Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA.
Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA.
Angew Chem Int Ed Engl. 2023 Apr 17;62(17):e202218213. doi: 10.1002/anie.202218213. Epub 2023 Mar 17.
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.
富含氮原子的杂环和有机叠氮化物在从药物发现到高能材料的现代化学的许多领域都有广泛的应用。因此，预测和理解它们的能量性质是这些化合物安全有效应用的关键。在这项工作中，我们公开了多元线性回归建模在预测结构多样的四唑和有机叠氮化物的分解温度和冲击感度中的应用。我们报告了一种基于属性预测的数据驱动方法，该方法具有量子力学参数和计算工作流程的集合。本文报道的统计模型具有预测准确性和化学可解释性。通过仅在计算机上生成参数的四唑测试集成功完成了模型验证。对统计模型的机理分析表明，热和冲击引发的分解存在明显不同的发散途径。
