Plasmonics seeks to manipulate light at the nanoscale. Precise control over plasmon response enables many applications including: sub-wavelength waveguides, nanoantennas, superlenses, subwavelength imaging, nanocircuitry, and biosensors. Finding new metals and doped semiconductors which may act as viable choices for plasmonic applications remains an outstanding problem. This work uses high throughput density functional theory to calculate quality factors for 970 metals randomly chosen from the Materials Project database. These quality factors are used to train machine learning models which enable rapid estimation of quality factors without requiring DFT calculations. For materials predicted to have high quality factors, crystal, electronic, and optical properties will be computed with DFT. This work will use the application of plasmonics to motivate the study of physical effects which influence optical absorption of crystals, including bandstructure, optical transition matrix elements, spin-orbit coupling, exciton formation, and surface geometry.
