含能材料2024，Vol.32Issue(6)：660-671,12.DOI:10.11943/CJEM2024088
基于数据驱动的氮杂多环含能化合物的开发研究进展
Research Progress of Nitrogen Heteropolyclic Energetic Materials Based on Data-driven
摘要
Abstract
The development of energetic materials faces many challenges,and the traditional trial-and-error research model often results in long development cycles and low efficiency.With the advancement of data science and artificial intelligence(AI)tech-nologies,a data-driven research model has emerged as a new path for the development of energetic materials.Polycyclic ener-getic compounds are currently a hot topic in the field of energetic materials,among which nitrogen-containing polycyclic frame-works,due to the presence of π electrons for delocalized resonance and multiple modifiable sites,exhibit enhanced molecular structural stability.At the same time,the presence of energy groups ensures the energy level of the molecules,achieving a good balance between energy and stability,overcoming the inherent contradiction between them.This study briefly introduces the workflow of data-driven development of novel energetic materials,outlines the latest research progress of data-driven methods for the development of nitrogen-containing polycyclic energetic compounds,and finally proposes prospects for the application of data-driven methods in the development of novel energetic materials.Future directions should consider supplementing data volume through means such as data augmentation and governance to improve the accuracy and generalization ability of model predictions.Machine learning models can be used to predict the molecular synthetic feasibility by establishing chemical reaction conditions and synthetic pathways,thereby accelerating the development of novel nitrogen-containing polycyclic energetic com-pounds.关键词
含能材料/数据驱动/氮杂多环含能化合物/机器学习Key words
energetic materials/data-driven,nitrogen heteropolyclic energetic compounds/machine learning分类
军事科技引用本文复制引用
刘友海,黄实,张文全,杨福胜..基于数据驱动的氮杂多环含能化合物的开发研究进展[J].含能材料,2024,32(6):660-671,12.基金项目
国家自然科学基金(22375190) National Natural Science Foundation of China(No.22375190) （22375190）
