Machine learning-assisted search for high performance plasmonic metals

Plasmonics aims to manipulate light through choice of materials and nanoscale structure. Finding materials which exhibit low-loss responses to applied optical fields while remaining feasible for widespread use is an outstanding challenge. Online databases compiled computational data for numerous properties of tens of thousands of materials. Due to the large number of materials and high computational cost, it is not viable to compute optical properties for all materials from first principles. Geometry-dependent plasmonic quality factors for a training set of ~1,000 materials are computed using density functional theory and the Drude model. We train then apply random-forest regressors to rapidly screen Materials Project to identify potential new plasmonic metals. Descriptors are limited to symmetry and quantities obtained using the chemical formula and the Mendeleev database. The machine learning models filter through 7,445 metals in Materials Project. We iteratively improve the model with active learning by adding DFT results for predicted high quality factor metals into the training set. From this we predict Cu₃Au, MgAg₃, and CaN₂ as candidates and verify their quality factors with DFT.