Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods

A large database is desired for machine learning (ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure. When a large database is not available, the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database. In this work, we show that two new featurization methods, volume occupation spatial matrix and heat contribution spatial matrix, can improve the accuracy in predicting energetic materials’ crystal density (ρ_crystal) and solid phase enthalpy of formation (H_f,solid) using a database containing 451 energetic molecules. Their mean absolute errors are reduced from 0.048 g/cm³ and 24.67 kcal/mol to 0.035 g/cm³ and 9.66 kcal/mol, respectively. By leave-one-out-cross-validation, the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes. Our ML models are applied to predict ρ_crystal and H_f,solid of CHON-based molecules of the 150 million sized PubChem database, and screened out 56 candidates with competitive detonation performance and reasonable chemical structures. With further improvement in future, spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.