As an illustration of this approach, Kang et al. used a combination of SAXS, UV-vis-NIR spectroscopy, and Brownian dynamics modeling with MPM computations to discover an approximately universal relation between the relative shift of the collective plasmon resonant frequency and the nanocrystal diameter-scaled gap distance (i.e., a plasmon ruler) for linked gels of nanocrystals with different sizes and Sn doping concentrations (Figure ). Insights from this empirical structure-property relationship enabled design and experimental manifes-tation of mixed nanocrystal gels with an identical combination of ITO cores, but showcasing either broadening or narrowing spectral linewidth depending on the ligands used (Figure ). The gel exhibiting linewidth narrowing passed a stringent test for the design strategy since local heterogeneity typically imposes broadening in disordered nanocrystal assemblies. In addition to heuristic designs, the ability to generate high-quality structural and optical property data from experiments and simulations will present new possibilities for designing materials using computational inverse methods and machine learning (Figure ). Dipolebased methods such as MPM can be enhanced to address large-scale disordered structures, including other nanoparticle shapes, by using deep learning-based surrogate models to compute their dressed polarizabilities. Direct and inverse neural network modeling strategies can be employed to navigate the space of nanocrystal dopant concentrations and sizes to learn design rules for spectral control in mixed gels or metasurfaces, while respecting known experimental or synthetic constraints. Specific design targets may aim to maximize near-field enhancements or the density of electromagnetic hot spots that exceed a specified threshold; they may target single-or dual-band collective plasmon resonances at desired frequencies, or broadband absorption over a specified frequency range. Alternatively, such strategies can be integrated with systematic coarse-graining approaches to discover colloidal interactions, dictated by assembly mediators such as molecular depletants or linkers, that reach property design targets through optimal structuring. Lessons learned from these studies will help expand what is known about how disorder can be used to realize materials with desired optical properties, and how measured optical properties, when combined with SAXS measurements, help identify underlying structures and interactions in disordered materials.