摘要
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.
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。
