Numerous experimental and computational studies have provided valuable insight into the decomposition of HEMs. However, the narrow scope of such studies often results in limited applicability for the streamlined screening of large numbers of HEM candidates. Mechanistic studies of HEM decomposition are usually performed on datasets consisting of a single molecule or a narrow set of related molecules. Conclusions drawn from such studies can lead to poor generalizability when applied to structurally dissimilar compounds. Furthermore, the decomposition of energetic materials is often governed by multiple mechanisms with comparable activation energies, hindering rational design of HEMs based on a single mechanistic proposal. Additionally, theoretical mechanistic studies involving excited states and transition state calculations remain a computationally expensive exercise, limiting their applicability for virtual screening of large numbers of structurally diverse compounds. Analogous challenges of limited generalizability and ballooning resource utilization are also encountered in other areas of chemistry such as asymmetric catalysis, organic methodology development, and battery research. These constraints are increasingly addressed by the implementation of data-driven approaches which aim to construct quantitative structure-property relationships (QSPRs) to aid prediction and guide rational design. In this vein, multivariate linear regression (MLR) statistical modeling is an attractive approach for developing QSPRs due to its inherent interpretability when used in conjunction with physically relevant molecular descriptors. We sought to utilize an MLR-based approach to construct sensitivity property models which would reveal key mechanistic features in HEM decomposition for energetic material families at the forefront of current research.