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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We focused on the influence of particle size, volume fraction, and layer thickness on the structural colors of gold nanoparticle systems
We combined the Monte Carlo, Mie scattering simulations, and bidirectional neural network model to successfully improve the design of structural colors in gold nanoparticle systems
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Machine learning methods based on artificial neural networks have emerged as a powerful tool in solving complex computation and inverse design problems. Machine learning techniques have been utilized to inversely design the structure and material to achieve the desired optical and color properties [28,29,30,31,32,33]. Baxter et al [28] proposed a deep learning method to predict the colors of silver surfaces doped with silver nanoparticles and to inversely design the geometric parameters for the desired colors. A multilayered BNN model was built to accurately predict the color generation of gold nanoparticle systems and to inversely design the geometric parameters for the desired colors. Our approach captures the complex relationships between the geometric parameters and structural colors with high reliability and accuracy, and can be used for the analysis and optimization design of structural color in nanoparticle systems
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