受限于试错法较低的研发效率，寻找具有特定性能的含能材料始终是一个极具挑战性的工作。本文展示了基于领域知识、机器学习算法和实验验证的含能材料研发新模式。设计了一个集成分子生成和机器学习模型的高通量虚拟筛选（HTVS）系统，该系统可预测分子性能，并对晶体堆积模式进行评估。在该系统指导下，快速生成了25 112 个分子，并从中确认了具有理想性能和晶体堆积模式的候选分子。对目标分子进行实验合成，后续的晶体结构和性质研究表明，目标分子良好的综合性能与预测结果一致；验证了本文中研发模式的有效性。本研究展示了一种用于发现新型含能材料的新的研究范式，并且可以无障碍地
将其用于其他有机功能材料的探索。

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error. Herein, a methodology combining domain knowledge, a machine learning algorithm, and experiments is presented for accelerating the discovery of novel energetic materials. A high-throughput virtual screening (HTVS) system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established. With the proposed HTVS system, candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25 112 molecules. Furthermore, a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results, thus verifying the effectiveness of the proposed methodology. This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.