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Applications and Prospects of AI-assisted Design of Energetic Molecules

1.Institute of Chemical Materials, CAEP, Mianyang 621999, China;2.School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China

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    The explore of energetic molecules faces multiple challenges, and the traditional design method are inefficient. The emergence of computer-aided molecular design has changed the research and development model. This review provides an overview of the development of energetic molecular design and introduces the current research status of computer-aided energetic molecular design. By summarizing the latest advancements in Artificial Intelligence (AI) technology across various design aspects, including performance prediction, molecular generation, retrosynthetic reaction prediction, and reaction condition prediction, we discussed the existing gap between the current approaches in energetic molecular design and other materials design methods. By thinking about the causes of the gap, we present an outlook on the future developmental directions of AI-assisted energetic molecular design. Research indicates that AI has already been applied in property prediction and molecular generation of energetic molecular design, but requires further exploration in retrosynthetic reaction prediction, and reaction conditions prediction. AI-assisted design of energetic molecules holds broad promising application prospects. Data enhancement, transfer learning and high-throughput computing are expected to solve the problem of weak data of energetic molecules. Enhancing AI-assisted prediction of synthesis routes and reaction conditions for energetic molecules shows promise for achieving the automatic molecular design via whole process of “design→evaluation→preparation→verification”. AI-assisted energetic molecular design provides new possibilities for improving the level of energetic molecular design and helps to improve the efficiency of energetic molecule research and development.

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LIU Rui, LIU Jian, TANG Yue-chuan, et al. Applications and Prospects of AI-assisted Design of Energetic Molecules[J]. Chinese Journal of Energetic Materials,2024,32(4):408-421.

  • Received:October 29,2023
  • Revised:January 09,2024
  • Adopted:January 05,2024
  • Online: January 08,2024
  • Published: April 25,2024