Deep-learning framework has significantly impelled the development of modern machine learning technology by continuously pushing the limit of traditional recognition and processing of images, speech, and videos. In the meantime, it starts to penetrate other disciplines, such as biology, genetics, materials science, and physics. Here, we report a deep-learning-based model, comprising two bidirectional neural networks assembled by a partial stacking strategy, to automatically design and optimize three-dimensional chiral metamaterials with strong chiroptical responses at predesignated wavelengths. The model can help to discover the intricate, nonintuitive relationship between a metamaterial structure and its optical responses from a number of training examples, which circumvents the time-consuming, case-by-case numerical simulations in conventional metamaterial designs. This approach not only realizes the forward prediction of optical performance much more accurately and efficiently but also enables one to inversely retrieve designs from given requirements. Our results demonstrate that such a data-driven model can be applied as a very powerful tool in studying complicated light–matter interactions and accelerating the on-demand design of nanophotonic devices, systems, and architectures for real world applications.

中文翻译：

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能够进行深度学习的手性超材料的按需设计

深度学习框架不断推动传统的图像，语音和视频识别和处理的极限，从而极大地推动了现代机器学习技术的发展。同时，它开始渗透到其他学科，例如生物学，遗传学，材料科学和物理学。在这里，我们报告了一个基于深度学习的模型，该模型包括通过部分堆叠策略组装的两个双向神经网络，以自动设计和优化在预定波长下具有强手性响应的三维手性超材料。该模型可以帮助您从许多训练示例中发现超材料结构与其光学响应之间的复杂，非直觉的关系，从而避免了耗时，常规超材料设计中的个案数值模拟。这种方法不仅可以更准确，更有效地实现光学性能的前瞻预测，而且还可以使用户根据给定的要求反向检索设计。我们的结果表明，这种数据驱动的模型可以用作研究复杂的光-物质相互作用和加速纳米光子器件，系统和体系结构在实际应用中的按需设计的非常强大的工具。