High throughput search for plasmonic semiconductors using DFT databases

The field of plasmonics aims to manipulate light via choice of materials and nanoscale structuring. Finding materials which exhibit low-loss responses to applied optical fields while remaining feasible for widespread use is an outstanding challenge. Online databases have compiled structural and electronic data for of tens of thousands of materials, but lack the expensive optical response calculations needed for selection of plasmonic materials. Understanding the optical response of semiconductors within DFT is complicated due to the DFT bandgap error and the influence of carrier doping density. We describe, validate, and demonstrate an approach which rapidly screens existing online databases to identify high quality factor plasmonic semiconductors. Using DFT bandstructures, we tabulate material bandgaps, optical transition energies, and effective masses. From correlations between bandstructure features and quality factor computed for a restricted set of semiconductors we predict CaMg₂N₂ , InGaO₃, and LiInO₂ as candidate high quality factor plasmonic semiconductors. Quality factors are verified with DFT to describe optical absorption and the Drude model for intraband transitions.