Crossing Species: meta-learning the shock response of CHNO energetic materials in a predictive framework

Predicting the shock response of CHNO energetic materials is critical for their safe and effective use in applications, propellants, pyrotechnics, and explosives. However, accurately and consistently predicting the shock response of CHNO is challenging since the group include a variety of materials species, each behaves differently under the application of shock. In this study, we developed a physics-informed – "meta-learning" - (PIML) method which can learn the generalized shock response knowledge across different CHNO species and quickly adapt to make prediction across different species with minimal amount of training data. We, present a chemical decomposition model for energetic materials, which aims to understand and predict the thermal decomposition behavior of these materials that utilizes a combination of thermodynamic and kinetic data to predict the decomposition pathways and rates of these materials. Importantly, the kinetic data from this experiment can be interpreted as sensitivity of those materials, which allowed us to develop kinetic equation for a single step from a multistep. The kinetic parameters are obtained from solving a system of coupled differential equations that describes the time-dependent evolution of the concentrations of the species involved in the decomposition process. Later on, reactive void collapse experiments were conducted on HMX, RDX, TATB, PETN, and TNT for different pressures and diameters of void. Consequently, we used PIML to quickly adapt the void collapse knowledge from the HMX dataset to other species. Out validation with RDX showed that PIML can predict the shock response of EM well, demonstrating agreement between results from numerical and machine learning prediction, despite of being trained with a small dataset. The validated meta-learning model is then used to provide valuable insight into the underlying mechanisms of sensitivity prediction of energetic materials and could be useful for the development of new and improved materials