Notably, very low mean absolute errors were achieved for both Tdec and log(IS) (7.3 °C and 0.13, respectively), testifying to the accuracy of this method for predictive modeling of azidecontaining HEMs. Good statistical performance achieved with removal of very few outliers, despite the diversity of the dataset with regards to the presence of various additional explosophores. Furthermore, the statistical models were constructed from descriptors unique to the azide moiety and were largely agnostic of other explosophores contained in the molecule. This corroborates that the azides likely serve as triggers for explosive decomposition, even in the presence of other explosophores.