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材料导报  2025, Vol. 39 Issue (15): 24080168-9    https://doi.org/10.11896/cldb.24080168
  无机非金属及其复合材料 |
基于机器学习的光谱选择性红外辐射材料设计研究进展
葛宇飞, 刘东青*, 程海峰, 桂博恒, 王新飞, 贾岩
国防科技大学空天科学学院,新型陶瓷纤维及其复合材料重点实验室,长沙 410073
Research Progress in Machine Learning-based Design of Spectrally Selective Infrared Radiating Materials
GE Yufei, LIU Dongqing*, CHENG Haifeng, GUI Boheng, WANG Xinfei, JIA Yan
Science and Technology on Advanced Ceramic Fibers and Composites Laboratory, College of Aerospace and Engineering, National University of Defense Techno-logy, Changsha 410073, China
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摘要 随着热光伏、辐射制冷、气体检测、红外隐身等领域的快速发展,对光谱选择性红外辐射材料的需求日益迫切。单种材料通常难以实现对红外光谱的精细选择性调控,需要结合多种材料进行结构优化设计,以满足不同应用场景对光谱选择性红外辐射材料的需求。但光谱选择性红外辐射材料设计参数空间大,传统的“试错型”设计难以实现全局优化和处理多目标约束问题,且耗时较长。随着人工智能和大数据技术的发展,通过构建机器学习模型,对材料成分、排布方式、膜层厚度等参数进行并行设计,能够实现光谱选择性红外辐射材料设计的快速全局优化。本文旨在梳理机器学习应用于光谱选择性红外辐射多层膜和超表面设计的最新研究进展,分析机器学习辅助光谱选择性红外辐射材料设计的优势与问题,并指出未来发展趋势。
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葛宇飞
刘东青
程海峰
桂博恒
王新飞
贾岩
关键词:  机器学习  光谱选择性  红外辐射  多层膜  超表面  发射率    
Abstract: The rapid development of thermophotovoltaics, radiative cooling, gas sensing and infrared stealth has proposed significant demands on spectrally selective infrared radiating materials. It is usually difficult for a single kind of material to realize complicated spectral selectivity. Consequently, selecting various materials and designing the structure are necessary to meet the requirements of different application scenarios. Howe-ver, the design parameter space of spectrally selective infrared radiating materials is large, and the conventional "trial-and-error" design is difficult to realize global optimization and deal with the multi-objective constraint problems. Due to the development of artificial intelligence and big data technology, rapid global optimization of spectrally selective infrared radiation material can be achieved by constructing a machine learning model for the design of parameters such as material composition, arrangement mode and film layer thickness. This paper mainly outlines the latest research progress of machine learning-based design of multilayer film and metasurface, analyzes the advantages and problems of machine learning-assisted design of spectrally selective infrared radiation materials, and puts forward the future development trend.
Key words:  machine learning    spectrally selective    infrared radiation    multilayer film    metasurface    emissivity
出版日期:  2025-08-10      发布日期:  2025-08-13
ZTFLH:  TB34  
基金资助: 国家自然科学基金(52073303)
通讯作者:  刘东青,博士,国防科技大学空天科学学院副教授、博士研究生导师,主要从事红外辐射调控材料与器件研究。liudongqing07@nudt.edu.cn   
作者简介:  葛宇飞,国防科技大学空天科学学院硕士研究生,在刘东青副教授的指导下进行研究。目前主要研究领域为红外辐射调控材料。
引用本文:    
葛宇飞, 刘东青, 程海峰, 桂博恒, 王新飞, 贾岩. 基于机器学习的光谱选择性红外辐射材料设计研究进展[J]. 材料导报, 2025, 39(15): 24080168-9.
GE Yufei, LIU Dongqing, CHENG Haifeng, GUI Boheng, WANG Xinfei, JIA Yan. Research Progress in Machine Learning-based Design of Spectrally Selective Infrared Radiating Materials. Materials Reports, 2025, 39(15): 24080168-9.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.24080168  或          https://www.mater-rep.com/CN/Y2025/V39/I15/24080168
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