摘要
含能材料在军事和航天等领域应用广泛,但其发现与合成主要依赖“试错法”,严重制约了新型含能材料的研发与突破。本研究选取含能材料关键热力学性质——生成焓作为预测目标,提出一种融合主动学习策略与SMILES分子特征表示的机器学习构效关系模型。基于G4高精度量子化学方法构建了包含1447种气相含能分子的数据集,提取了93个SMILES有效描述符,建立了基于线性模型的气相生成焓初步预测模型。对从PubChem数据库中获取的221738种潜在含能分子进行了系统预测。进一步引入主动学习策略,对高误差样本迭代优化,得到了泛化能力更好的预测模型,该模型在典型含能分子上得到了验证。最终筛选出20个威力指数超过2.0倍TNT的候选分子,其中,大多数未见于现有已知含能材料库,显示出本研究在高性能含能材料发现方面的潜力,为新型含能材料的开发提供新策略与新途径。
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.
作者
杨琳
张晓龙
王鹤
周余伟
滕波涛
YANG Lin;ZHANG Xiaolong;WANG He;ZHOU Yuwei;TENG Botao(School of Statistics,Shanxi University of Finance and Economics,Taiyuan 030006,China;College of Chemical Engineering and Materials Science,Tianjin University of Science and Technology,Tianjin 300457,China;State Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China;Synfuels China Technology Co.,Ltd.,Beijing 101407,China)
出处
《燃料化学学报(中英文)》
北大核心
2026年第1期135-145,共11页
Journal of Fuel Chemistry and Technology
基金
国家自然科学基金(22372185,22372120)
山西省自然科学研究面上项目(202203021221219)
山西省高等学校科技创新项目(2023L164)资助。
关键词
含能材料筛选
主动学习
SMILES特征
生成焓
爆炸热
energetic materials screening
active learning
SMILES features
heat of formation
heat of explosion
