(18j) Inverse Design of Self-Assembled Optical Nanoparticle Metamaterials

Optoelectronic devices harnessing solar energy can be improved by incorporating colloidal soft materials as energy harvesters, waveguides, optical filters, and photo- and thermocatalysts. Understanding, controlling, and designing the light-matter interactions of these components is crucial to improving their performance. In this talk, I’ll discuss how we formulate optical materials design as an inverse problem, where we first specify target optical behavior and then use numerical optimization to autonomously discover self-assembled nanoparticle metamaterials possessing the desired features. Our design framework rapidly iterates through candidate materials self-assembled from tens of thousands of nanoparticles, and specifies the position, size, dielectric properties, and other physical features of each nanoparticle within an optimal metamaterial, subject to arbitrary constraints on the choices of design parameters. Our inverse scheme leverages an in-house electromagnetic simulation method which is (1) rapid enough to handle the large number of simulations needed for iterative optimization and (2) differentiable, so that gradient-based optimization methods are viable. Using our inverse design algorithm, we produce physically realizable metamaterials with unique emergent optical features tunable over an enormous range. Future work will include incorporating our inverse design methods into an AI controller for automated and high-throughput experimental workflows to synthesize optical metamaterials.