Issue 37, 2019

Plasmonic nanoparticle simulations and inverse design using machine learning

Abstract

Collective oscillation of quasi-free electrons on the surface of metallic plasmonic nanoparticles (NPs) in the ultraviolet to near-infrared (NIR) region induces a strong electromagnetic enhancement around the NPs, which leads to numerous important applications. These interesting far- and near-field optical characteristics of the plasmonic NPs can be typically obtained from numerical simulations for theoretical guidance of NP design. However, traditional numerical simulations encounter irreconcilable conflicts between the accuracy and speed due to the high demand of computing power. In this work, we utilized the machine learning method, specifically the deep neural network (DNN), to establish mapping between the far-field spectra/near-field distribution and dimensional parameters of three types of plasmonic NPs including nanospheres, nanorods, and dimers. After the training process, both the forward prediction of far-field optical properties and the inverse prediction of on-demand dimensional parameters of NPs can be accomplished accurately and efficiently with the DNN. More importantly, we have achieved for the first time ultrafast and accurate prediction of two-dimensional on-resonance electromagnetic enhancement distributions around NPs by greatly reducing the amount of electromagnetic data via screening and resampling methods. These near-field predictions can be realized typically in less than 10−2 seconds on a laptop, which is 6 orders faster than typical numerical simulations implemented on a server. Therefore, we demonstrate that the DNN is an ultrafast, highly efficient, and computing resource-saving tool to investigate the far- and near-field optical properties of plasmonic NPs, especially for a number of important nano-optical applications such as surface-enhanced Raman spectroscopy, photocatalysis, solar cells, and metamaterials.

Graphical abstract: Plasmonic nanoparticle simulations and inverse design using machine learning

Supplementary files

Article information

Article type
Paper
Submitted
23 апр 2019
Accepted
23 авг 2019
First published
26 авг 2019

Nanoscale, 2019,11, 17444-17459

Plasmonic nanoparticle simulations and inverse design using machine learning

J. He, C. He, C. Zheng, Q. Wang and J. Ye, Nanoscale, 2019, 11, 17444 DOI: 10.1039/C9NR03450A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements