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    Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
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    • Wei Ma
      Wei Ma
      Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, United States
      More by Wei Ma
      Orcidhttp://orcid.org/0000-0002-5665-7840
    • Feng Cheng
      Feng Cheng
      Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, United States
      More by Feng Cheng
    • Yongmin Liu*
      Yongmin Liu
      Department of Mechanical and Industrial Engineering  and  Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, United States
      *E-mail: [email protected]
      More by Yongmin Liu
      Orcidhttp://orcid.org/0000-0003-1084-6651
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    ACS Nano

    Cite this: ACS Nano 2018, 12, 6, 6326–6334
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    https://doi.org/10.1021/acsnano.8b03569
    Published June 1, 2018
    Copyright © 2018 American Chemical Society
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    Abstract

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    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.

    Copyright © 2018 American Chemical Society

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.8b03569.

    • Details about the structure and training of the deep-learning model; modeling of strong chiral metamaterial with slight breaking in symmetry (PDF)

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    Cite this: ACS Nano 2018, 12, 6, 6326–6334
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