摘要
Green hydrogen production is crucial for a sustainable future,but current catalysts for the oxygen evolution reaction(OER)suffer from slow kinetics,despite many efforts to produce optimal designs,particularly through the calculation of descriptors for activity.In this study,we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides,and adsorption energies of OER intermediates,which includes compositions up to quaternary and facets up to(555).We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting,and develop a machine learning model that accurately predicts these properties directly from the local chemical environment.We leverage these per-site properties to identify promising perovskites with high theoretical OER activity.The identified design principles and promising materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes such as photosystem II.
基金
supported by the Advanced Research Projects Agency-Energy(ARPA-E),US Department of Energy under award number DE-AR0001220.
