Du, S;
You, J;
Zhang, J;
Tao, Z;
Hao, H;
Tang, Y;
Zheng, X;
(2020)
Expedited circular dichroism prediction and engineering in two-dimensional diffractive chiral metamaterials leveraging a powerful model-agnostic data enhancement algorithm.
Nanophotonics
10.1515/nanoph-2020-0570.
(In press).
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[21928614 - Nanophotonics] Expedited circular dichroism prediction and engineering in two-dimensional diffractive chiral metamaterials leveraging a powerful model-agnostic data enhancement algorithm.pdf - Published Version Download (6MB) | Preview |
Abstract
A model-agnostic data enhancement (MADE) algorithm is proposed to comprehensively investigate the circular dichroism (CD) properties in the higher-order diffracted patterns of two-dimensional (2D) chiral metamaterials possessing different parameters. A remarkable feature of MADE algorithm is that it leverages substantially less data from a target problem and some training data from another already solved topic to generate a domain adaptation dataset, which is then used for model training at no expense of abundant computational resources. Specifically, nine differently shaped 2D chiral metamaterials with different unit period and one special sample containing multiple chiral parameters are both studied utilizing the MADE algorithm where three machine learning models (i.e, artificial neural network, random forest regression, support vector regression) are applied. The conventional rigorous coupled wave analysis approach is adopted to capture CD responses of these metamaterials and then assist the training of MADE, while the additional training data are obtained from our previous work. Significant evaluations regarding optical chirality in 2D metamaterials possessing various shape, unit period, width, bridge length, and separation length are performed in a fast, accurate, and data-friendly manner. The MADE framework introduced in this work is extremely important for the large-scale, efficient design of 2D diffractive metamaterials and more advanced photonic devices.
| Type: | Article |
|---|---|
| Title: | Expedited circular dichroism prediction and engineering in two-dimensional diffractive chiral metamaterials leveraging a powerful model-agnostic data enhancement algorithm |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1515/nanoph-2020-0570 |
| Publisher version: | http://dx.doi.org/10.1515/nanoph-2020-0570 |
| Language: | English |
| Additional information: | © 2020 Shiyin Du et al., published by De Gruyter, Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 International License. BY 4.0 |
| Keywords: | diffractive chiral metamaterials; machine learning methods; model-agnostic data enhancement algorithm; optical chirality; polarization-controllable devices. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > Centre for Languages and Intl Educatn |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10118285 |
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