Machine Learning Assisted High-throughput Design of［5，6］Fused Ring Energetic Compounds

Compared with the research and development model guided by experience and calculations， machine learning-assisted high-throughput virtual screening technology for energetic molecules has shown obvious advantages in terms of molecular design efficiency and quantitative analysis of structure-activity relationships. In view of the fact that nitrogen-rich fused ring energetic compounds usually show better energy-stable balance properties，this study uses machine learning-assisted high-throughput virtual technology to conduct chemical space exploration of［5，6］nitrogen-rich fused ring energetic molecules. Based on the［5，6］all-carbon skeleton，this study obtained 142，689［5，6］fused ring compounds through combined enumeration and aromatic screening. At the same time，a machine learning algorithm was used to establish and optimize an energetic molecular property prediction model（including density，decomposition temperature，detonation velocity，detonation pressure，impact sensitivity and enthalpy of formation）. The effects of nitrogen and oxygen atoms on the fused ring and functional groups on the molecule on the performance of energetic compounds were analyzed. The research results show that the structure-activity relationship of the generated fused ring compounds is consistent with the general correlation between energy and stability of energetic compounds，verifying the rationality of the prediction model. Taking detonation velocity and decomposition temperature as the criteria for energy and thermal stability，five molecules with outstanding comprehensive properties were screened，and the quantum chemical calculation results were in good agreement with the machine learning prediction results，which further verified the accuracy of the prediction model. © 2024 Institute of Chemical Materials, China Academy of Engineering Physics. All rights reserved.