Machine-learning designed smart coating: temperature-dependent self-adaptation between a solar absorber and a radiative cooler

TL;DR

This paper presents a machine-learning optimized multilayered metamaterial that dynamically switches between solar absorption and radiative cooling modes based on temperature, advancing sustainable energy technologies.

Contribution

It introduces a novel self-adaptive metamaterial design utilizing phase change materials and machine learning for optimized temperature-dependent switching.

Findings

Successful design of a temperature-responsive metamaterial

Potential for significant carbon emission reduction

Advancement in machine-learning-based metamaterial development

Abstract

We designed a multilayered self-adaptive absorber/emitter metamaterial, which can smartly switch between a solar absorber and a radiative cooler based on temperature change. The switching capability is facilitated by the phase change material and the structure is optimized by machine learning. Our design not only advances the machine-learning-based development of metamaterials but also has the potential to significantly reduce carbon emissions and contribute to the goal of achieving carbon neutrality.