Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis;however,selecting efficient candidates through experiments is time-consuming and costly.Herein,we employed a data-driven virtual...Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis;however,selecting efficient candidates through experiments is time-consuming and costly.Herein,we employed a data-driven virtual screening(VS)method to discover new electrocatalysts.Specifically,we identified Cu-N_(2)sites for Zn-air batteries(ZABs)in harsh electrolytes by combining VS and machine learning(ML)techniques.A thermodynamically stable and highly active Cu-N_(2)Lewis acid site was pinpointed using molecular dynamics(MD)simulations and density functional theory(DFT)calculations:MD simulations filtered stable structures,while DFT calculations excluded those with high overpotentials by evaluating adsorption energies of oxygen intermediates using the“Volcano plots”theory.The ML model,trained in atomic types and geometries,predicted catalysts with DFT-level accuracy.The as-predicted Cu-N_(2)Lewis acid site was experimentally synthesized in a hollow nitrogen-doped octahedron carbon framework(Cu-N_(2)@HNOC),with a high Cu loading of 13.1 wt%.A ZAB with Cu-N_(2)@HNOC as the cathode catalyst showed prolonged cycling stability and a high maximum power density of 78.1 mW/cm^(2).Our strategy is applicable in the quest of valuable catalysts across a wide range of applications.With the accumulation and experimental validation of datasets to improve quality,this approach is expected to accurately predict promising electrocatalysts by integrating deep ML.展开更多
基金supported by National Natural Science Foundation of China(grant nos.52274298,51974114,51672075,and 2190804)International Postdoctoral Exchange Fellowship Program,China(grant no.PC2022020)+4 种基金the Natural Science Foundation of Hunan Province,China(grant nos.2024JJ4022 and 2025JJ60382)the Scientific Research Fund of Hunan Provincial Education Department,China(grant no.24B0270)Technology Innovation Program of Hunan Province,China(grant no.2020RC2024)China Postdoctoral Science Foundation(grant nos.2020M682560 and GZC20233205).The National Supercomputing Center in Changsha,China,is acknowledged for allowing the use of computational resourcesK.L.would like to thank the China Scholarship Council/UCL Dean’s Prize for the joint PhD funding.Engineering and Physical Sciences Research Council(EPSRC),United Kingdom,is thanked for funding through EP/L015862/1.Richard G.Compton from the University of Oxford,United Kingdom,assisted with the proofreading of the manuscript.
文摘Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis;however,selecting efficient candidates through experiments is time-consuming and costly.Herein,we employed a data-driven virtual screening(VS)method to discover new electrocatalysts.Specifically,we identified Cu-N_(2)sites for Zn-air batteries(ZABs)in harsh electrolytes by combining VS and machine learning(ML)techniques.A thermodynamically stable and highly active Cu-N_(2)Lewis acid site was pinpointed using molecular dynamics(MD)simulations and density functional theory(DFT)calculations:MD simulations filtered stable structures,while DFT calculations excluded those with high overpotentials by evaluating adsorption energies of oxygen intermediates using the“Volcano plots”theory.The ML model,trained in atomic types and geometries,predicted catalysts with DFT-level accuracy.The as-predicted Cu-N_(2)Lewis acid site was experimentally synthesized in a hollow nitrogen-doped octahedron carbon framework(Cu-N_(2)@HNOC),with a high Cu loading of 13.1 wt%.A ZAB with Cu-N_(2)@HNOC as the cathode catalyst showed prolonged cycling stability and a high maximum power density of 78.1 mW/cm^(2).Our strategy is applicable in the quest of valuable catalysts across a wide range of applications.With the accumulation and experimental validation of datasets to improve quality,this approach is expected to accurately predict promising electrocatalysts by integrating deep ML.
