Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders

TL;DR

This paper introduces a dual variational autoencoder approach to rapidly and accurately predict the geometry of plasmonic nanoparticle assemblies from hyperspectral images, enabling automated structure-property analysis.

Contribution

The novel dual-VAE method links nanoparticle geometry and spectral responses, improving prediction accuracy and automation in nanoscale imaging beyond diffraction limits.

Findings

Achieved high fidelity predictions for monomers (0.96), dimers (0.86), and trimers (0.58).

Established a universal structure-property relationship using shared encoding.

Demonstrated applicability to broader materials science imaging problems.

Abstract

We demonstrate the application of machine learning for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. We show that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that we can use hyperspectral darkfield microscopy to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.