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

15 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups), ∑▒〖Q_1 (NO_2)〗 (the total dipole, i.e., polarization, of the nitro groups), ∑▒〖Q_2 (C) 〗 (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. 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. The predicted h_50 values of molecules having very different sensitivities for the four algorithms are in the range 19% - 28% compared to experimental data. 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 its 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 ∑▒〖Q_1 (NO_2)〗 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.


Impact sensitivities of explosives
Machine Learning
Molecular charge (electronic) densities
Drop weight test
Distributed Multipole Analysis (DMA)
Extra Trees
Random Forests
Gradient Boosting

Supplementary materials

Supplementary Materials
Molecular structures, acronyms, input file, machine learning algorithms


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