Machine learning enabled rational design for dynamic thermal emitters with phase change materials

Summary

Dynamic thermal emitters have attracted considerable attention due to their potential in widespread applications such as radiative cooling, thermal switching, and adaptive camouflage. However, the state-of-art performances of dynamic emitters are still far below expectations. Here, customized to the special and stringent requirement of dynamic emitters, a neural network model is developed to effectively bridge the structural and spectral spaces and further realizes the inverse design with coupling to genetic algorithms, which considers the broadband spectral responses in different phase-states and utilizes comprehensive measures to ensure the modeling accuracy and computational speed. Besides achieving an outstanding emittance tunability of 0.8, the physics and empirical rules have also been mined qualitatively through decision trees and gradient analyses. The study demonstrates the feasibility of using machine learning to obtain the near-perfect performance of dynamic emitters, as well as guiding the design of other thermal and photonic nanostructures with multifunctions.

Graphical abstract

Highlights

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  Accurate neural network (NN) models have been trained for dynamic thermal emitters

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  Superior emittance tunability has been achieved with simple configurations

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  NN-aided genetic algorithm makes the inverse design with extreme performance feasible

Abstract

Physics; Phase Transitions; Materials science; Thermal property