Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Abstract

Machine learning atomistic potentials (MLPs) trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost.