Xiao Chengyu, Liu Mengqi, Yao Kan, Zhang Yifan, Zhang Mengqi, Yan Max, Sun Ya, Liu Xianghui, Cui Xuanyu, Fan Tongxiang, Zhao Changying, Hua Wansu, Ying Yinqiao, Zheng Yuebing, Zhang Di, Qiu Cheng-Wei, Zhou Han
State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China.
Future Materials Innovation Center, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, P. R. China.
Nature. 2025 Jul;643(8070):80-88. doi: 10.1038/s41586-025-09102-y. Epub 2025 Jul 2.
Thermal nanophotonics enables fundamental breakthroughs across technological applications from energy technology to information processing. From thermal emitters to thermophotovoltaics and thermal camouflage, precise spectral engineering has been bottlenecked by trial-and-error approaches. Concurrently, machine learning has demonstrated its powerful capabilities in the design of nanophotonic and meta-materials. However, it remains a considerable challenge to develop a general design methodology for tailoring high-performance nanophotonic emitters with ultrabroadband control and precise band selectivity, as they are constrained by predefined geometries and materials, local optimization traps and traditional algorithms. Here we propose an unconventional machine learning-based paradigm that can design a multitude of ultrabroadband and band-selective thermal meta-emitters by realizing multiparameter optimization with sparse data that encompasses three-dimensional structural complexity and material diversity. Our framework enables dual design capabilities: (1) it automates the inverse design of a vast number of possible metastructure and material combinations for spectral tailoring; (2) it has an unprecedented ability to design various three-dimensional meta-emitters by applying a three-plane modelling method that transcends the limitations of traditional, flat, two-dimensional structures. We present seven proof-of-concept meta-emitters that exhibit superior optical and radiative cooling performance surpassing current state-of-the-art designs. We provide a generalizable framework for fabricating three-dimensional nanophotonic materials, which facilitates global optimization through expanded geometric freedom and dimensionality and a comprehensive materials database.
热纳米光子学在从能源技术到信息处理的各种技术应用中实现了根本性突破。从热发射体到热光伏和热伪装，精确的光谱工程一直受到反复试验方法的制约。与此同时，机器学习在纳米光子学和超材料设计中展现出了强大的能力。然而，开发一种通用的设计方法来定制具有超宽带控制和精确带选择性的高性能纳米光子发射体仍然是一个巨大的挑战，因为它们受到预定义的几何形状和材料、局部优化陷阱以及传统算法的限制。在此，我们提出一种基于机器学习的非常规范式，该范式可以通过利用包含三维结构复杂性和材料多样性的稀疏数据实现多参数优化，从而设计出多种超宽带和带选择性的热超发射体。我们的框架具备双重设计能力：（1）它能自动进行大量可能的亚结构和材料组合的逆向设计，以实现光谱定制；（2）它具有前所未有的能力，通过应用超越传统二维平面结构限制的三平面建模方法来设计各种三维超发射体。我们展示了七个概念验证超发射体，它们展现出超越当前最先进设计的卓越光学和辐射冷却性能。我们提供了一个用于制造三维纳米光子材料的通用框架，该框架通过扩大几何自由度和维度以及全面的材料数据库来促进全局优化。
