lenses for the study of SPP linkages by shedding light on identifying the morphological characteristic of microstructures that lead to energy localization and initiation. The impact of the current work is a novel capacity to estimate SPP linkages in a significantly quicker turnaround, enabling the design of EM microstructures with engineered properties.