Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

TLDR

Deep‑learning has advanced machine‑learning capabilities across image, speech, and video domains and is now extending into biology, genetics, materials science, and physics. The study presents a deep‑learning model that automatically designs and optimizes 3D chiral metamaterials with strong chiroptical responses at specified wavelengths. The model uses two bidirectional neural networks combined via a partial stacking strategy. The model uncovers complex structure–optical response relationships, enabling accurate forward predictions and inverse design, thereby accelerating on‑demand creation of nanophotonic devices.

Abstract

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.