Predictability of localized plasmonic responses in nanoparticle assemblies

TL;DR

This paper introduces neural network models that predict localized plasmonic responses from nanoparticle geometries, enabling efficient design of nanostructures with desired optical properties.

Contribution

The study develops encoder-decoder neural networks to establish a bidirectional relationship between nanoparticle geometries and their plasmonic spectra, facilitating predictive design.

Findings

High-accuracy predictions of plasmonic spectra from geometries.

Insights into the generative mechanisms of plasmonic interactions.

Pathway for stochastic design of nanoplasmonic structures.

Abstract

Design of nanoscale structures with desired nanophotonic properties are key tasks for nanooptics and nanophotonics. Here, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the correlative relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of the local responses based on geometries for fixed compositions and chemical states of the surface. The analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that can yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures.