A Physical Organic Approach towards Statistical Modeling of Tetrazole and Azide Decomposition**
Abstract
Nitrogen 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 17, 2023
- Source ID
- 10.1002/ange.202218213
Entities
People
- Jonas Rein
- Jonathan M. Meinhardt
- Julie L Hofstra
- Matthew Sigman
- Song Lin
Organizations
- Cornell University
- German National Academic Foundation
- National Institute of General Medical Sciences
- National Science Foundation Directorate for Mathematical & Physical Sciences
- Office of Naval Research
- University of Utah