Screening of Material Defects using Universal Machine-Learning Interatomic Potentials

Abstract

Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated by the invent of universal machine-learning interatomic potentials that offer first principles accuracy at orders of magnitude lower computational cost. Their application to the screening of defects with desired properties or to finding new stable compounds with high density of defects, however, has not been explored. Here, it is shown that the universal machine-learning interatomic potentials have reached sufficient accuracy to enable large-scale screening of defective materials. Vacancy calculations are carried out for 86,259 materials in the Materials Project database and the formation energies analyzed in terms of oxidation numbers. The application of these models is further demonstrated for finding new materials at or below the convex hull of known materials and for simulated etching of low-dimensional materials.