General Deep Learning Framework for Emissivity Engineering

Abstract

Abstract Wavelength-selective thermal emitters have been frequently adopted as a typical platform for emissivity engineering to achieve desired target emissivity spectra for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous design methods fail to tackle the simultaneous design of both materials and structures, either fixing materials to design structures or fixing structures to select proper materials, hindering the establishment of a general design framework for emissivity engineering applicable across different applications. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer wavelength-selective thermal emitters for a diverse range of applications, including thermal camouflage, radiative cooling and gas sensing. With magnetron sputtering, these emitters are fabricated and measured, validating the desired emissivity spectra with the designed ones. The main merits of the deep Q-learning algorithm include that it can 1) autonomously select suitable materials from a self-built material library and 2) autonomously optimize structures, thus realizing simultaneous optimization of materials and structures for various emissivity engineering applications. The present method is demonstrated to be feasible and efficient in designing multilayer wavelength-selective thermal emitters, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems across various physical fields.