With experimental and descriptor datasets in hand, we applied MLR modeling to develop QSPRs for HEM sensitivity according to the general workflow described in Figure . First, an initial set of MLR models is generated for the sensitivity property of interest using forward stepwise regression. Multiple leading models are considered in terms of the number of parameters in the model, avoidance of cross-terms, and the recorded regression statistics (see SI for details). Notably, consideration of the univariate correlations between descriptors and sensitivity properties was a useful tool for model selection and the construction of additional descriptors to improve model performance. Secondly, univariate correlations can be explored to identify outlier compounds, i.e., compounds where the presence of a neighboring structure (e.g., acyl azides in an azide dataset) alters the properties of an explosophore in a manner that categorically differentiates it from the remainder of the test set. Inclusion of such compounds has the capacity to disproportionately influence the overall model. All suspected model outliers were verified using a one-tailed Grubbs outlier test at the 0.05 significance level. When deemed appropriate, removal of such outliers was conducted and new models were generated to produce improved predictions. In the following sections, we apply this general workflow to generate accurate and interpretable structure-property relationships for nitrogen-rich HEMs.