General deep learning framework for emissivity engineering

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

doi:10.1038/s41377-023-01341-w

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General deep learning framework for emissivity engineering

Original Articles

- General deep learning framework for emissivity engineering
- Light: Science & Applications Vol. 12, Issue 12, Pages: 2755-2767(2023)
- Affiliations：
  
  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
- Author bio：
  
  Wangnan Li (liwangnan@hbuas.edu.cn)
  Junichiro Shiomi (shiomi@photon.t.u-tokyo.ac.jp)
  Run Hu (hurun@hust.edu.cn)
- Funds：
- DOI：10.1038/s41377-023-01341-w
  CLC：
- Published：31 December 2023，
  
  Published Online：05 December 2023，
  
  Received：04 July 2023，
  
  Revised：17 November 2023，
  
  Accepted：18 November 2023
- Accepted：
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Yu, S. L. et al. General deep learning framework for emissivity engineering. Light: Science & Applications, 12, 2755-2767 (2023).
DOI：

Yu, S. L. et al. General deep learning framework for emissivity engineering. Light: Science & Applications, 12, 2755-2767 (2023). DOI： 10.1038/s41377-023-01341-w.

摘要

Abstract

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

Keywords

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