高等学校化学学报2026，Vol.47Issue(6)：115-124,10.DOI:10.7503/cjcu20250297
基于机器学习方法的含能材料分解温度预测
Prediction of Decomposition Temperature of Energetic Materials Based on Machine Learning Methods
摘要
Abstract
In this study,a novel composite descriptor system was suggested,and a high-performance prediction model for the decomposition temperature of energetic materials was constructed.First,molecular structure descriptors based on group contribution were proposed.Then,bond dissociation energy(BDE)was introduced as a key supplementary parameter to quantify the effect of bond strength on decomposition temperature.Finally,RDKit descriptors were generated using the RDKit software,ultimately integrating them into a multidimensional feature set.This feature set was submitted to random forest(RF),support vector machine(SVM),and partial least squares(PLS)individually to construct multiple prediction models and conduct a systematic comparison.Among them,the best results were obtained using the model built by RF.Its prediction performance was superior to the results reported in the literature,indicating that the proposed composite descriptors can effectively capture the key factors affecting the decomposition temperature.To further interpret the model and identify critical influencing factors,Shapley additive explanations(SHAP)visualization technology was employed to analyze the optimal model,thereby providing valuable data-driven insights into the thermal stability mechanisms of energetic materials.关键词
分解温度/分子基团描述符/定量构效关系/随机森林Key words
Decomposition temperature/Molecular group descriptor/Quantitative structure-property relationship(QSPR)/Random forest(RF)分类
化学化工引用本文复制引用
郭莉莉,户梦倩,黄孟梅,卢艳华,李伟,张庆友,郭翔..基于机器学习方法的含能材料分解温度预测[J].高等学校化学学报,2026,47(6):115-124,10.基金项目
中国航天化学动力实验室开放研究基金(批准号:120201B01)资助.Supported by the Open Research Fund of the Science and Technology on Aerospace Chemical Power Laboratory,China(No.120201B01). （批准号:120201B01）
