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Machine learning for spectral precision:a new horizon in radiative cooling material design

机器学习驱动光谱精准调控:辐射冷却材料设计的新范式
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摘要 Radiative cooling has emerged as a sustainable strategy for passive thermal management,offering a promising route to reduce global energy demand and mitigate the effects of climate change[1-3].By leveraging the atmospheric window in the mid-infrared region(8-13μm),thermal radiation can be emitted directly into outer space,enabling cooling without the need for power input[4].This approach,grounded in fundamental thermodynamic principles,has attracted significant attention in various fields,including materials science,photonics,architecture,and atmospheric physics[5].
作者 Xinpeng Hu Mingxiang Liu Xuemei Fu Guangming Tao Xiang Lu Jinping Qu 胡新鹏;刘铭祥;付雪梅;陶光明;卢翔;瞿金平
出处 《Science Bulletin》 2025年第24期4122-4124,共3页 科学通报(英文版)
基金 supported by the National Natural Science Foundation of China(T2425018,U2441275,and 62175082) the Interdisciplinary Research Program of HUST(2023JCYJ039) the Innovation Fund of Wuhan National Laboratory for Optoelectronics the support from the New Cornerstone Science Foundation through the XPLORER PRIZE。

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