Self-Learning Perfect Optical Chirality via a Deep Neural Network

Optical chirality occurs when materials interact differently with light in a specific circular polarization state. Chiroptical phenomena inspire wide interdisciplinary investigations, which require advanced designs to reach strong chirality for practical applications. The development of artificial i...

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Bibliographic Details
Published in:Physical review letters 2019-11, Vol.123 (21), p.213902-213902, Article 213902
Main Authors: Li, Yu, Xu, Youjun, Jiang, Meiling, Li, Bowen, Han, Tianyang, Chi, Cheng, Lin, Feng, Shen, Bo, Zhu, Xing, Lai, Luhua, Fang, Zheyu
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
Chirality
Circular polarization
Electric fields
Mathematical analysis
Nanostructure
Neural networks
Optical properties
Optimization
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Summary:Optical chirality occurs when materials interact differently with light in a specific circular polarization state. Chiroptical phenomena inspire wide interdisciplinary investigations, which require advanced designs to reach strong chirality for practical applications. The development of artificial intelligence provides a new vision for the manipulation of light-matter interaction beyond the theoretical interpretation. Here, we report a self-consistent framework named the Bayesian optimization and convolutional neural network that combines Bayesian optimization and deep convolutional neural network algorithms to calculate and optimize optical properties of metallic nanostructures. Both electric-field distributions at the near field and reflection spectra at the far field are calculated and self-learned to suggest better structure designs and provide possible explanations for the origin of the optimized properties, which enables wide applications for future nanostructure analysis and design.
ISSN:0031-9007
1079-7114
DOI:10.1103/physrevlett.123.213902