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Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Authors :
Lanxin Ma
Kaixiang Hu
Chengchao Wang
Jia-Yue Yang
Linhua Liu
Source :
Nanomaterials, Vol 11, Iss 12, p 3339 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles’ structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.

Details

Language :
English
ISSN :
20794991
Volume :
11
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Nanomaterials
Publication Type :
Academic Journal
Accession number :
edsdoj.86a2e214feb648179d5f6023cc1ca5ff
Document Type :
article
Full Text :
https://doi.org/10.3390/nano11123339