2024/09/13 by Bach Do, Sina Jafari Ghalekohneh, Do, Bach +7 · 3 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Applied Physics (physics.app-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Heat Transfer and Optimization #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2409.09192
openalex publication_date 2024/09/13 · openalex created_date 2024/10/23 · openalex updated_date 2026/07/28
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.