Interpretable-machine-learning-guided discovery of dominant intrinsic factors of sensitivity of high explosives

Abstract

Determination of sensitivity of high explosives (HEs) to different external loadings in a safe and rapid way is of great concern throughout the entire lifetime of a high explosive. However, sensitivity related research is still challenging due to the complexity of numerous physical–chemical processes that coexist under a transient external loading. Laser-induced plasma spectroscopy (LIPS) has emerged as a promising tool for detecting and characterizing explosives. Herein, we present an interpretable-machine-learning analytical approach for probing the potential intrinsic factors of sensitivity at the atomic and molecular levels using LIPS spectra and custom descriptors. The intrinsic natures of sensitivity are revealed as an attempt, which promotes a tight correlation between measured sensitivity and molecular structure, atomic proportions and electron transfer through statistical methods. Several factors, such as multi-core oil–water partition coefficient (Mlog P), oxygen balance (OB), minimum value of partial charge (MinPC), aromatic index (AI), and emission intensity of CN radicals and C2 dimers, are found to be intimately related to sensitivity. By considering these factors, we can easily differentiate between sensitive and insensitive explosives. These findings highlight the significance of combining observed LIPS spectra and calculated atomic and molecular descriptors with machine learning to enhance the future analytical workflow for studying the sensitivity of explosives.