燃料化学学报（中英文）2026，Vol.54Issue(1)：148-183,36.DOI:10.3724/2097-213X.2025.JFCT.0030
机器学习助力高效含能材料分子筛选与设计
Machine learning-accelerated efficient screening and design of energetic material molecules
摘要
Abstract
Energetic materials play a crucial role in military and aerospace applications.However,the discovery and synthesis of novel energetic compounds still largely rely on traditional trial-and-error approaches,which severely hinder the development of novel energetic materials.This study focuses on the prediction of a key thermodynamic property of energetic materials-heat of formation(HOF)and proposes a machine learning structure-property relationship model that integrates active learning strategies with SMILES-based molecular feature representation.A dataset containing 1447 gas-phase energetic molecules was constructed based on the high-accuracy G4 quantum chemical method,and 93 effective SMILES descriptors were extracted to establish a preliminary model for gas-phase HOF using linear model.Subsequently,the model was applied on the systematical prediction of 221738 potential energetic molecules retrieved from the PubChem database,enabling the screening of candidates with superior explosive performance.For samples with high prediction errors,an active learning strategy was implemented to iteratively refine the model parameters,significantly improving prediction accuracy.Validation on classical energetic molecules illustrated excellent predictive performance,highlighting the model's strong generalization capability.Finally,20 candidate molecules with a TNT equivalent power index exceeding 2.0 were screened,most of which are new to the existing reservoir of known energetic materials.These results underscore the potential of this proposed approach in accelerating the discovery of high-performance energetic materials and offer a new strategy for the development of next-generation energetic compounds.关键词
含能材料筛选/主动学习/SMILES特征/生成焓/爆炸热Key words
energetic materials screening/active learning/SMILES features/heat of formation/heat of explosion分类
化学化工引用本文复制引用
杨琳,张晓龙,王鹤,周余伟,滕波涛..机器学习助力高效含能材料分子筛选与设计[J].燃料化学学报（中英文）,2026,54(1):148-183,36.基金项目
Supported by National Natural Science Foundation of China(22372185,22372120),Natural Science Foundation of Shanxi Province(202203021221219)and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(2023L164).国家自然科学基金(22372185,22372120),山西省自然科学研究面上项目(202203021221219)和山西省高等学校科技创新项目(2023L164)资助 （22372185,22372120）
