Self-Learning Perfect Optical Chirality via a Deep Neural Network

被引：79

作者：

Li, Yu ^{[1
,2
]}

Xu, Youjun ^{[3
]}

Jiang, Meiling ^{[1
,2
]}

Li, Bowen ^{[1
,2
]}

Han, Tianyang ^{[1
,2
]}

Chi, Cheng ^{[1
,2
]}

Lin, Feng ^{[1
]}

Shen, Bo ^{[1
,2
]}

Zhu, Xing ^{[1
]}

Lai, Luhua ^{[3
]}

Fang, Zheyu ^{[1
,2
]}

机构：

[1] Peking Univ, State Key Lab Mesoscop Phys, Acad Adv Interdisciplinary Studies, Minist Educ,Sch Phys,Nanooptoelect Frontier Ctr, Beijing 100871, Peoples R China

[2] Collaborat Innovat Ctr Quantum Matter, Beijing 100871, Peoples R China

[3] Peking Univ, BNLMS, State Key Lab Struct Chem Unstable & Stable Speci, Coll Chem & Mol Engn, Beijing 100871, Peoples R China

来源：

PHYSICAL REVIEW LETTERS | 2019年 / 123卷 / 21期

基金：

美国国家科学基金会; 北京市自然科学基金;

关键词：

INVERSE DESIGN;

D O I：

10.1103/PhysRevLett.123.213902

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：6

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