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 ...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.展开更多
Single-atom catalysts(SACs)with multiple active sites exhibit high activity for a wide range of sluggish reactions,but identifying optimal multimetallic SAC is challenging due to the vast design space.Here,we present ...Single-atom catalysts(SACs)with multiple active sites exhibit high activity for a wide range of sluggish reactions,but identifying optimal multimetallic SAC is challenging due to the vast design space.Here,we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network(GNN)to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions(ORR/OER).Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active ones.The GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and selectivity.The computational predictions of promising Co-Fe,Co-Co,and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the literature.This approach can be extended to a broader class of multi-element high entropic materials systems.展开更多
基金supported by the Advanced Research Projects Agency-Energy(ARPA-E),US Department of Energy under award number DE-AR0001220.
文摘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H.C.,J.K.K.and B.H.acknowledge National Research Foundation of Korea(2022M3H4A1A04096482 and RS-2023-00229679)funded by the Ministry of Science and ICT.H.C.also acknowledges“Program for Fostering Innovative Global Leaders”of the Korea Institute for Advancement of Technology(KIAT)with financial support by the Ministry of Trade,Industry&Energy(MOTIE),Republic of Korea(P0017304)supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No.FA8702-15-D-0001。
文摘Single-atom catalysts(SACs)with multiple active sites exhibit high activity for a wide range of sluggish reactions,but identifying optimal multimetallic SAC is challenging due to the vast design space.Here,we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network(GNN)to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions(ORR/OER).Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active ones.The GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and selectivity.The computational predictions of promising Co-Fe,Co-Co,and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the literature.This approach can be extended to a broader class of multi-element high entropic materials systems.
