After this initial exploration, we systematically developed models for Tdec for the remaining 26 azide HEMs in our study. We first generated an initial training model using the 19 HEMs containing only a single azide explosophore (Figure , Step 1) see SI for model equation and statistics). We then applied an iterative workflow to expand the scope of our prediction to the remaining 7 HEMs with multiple azide explosophores per molecule. This iterative process is described as follows and is demonstrated in detail in the SI. First, the training model for HEMs with one azide explosophore was used to generate Tdec predictions for each azide explosophore in every HEM in the dataset (Figure , Step 2). For HEMs with multiple azide explosophores, the azide group predicted to be the most sensitive (i.e., lowest predicted Tdec) is added to the training dataset for a subsequent generation of modeling (Figure Step 3). At this point, all HEMs were now included in this updated training set. A new generation of models was produced from this training set, and the azide explosophore in each molecule with the lowest predicted Tdec was again compiled into a new dataset. This process was repeated until dataset convergence was reached, i.e., once explosophore selection for every HEM in the dataset was the same as in the previous iteration of modeling (Figure , Step 4). In our experience, convergence was achieved within 2-4 iterations of this workflow. After three iterations of modeling, our workflow had converged on a final training set and a corresponding predictive model for azide decomposition temperature (Figure ). ). Notably, acyl, iminyl, and peroxo azides were successfully predicted in the final model.