基于贝叶斯优化的非互易热辐射体自动化设计
Automated design of nonreciprocal thermal emitters via Bayesian optimization
AI总结：
针对非互易热辐射体设计依赖物理直觉的问题，提出结合贝叶斯优化与重参数化的通用数值方法，仅用较少层数即可实现5-40微米宽带非互易发射，性能与简洁性均超现有最先进设计。
摘要：
打破热辐射基尔霍夫定律的非互易热辐射体在热与能源领域具备极具前景的应用价值。非互易效应的带宽与角度范围直接影响非互易辐射体的性能，其设计通常依赖物理直觉。本研究提出一种通用数值方法，用于最大化非互易效应。我们选取掺杂磁光材料与磁性外尔半金属材料作为模型材料，聚焦无图案多层结构。优化过程从性能较差的结构随机起始，通过贝叶斯优化与重参数化相结合的方式，逐步提升宽带非互易性。优化结果表明，所提方法仅需较少层数，就能发现可在5至40微米波长范围内实现宽带非互易发射的结构，在性能与简洁性上均显著优于当前基于直觉的最先进设计。
英文摘要：
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.
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