Affiliation:
1. Department of Chemistry and Chemical Biology Cornell University Ithaca NY 14853 USA
2. Department of Chemistry University of Utah Salt Lake City UT 84112 USA
Abstract
AbstractNitrogen atom‐rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data‐driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact‐initiated decomposition.
Funder
Office of Naval Research
Studienstiftung des Deutschen Volkes
National Institute of General Medical Sciences
Directorate for Mathematical and Physical Sciences
Subject
General Chemistry,Catalysis
Cited by
2 articles.
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