CHINESE JOURNAL OF ENERGETIC MATERIALS
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基于数据驱动的氮杂多环含能化合物的开发研究进展
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作者单位:

1.中国工程物理研究院化工材料研究所;2.西安交通大学化学工程与技术学院

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基金项目:

国家自然科学基金(22375190)


Research Progress of Nitrogen Heteropolyclic Energetic Materials Based on Data-driven
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Affiliation:

1.Institute of Chemical Materials, CAEP;2.School of Chemical Engineering and Tecnology,Xi′an jiaotong University,Xi′an , China

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    摘要:

    含能材料的开发面临诸多挑战,传统“试错法”的研发模式会导致研发周期长,效率低。随着数据科学与人工智能技术的发展,基于数据驱动的研发模式为含能材料的发展开辟了新的路径。多环含能化合物是当前含能材料学科的研究热点,其中氮杂多环骨架由于存在π电子的离域共振和较多的可修饰位点,分子结构的稳定性得到提高,同时能量基团的存在保证了分子的能量水平,使得能量与稳定性之间的固有矛盾得到很好的平衡。研究简要介绍了数据驱动开发新型含能材料的工作流程,概述了数据驱动方法用于氮杂多环含能化合物开发的最新研究进展,最后对数据驱动的方法用于新型含能材料的开发提出展望。未来的发展方向应考虑通过数据增强、治理等手段补充数据量,以提高模型预测的准确性及泛化能力;可通过建立化学反应条件和合成路径筛选的机器学习模型预测分子的可合成性,从而加速新型氮杂多环含能化合物的开发。

    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) technologies, a data-driven research model has emerged as a new path for the development of energetic materials. Polycyclic energetic compounds are currently a hot topic in the field of energetic materials, among which nitrogen-containing polycyclic frameworks, 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 compounds.

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刘友海,黄实,张文全,等. 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,DOI:10.11943/CJEM2024088.

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历史
  • 收稿日期: 2024-03-20
  • 最后修改日期: 2024-05-26
  • 录用日期: 2024-05-20
  • 在线发布日期: 2024-05-22
  • 出版日期: