Liu Youhai, Yang Fusheng, Zhang Wenquan, Xia Honglei, Wu Zhen, Zhang Zaoxiao
School of Chemical Engineering and Technology, Xi'an Jiaotong University Xi'an 710049 China
Research Center of Energetic Material Genome Science, Institute of Chemical Materials, China Academy of Engineering Physics (CAEP) Mianyang 621900 P. R. China
RSC Adv. 2024 Jul 29;14(33):23672-23682. doi: 10.1039/d4ra03233k. eCollection 2024 Jul 26.
Finding novel energetic materials with good comprehensive performance has always been challenging because of the low efficiency in conventional trial and error experimental procedure. In this paper, we established a deep learning model with high prediction accuracy using embedded features in Directed Message Passing Neural Networks. The model combined with high-throughput screening was shown to facilitate rapid discovery of fused [5,5] biheterocyclic energetic materials with high energy and excellent thermal stability. Density Functional Theory (DFT) calculations proved that the performances of the targeting molecules are consistent with the predicted results from the deep learning model. Furthermore, 6,7-trinitro-3-pyrrolo[1,2-][1,2,4]triazo-5-amine with both good detonation properties and thermal stability was screened out, whose crystal structure and intermolecular interactions were also analyzed.
由于传统试错实验过程效率低下，寻找具有良好综合性能的新型含能材料一直具有挑战性。在本文中，我们利用定向消息传递神经网络中的嵌入特征建立了一个具有高预测精度的深度学习模型。该模型与高通量筛选相结合，被证明有助于快速发现具有高能量和优异热稳定性的稠合[5,5]双杂环含能材料。密度泛函理论（DFT）计算证明，目标分子的性能与深度学习模型的预测结果一致。此外，筛选出了具有良好爆轰性能和热稳定性的6,7-二硝基-3-吡咯并[1,2-][1,2,4]三唑-5-胺，并对其晶体结构和分子间相互作用进行了分析。
