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Machine learning-accelerated efficient screening and design of energetic material molecules
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Lin YANG1, Xiaolong ZHANG2, He WANG3, Yuwei ZHOU3, 4, *, Botao TENG2, *
Journal of Fuel Chemistry and Technology | 2026, 54(1) : 20250281-135 - 20250281-145
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Journal of Fuel Chemistry and Technology | 2026, 54(1): 20250281-135-20250281-145
Machine learning-accelerated efficient screening and design of energetic material molecules
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Lin YANG1, Xiaolong ZHANG2, He WANG3, Yuwei ZHOU3, 4, *, Botao TENG2, *
Affiliations
  • 1School of Statistics, Shanxi University of Finance and Economics, Taiyuan 030006, China
  • 2College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China
  • 3State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
  • 4Synfuels China Technology Co., Ltd., Beijing 101407, China
Published: 2026-01-01 doi: 10.3724/2097-213X.2025.JFCT.0030
Outline
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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.

energetic materials screening  /  active learning  /  SMILES features  /  heat of formation  /  heat of explosion
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. 燃料化学学报(中英文), 2026 , 54 (1) : 20250281-135 -20250281-145 . DOI: 10.3724/2097-213X.2025.JFCT.0030
Lin YANG, Xiaolong ZHANG, He WANG, Yuwei ZHOU, Botao TENG. Machine learning-accelerated efficient screening and design of energetic material molecules[J]. Journal of Fuel Chemistry and Technology, 2026 , 54 (1) : 20250281-135 -20250281-145 . DOI: 10.3724/2097-213X.2025.JFCT.0030
Year 2026 volume 54 Issue 1
151
64
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Article Info
doi: 10.3724/2097-213X.2025.JFCT.0030
  • Receive Date:2025-10-13
  • Online Date:2026-09-23
  • Published:2026-01-01
Article Data
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History
  • Received:2025-10-13
  • Revised:2025-11-10
  • Accepted:2025-11-20
Affiliations
    1School of Statistics, Shanxi University of Finance and Economics, Taiyuan 030006, China
    2College of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin 300457, China
    3State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
    4Synfuels China Technology Co., Ltd., Beijing 101407, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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