Machine Learning of Energetic Material Properties and Performance

We present advances in accurate, rapid prediction of detonation pressure, detonation velocity, heat of formation, density, and melting point of energetic molecules. Molecules evaluated are CHNO-containing organic molecules drawn from public datasets and known explosives. These models may be integrated into a larger effort for high-throughput virtual screening or rapid pre-screening of molecules before any hazardous synthesis is attempted. Our research evaluates a message-passing neural network (MPNN) model with representation learned from 2D structure trained on a large body of data generated by physics-driven (quantum mechanically derived) models, and also a thermodynamic fingerprint representation used to train a gradient-boosted decision tree method on a smaller body of experimental data. The utility of each representation and statistical model is discussed. The Python workflow for each analysis is discussed. This data-driven approach is shown to provide advances in speed and accuracy for energetic material property prediction. A brief introduction to energetic materials and detonation physics is provided for non-experts.