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

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 constan...

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Autores principales: Ma, Lanxin, Hu, Kaixiang, Wang, Chengchao, Yang, Jia-Yue, Liu, Linhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703294/
https://www.ncbi.nlm.nih.gov/pubmed/34947688
http://dx.doi.org/10.3390/nano11123339
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author Ma, Lanxin
Hu, Kaixiang
Wang, Chengchao
Yang, Jia-Yue
Liu, Linhua
author_facet Ma, Lanxin
Hu, Kaixiang
Wang, Chengchao
Yang, Jia-Yue
Liu, Linhua
author_sort Ma, Lanxin
collection PubMed
description 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.
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spelling pubmed-87032942021-12-25 Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network Ma, Lanxin Hu, Kaixiang Wang, Chengchao Yang, Jia-Yue Liu, Linhua Nanomaterials (Basel) Article 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. MDPI 2021-12-08 /pmc/articles/PMC8703294/ /pubmed/34947688 http://dx.doi.org/10.3390/nano11123339 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Lanxin
Hu, Kaixiang
Wang, Chengchao
Yang, Jia-Yue
Liu, Linhua
Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title_full Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title_fullStr Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title_full_unstemmed Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title_short Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network
title_sort prediction and inverse design of structural colors of nanoparticle systems via deep neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703294/
https://www.ncbi.nlm.nih.gov/pubmed/34947688
http://dx.doi.org/10.3390/nano11123339
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