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

By Wei Ma, Feng Cheng, Yongmin Liu. 2018

807 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering, Electronic, Optical and Magnetic Materials

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Citations in the corpus

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, background): “Therefore, some recent works apply deep generative models and neural network inverse modeling [20, 21, 7, 22, 32, 28, 19] to train a more efficient materials sampler, aiming to solve the inverse design problems for metamaterials, i.”
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy (2019, background): “Page 5 of 28 metamaterials [50], chiral metamaterials [51] and nanophotonic particles [52].”
Interpretable inverse design of particle spectral emissivity using machine learning (2020, methodology): “…machine learning has also shown some success in further accelerating the inverse design process by performing the inverse mapping in a single-shot (i.e. without iteratively solving the forward problem) for some optics problems (e.g., concentric spherical shells 12 and periodic metasurfaces 13–16 ).”
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale (2018, background): “plete complex tasks [15]. The associated technique of deep learning is thus considered a promising candidate for the inverse design of new materials [16, 17, 18], drugs [19], and nanophotonic devices [20, 21, 22] (Figure 1). In general, the role of deep learning in nanophotonic design is also to sea”
A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design (2019, neutral): “To achieve this goal, we connected the fully-trained PNN to a meta-filter generator to form a cascaded network and avoid the non-convergence problem resulting from non-unique solutions (21, 22).”