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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.09192 |
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| _version_ | 1866913500126773248 |
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| author | Do, Bach Ghalekohneh, Sina Jafari Adebiyi, Taiwo Zhao, Bo Zhang, Ruda |
| author_facet | Do, Bach Ghalekohneh, Sina Jafari Adebiyi, Taiwo Zhao, Bo Zhang, Ruda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated design of nonreciprocal thermal emitters via Bayesian optimization Do, Bach Ghalekohneh, Sina Jafari Adebiyi, Taiwo Zhao, Bo Zhang, Ruda Materials Science Machine Learning Applied Physics 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. |
| title | Automated design of nonreciprocal thermal emitters via Bayesian optimization |
| topic | Materials Science Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2409.09192 |