Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives.

We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, (the charge of the nitro groups), (the total dipole, i.e. , polarization, of the nitro groups), (the total electron delocalization of the C ring atoms), and the number of explosophore groups (#NO 2 ) were selected as features for a comprehensive machine learning (ML) investigation. The target property was the impact sensitivity h 50 (cm) values quantified by drop-weight measurements, with a large h 50 ( e.g. , 150 cm) indicating that an explosive is insensitive and vice versa . After a preliminary screening of 42 ML algorithms, four were selected based on the lowest root mean square errors: Extra Trees, Random Forests, Gradient Boosting, and AdaBoost. Compared to experimental data, the predicted h 50 values of molecules having very different sensitivities for the four algorithms have differences in the range 19-28%. The most important properties for predicting h 50 are the electron delocalization in the ring atoms and the polarization of the nitro groups with averaged weights of 39% and 35%, followed by the charge (16%) and number (10%) of nitro groups. A significant result is how the contribution of these properties to h 50 depends on their actual sensitivities: for the most sensitive explosives ( h 50 up to ∼50 cm), the four properties contribute to reducing h 50 , and for intermediate ones (∼50 cm ≲ h 50 ≲ 100 cm) #NO 2 and contribute to increasing it and the other two properties to reducing it. For highly insensitive explosives ( h 50 ≳ 200 cm), all four properties essentially contribute to increasing it. These results furnish a consistent molecular basis of the sensitivities of known explosives that also can be used for developing safer new ones.