In another work, Wang and coworkers employed a genetic algorithm for selecting descriptors for the QSPR analysis of 186 explosive molecules using neural networks. Nine topological and quantum descriptors were selected from an initial pool of over 500. Prana et al. investigated the impact sensitivity of 50 nitroaliphatic compounds using about 400 descriptors including quantum chemical ones based on DFT calculations. Linear and multi-linear regressions including internal and external validations provided accurate relationships with experimental ℎ .# values. One interesting mechanistic finding is that several descriptors, especially the quantum mechanical ones, were related to the explosophore (functional group that makes a compound explosive) NO2 group involved in the decomposition of nitro compounds. Out of the four models, the most accurate also involved quantum chemical descriptors. Xu et al. applied ten three-dimensional (3D) descriptors that gather different information from the spatial structure of the molecule and neural networks on 156 molecules, obtaining better results compared to linear regression approaches. Deng and collaborators used sure independence screening and sparsifying operator (SISSO) methods to investigate the correlation between impact sensitivity and bulk modulus 𝐵 (a mechanical property that quantifies resistance of a material to compression) of 240 nitroaromatics, using 14 molecular structure descriptors and 7 structural parameters (including oxygen balance, crystal density and hydrophilicity), with results of similar accuracy when compared to neural networks. They found that the oxygen-containing group, hydrophilicity, and some atomic properties are the main contributions to impact sensitivity. A particularly interesting result is that the impact sensitivity sharply decreases with increasing 𝐵 when 𝐵 is small, while the opposite trend occurs for materials with relatively large bulk modulus 𝐵.