ArticleProceedings of the National Academy of Sciences of the United States of America2025
Designing strongly coupled polaritonic structures via statistical machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Strong coupling photonics, in which coupling strengths significantly exceed decay factors, holds great promise for applications in energy conversion and information processing. However, the effective and scalable design of strongly coupled polaritonic structures requires precise determination of the transition boundaries between strong and weak coupling regions. Traditional trial-and-error methods and classical machine learning (ML) algorithms struggle to achieve this due to the inherent difficulty in measuring decay factors during coupling. To address these challenges, we propose a hybrid ML framework that integrates physics-informed modeling with uncertainty quantification. This approach enables accurate determination of strong-weak coupling transition boundaries-even with inaccessible decay factors. By leveraging this capability, our method facilitates efficient and large-scale design of strongly coupled polaritonic structures with sparse data, achieving a computational speedup of ~10
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