Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik
Abstract
, spin-splitting energy) of over 187k TMCs. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of computational lead compounds with literature-mined, experimental compounds over the typically employed single-DFA approach.
Topics & Concepts
Path (computing)Computer scienceArtificial intelligenceMachine learningProgramming languageMachine Learning in Materials ScienceX-ray Diffraction in CrystallographyCatalysis and Oxidation Reactions