General deep learning framework for emissivity engineering  

作　　者：Shilv Yu Peng Zhou Wang Xi Zihe Chen Yuheng Deng Xiaobing Luo Wangnan Li Junichiro Shiomi Run Hu 

机构地区：[1]School of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan 430074,China [2]Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices,Hubei University of Arts and Science,Xiangyang 441053 Hubei,China [3]Hubei Longzhong Laboratory,Wuhan University of Technology(Xiangyang Demonstration Zone),Xiangyang 441000 Hubei,China [4]Department of Mechanical Engineering,The University of Tokyo,7-3-1 Hongo,Bunkyo-ku,Tokyo 113-8654,Japan

出　　处：《Light(Science & Applications)》2023年第12期2755-2767,共13页光（科学与应用)（英文版）

基　　金：support by National Natural Science Foundation of China(52211540005,52076087,52161160332);Natural Science Foundation of Hubei Province(2023AFA072);the Open Project Program of Wuhan National Laboratory for Optoelectronics(2021WNLOKF004);Wuhan City Science and Technology Program(2020010601012197);Knowledge Innovation Shuguang Program.W.L.acknowledges the financial support from Key Research and Development plan of Hubei Province(2021BGE037);J.S.acknowledges the financial support from JSPS Bilateral Joint Research Projects(120227404).

摘　　要：Wavelength-selective thermal emitters(WS-TEs)have been frequently designed to achieve desired target emissivity spectra,as a typical emissivity engineering,for broad applications such as thermal camouflage,radiative cooling,and gas sensing,etc.However,previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures,thus lacking of a general design framework for emissivity engineering across different applications.Moreover,previous designs fail to tackle the simultaneous design of both materials and structures,as they either fix materials to design structures or fix structures to select suitable materials.Herein,we employ the deep Q-learning network algorithm,a reinforcement learning method based on deep learning framework,to design multilayer WS-TEs.To demonstrate the general validity,three WS-TEs are designed for various applications,including thermal camouflage,radiative cooling and gas sensing,which are then fabricated and measured.The merits of the deep Q-learning algorithm include that it can(1)offer a general design framework for WS-TEs beyond one-dimensional multilayer structures;(2)autonomously select suitable materials from a self-built material library and(3)autonomously optimize structural parameters for the target emissivity spectra.The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications,and the design parameters are highly scalable in materials,structures,dimensions,and the target functions,offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

关 键 词：FRAMEWORK MULTILAYER AUTONOMOUS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]