基于机器学习的光谱选择性红外辐射材料设计研究进展

doi:10.11896/cldb.24080168

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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

Published: 10 August 2025 Online: 2025-08-13

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TB34

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	Articles by authors
	GE Yufei
	LIU Dongqing
	CHENG Haifeng
	GUI Boheng
	WANG Xinfei
	JIA Yan

Cite this article:

GE Yufei,LIU Dongqing,CHENG Haifeng, et al. Research Progress in Machine Learning-based Design of Spectrally Selective Infrared Radiating Materials[J]. Materials Reports, 2025, 39(15): 24080168-9.

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https://www.mater-rep.com/EN/10.11896/cldb.24080168 OR https://www.mater-rep.com/EN/Y2025/V39/I15/24080168

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