Accurate prediction of the sensitivity properties of high-energy materials (HEMs) and the study of their decomposition mechanisms are two major focuses within energetics research. Due to the hazards associated with the synthesis and handling of energetic materials, predictive models for HEM sensitivity are of great importance in enabling the safe and efficient development of future HEMs. Traditional predictive modeling of HEM decomposition via machine learning algorithms generally displays limited interpretability, while mechanistic studies of HEMs typically focus on small subsets of structurally analogous compounds lacking generalizability. This study aims to bridge the gap between predictive modeling and computational mechanistic analysis of HEMs, with the goal of providing chemically interpretable models for HEM sensitivity property prediction. Herein, we disclose the use of multivariate linear regression (MLR) modeling for the prediction of the decomposition temperature and impact sensitivity of HEMs. We report an explosophore-based approach to sensitivity property prediction featuring an ensemble of quantum mechanical parameters and computational workflows that enable rapid parameterization and modeling of energetic functional groups. We then employ these methods to accurately predict sensitivity properties of nitrogen-rich tetrazole and azide HEMs. These statistical MLR models are readily interpreted based on the principles of physical organic chemistry, producing structure-property relationships to guide the rational design of new HEMs. Furthermore, we extend our explosophore-based approach to predict the sensitivity properties of HEMs containing multiple, non-equivalent energetic functional groups through the identification of molecular triggers for the bulk decomposition of HEMs. Finally, we showcase the viability of our methods towards ab initio virtual screening of HEMs through predictive modeling of external test sets of tetrazole HEMs using structures and parameters generated exclusively in silico.
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