In this work, we combine high-level quantum chemical data -in our case, DMA site electric multipoles that accurately describe the DFT molecular charge density of molecules, with ML algorithms to identify molecular trends affecting the impact sensitivity of explosives. We investigate the broadly used class of nitroaromatics explosives that usually combine low sensitivity and high performance, hence, for this reason, are the most used type in warlike devices. The nitroaromatic molecules include nitrobenzenes, nibrobenzyls, nitroanilines, and nitrophenols, among other types.