The field of chiral plasmonics has registered considerable progress with machine-learning (ML)-mediated metamaterial prototyping, drawing from the success of ML frameworks in other applications such as pattern and image recognition. Here, we present an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. Our model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Finally, we explore the behavior of a sample ML-optimized structure in a practical application, assisting the sensing of biomolecular enantiomers. Other potential applications of our metastructure include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy, with our ML framework being applicable to a wider range of physical problems.

中文翻译：

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基于多任务深度学习的手性等离子体超材料设计

手性等离子体领域在机器学习 (ML) 介导的超材料原型制作方面取得了相当大的进展，这得益于 ML 框架在其他应用（如模式和图像识别）中的成功。在这里，我们提出了一种用于三维手性超材料设计和优化的端到端功能双向深度学习 (DL) 模型。该 ML 模型利用多任务联合学习功能来识别、概括和详细探索超材料几何形状与其手性光学响应之间的重要关系，无需辅助网络或等效方法来稳定物理相关输出。我们的模型以高精度有效地实现了正向和反向检索任务，为复杂物理系统中的迭代计算设计任务提供了一种很有前途的工具。最后，我们探索了样品 ML 优化结构在实际应用中的行为，帮助检测生物分子对映异构体。我们的元结构的其他潜在应用包括光电探测器、偏振分辨成像和圆二色性 (CD) 光谱，我们的 ML 框架适用于更广泛的物理问题。