Representative works of this approach include correlating impact sensitivities with molecular properties such as bond strengths and molecular electrostatic potentials, whereas others employed Mulliken charge values of nitro groups and examined binding forces using the Wiberg bonding index. Our own quantum chemical work on the molecular origins of the impact sensitivity has been based on the decomposition of a quantum chemical molecular charge (electronic) density (for instance, computed using density functional theory, DFT or an ab initio method) into atomcentered electric multipoles determined by the distributed multipole analysis (DMA) method. This approach provides a detailed and accurate picture of the molecular charge density, which we have been used to investigate the impact sensitivity of different families of explosive molecules and different phenomena related to catalysis. We recently reviewed this work on the prediction of impact sensitivities and others based on different theoretical descriptions of molecular charge distributions. The variety and diverse nature of the properties related to sensitivity indicate that those relationships cannot all represent the fundamental causes of sensitivity because "many of them can be symptomatic". Therefore, those properties correspond to general trends, not correlations, providing valuable estimates of the magnitude of the sensitivities. In this work, we search for these predictive trends by identifying and quantifying molecular properties that affect the sensitivity to impact. The application of artificial intelligence (AI) and machine learning (ML) techniques to chemistry and materials science, including the prediction of properties of energetic materials, is growing tremendously. In quantum chemistry applications, a given training data ML is now a "game changer" for inferring complicated and unknown nonlinear dependencies of molecular properties from fitting functions and making accurate predictions; In particular, ML can be used to learn features (properties) of materials properties. Concerning the application of ML techniques to sensitivity, and in particular, to impact sensitivity, there have been sparse works in the last two decades. Most of them used a large number of molecular structure descriptors to fit experimental ℎ .# values employing a classical QSPR analysis. Most of these investigations employed QSPR packages to generate and select sometimes over 1000 molecular descriptors that are afterward reduced by statistical techniques. The pioneering work of Nefati et al. employed neural networks to predict ℎ .# of 204 molecules of different families selected from originally 39 descriptors (27 purely topological and 12 from semiempirical electronic structure calculations) by a cross-validated (leave one out) standard error criterion, producing much better results compared to those from a linear regression. Choa and co-workers optimized neural networks based on this work and employed over 200 molecules of the same dataset, finding that compositional and topological descriptors gave better results compared to those composed of electronic descriptors, which may be due to their use of the not very accurate semiempirical AM1 method. Keshavarz and Jaafari also employed neural networks, with 275 molecules comprising the training set and 14 explosive molecules the test set. The descriptors were primarily structural, namely, the number of N -NO ' bonds, the number of 𝛼 -hydrogens, aromatic and heteroaromatic characters, and the number of each type of atom divided by the molecular weight. The predicted ℎ .# values were superior to the five quantum chemical models of Rice and Hare based on generalized interaction property functions (GIPFs) computed from molecular electrostatic potentials. Wang et al. employed neural networks and atom-and group-type electrotopological-state indices (ETSI), which combine the electronic and topological properties of a molecule and take the binding environment into account, to predict impact sensitivities of 156 compounds (training set with 127 molecules and test set with 29) of different chemical families (49 nitroaromatic, 55 nitramine, 40 nitroaliphatic compounds containing other functional groups, 7 nitrate esters, and 5 nitroaliphatics). Their results with neural networks had good agreement with experimental ℎ .# values and were superior compared to multi-linear regression and partial least squares modeling.