Machine Learning Energies of 2 Million Elpasolite (ABC(2)D(6)) Crystals

Elpasolite is the predominant quaternary crystal structure (AlNaK2F6 prototype) reported in the Inorganic Crystal Structure Database. We develop a machine learning model to calculate density functional theory quality formation energies of all similar to 2 x 10(6) pristine ABC(2)D(6) elpasolite crystals that can be made up from main-group elements (up to bismuth). Our model`s accuracy can be improved systematically, reaching a mean absolute error of 0.1 eV/atom for a training set consisting of 10 x 10(3) crystals. Important bonding trends are revealed: fluoride is best suited to fit the coordination of the D site, which lowers the formation energy whereas the opposite is found for carbon. The bonding contribution of the elements A and B is very small on average. Low formation energies result from A and B being late elements from group II, C being a late (group I) element, and D being fluoride. Out of 2 x 10(6) crystals, 90 unique structures are predicted to be on the convex hull-among which is NFAl2Ca6, with a peculiar stoichiometry and a negative atomic oxidation state for Al.