Predictive design of plasmonic color

Predictive design promises direct access to nanomaterials with optimal properties, identified by rigorous mathematical optimization. This strategy thus provides structural blueprints for synthesis and circumvents extensive parameter screening. Here, we establish predictive design of nanoparticle systems using the example of plasmonic coloration. The perceived color of nanoparticle dispersions is influenced by the interplay of the entire visible particle spectrum with the wavelength-dependent sensitivities of the human eye, and not only a function of plasmon resonance positions. We use Mie theory to compute the far-field spectra of spherical gold and silver core-shell nanoparticles and convert them into color perceived by the human eye. Color maps that describe the minimum color difference relative to a predefined target are established as a function of concentration, core diameter and shell thickness, thus providing predictions of the optimal particle structure for a targeted color. We demonstrate the feasibility of this predictive design by successfully targeting multiple colors and discuss limitations and discrepancies between experiment and simulations. Our results showcase that predictive design holds promise for resource-efficient syntheses of functional nanoparticles as the optimal structural blueprints for a desired target property can be accurately predicted in silico.