The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
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.
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Cited by
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Qian, C., Zheng, B., Shen, Y., Jing, L., Li, E., Shen, L., & Chen, H. (2020) · Nature Photonics · doi:10.1038/s41566-020-0604-2 · Cited by 585
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Deep learning enabled inverse design in nanophotonics
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Deep learning for accelerated all-dielectric metasurface design
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Metadata
- Authors
- Ma, W., Cheng, F., & Liu, Y.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-06-01
- Volume
- 12
- Issue
- 6
- Pages
- 6326-6334
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2806536390
- Cited by
- 925
- References
- 43
- FWCI
- 26.14
- Type
- journal article
- Language
- en
- Open access
- closed
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