Automated design of nonreciprocal thermal emitters via Bayesian optimization

TL;DR

This paper introduces a Bayesian optimization-based numerical method to design multilayer nonreciprocal thermal emitters, achieving broadband nonreciprocal emission with fewer layers than traditional intuition-based designs.

Contribution

It presents a novel optimization approach for designing multilayer nonreciprocal thermal emitters, outperforming existing designs in efficiency and simplicity.

Findings

Achieves broadband nonreciprocal emission from 5 to 40 micrometers

Uses fewer layers than traditional designs

Outperforms state-of-the-art intuition-based structures

Abstract

Nonreciprocal thermal emitters that break Kirchhoff's law of thermal radiation promise exciting applications for thermal and energy applications. The design of the bandwidth and angular range of the nonreciprocal effect, which directly affects the performance of nonreciprocal emitters, typically relies on physical intuition. In this study, we present a general numerical approach to maximize the nonreciprocal effect. We choose doped magneto-optic materials and magnetic Weyl semimetal materials as model materials and focus on pattern-free multilayer structures. The optimization randomly starts from a less effective structure and incrementally improves the broadband nonreciprocity through the combination of Bayesian optimization and reparameterization. Optimization results show that the proposed approach can discover structures that can achieve broadband nonreciprocal emission at wavelengths from 5 to 40 micrometers using only a fewer layers, significantly outperforming current state-of-the-art designs based on intuition in terms of both performance and simplicity.