From colloidal gel networks to compositionally mixed metasurfaces and multilayers, tunable disorder is increasingly recognized as a design element for preparing materials with new and desirable combinations of properties. Understanding the optical response of disordered plasmonic nanomaterials demands strategies to address the structure-dependent coupling intrin-sic to such systems across a hierarchy of length scales. In this paper, we demonstrated how large-scale Brownian dynamics simulations, together with the mutual polarization method (MPM), can be used to systematically characterize the structural and optical properties of A remaining open question is how to predict a particle's resonant frequency from structural motifs alone, without having to perform full optical simulations. From the collective resonance structures of Figures and, we have clues indicating which local morphologies may be important predictors of ω res,i , but a quantitative, predictive relationship has not been established. For example, though Figure shows that red particles form field-aligned chains, it is unclear how one might locate these particular chains a priori from structural information alone. Following recent work on modeling monolayer superlattices with realistic structural disorder and aperiodic metasurfaces, there may be an opportunity to apply tools from machine learning to extract which specific aspects of a particle's local environment are most predictive of the particle's resonant frequency and surface hotspot intensity.