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Design of nanoscale structures with desired optical properties is a key task for 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 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 local responses based on geometries for fixed compositions and surface chemical states. 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 yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures.

Predictability of Localized Plasmonic Responses in Nanoparticle Assemblies