Understanding impact sensitivity of energetic molecules by supervised machine learning

Abstract

Machine learning models have been developed to rationalise correlations between molecular structure and sensitivity to initiation by mechanical impact for a data set of 485 energetic molecules. The models use readily obtainable features derived from SMILES strings to classify structures, first by a binary split to differentiate between primary and secondary energetic material behaviour, and by subsequent boundary divisions to create up to five impact sensitivity classes. The best accuracy score was 0.79, which was obtained for the binary classifier random forest model. Feature importance and SHAP analysis showed that the features most likely to categorise a molecule with a high impact sensitivity were a high oxygen balance and a high molecular flexibility. The outcome of this study gives easily interpretable information on how the structure of a molecule can be tailored to design energetic materials with desired impact sensitivity properties. Included model codes also allow users to predict the sensitivity classes of any additional molecular structures from a SMILES string.