High-entropy alloys(HEAs)exhibit exceptional catalytic performance due to their complex surface structures.However,the vast number of active binding sites in HEAs,as opposed to conventional alloys,presents a significa...High-entropy alloys(HEAs)exhibit exceptional catalytic performance due to their complex surface structures.However,the vast number of active binding sites in HEAs,as opposed to conventional alloys,presents a significant computational challenge in catalytic applications.To tackle this challenge,robust methods must be developed to efficiently explore the configurational space of HEA catalysts.Here,we introduce a novel approach that combines alchemical perturbation density functional theory(APDFT)with a graph-based correction scheme to explore the binding energy landscape of HEAs.Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost,significantly accelerating configurational space sampling.However,APDFT errors increase substantially when permutations occur near binding sites.To address this issue,we developed a graph-based Gaussian process regression model to correct discrepancies betweenAPDFT and conventional density functional theory values.Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV,requiring a handful of ab initio simulations.展开更多
基金the support of the National Research Council Canada via Contract No.987243New Frontiers in Research Fund(NFRFE-2019-01095)+1 种基金the Natural Sciences and Engineering Research Council of Canada(NSERC)through the Discovery Grant.M.H.gratefully acknowledges the financial support from the Department of Mechanical Engineering at UBC through the Four Years Fellowshipsupported through high-performance computational resources and services provided by Advanced Research Computing at the University of British Columbia and the Digital Research Alliance of Canada.
文摘High-entropy alloys(HEAs)exhibit exceptional catalytic performance due to their complex surface structures.However,the vast number of active binding sites in HEAs,as opposed to conventional alloys,presents a significant computational challenge in catalytic applications.To tackle this challenge,robust methods must be developed to efficiently explore the configurational space of HEA catalysts.Here,we introduce a novel approach that combines alchemical perturbation density functional theory(APDFT)with a graph-based correction scheme to explore the binding energy landscape of HEAs.Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost,significantly accelerating configurational space sampling.However,APDFT errors increase substantially when permutations occur near binding sites.To address this issue,we developed a graph-based Gaussian process regression model to correct discrepancies betweenAPDFT and conventional density functional theory values.Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV,requiring a handful of ab initio simulations.
