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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.
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This content will become publicly available on January 1, 2026
Applying statistical modeling strategies to sparse datasets in synthetic chemistry
The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry. Statistical modeling hinges on the data (what is being modeled), descriptors (how data are represented), and algorithms (how data are modeled). Herein, we focus on how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, and turnover number) and data structures (e.g., binned, heavily skewed, and distributed) influence the choice of algorithm used for constructing predictive and chemically insightful statistical models.
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- Award ID(s):
- 2202693
- PAR ID:
- 10570114
- Publisher / Repository:
- American Association for the Advancement of Science
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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