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作 者:Shuang Han Steen Lysgaard Tejs Vegge Heine Anton Hansen
出 处:《npj Computational Materials》2023年第1期905-918,共14页计算材料学(英文)
基 金:The authors thank the financial support from the BIKE project:BImetallic catalysts Knowledge-based development for Energy applications;The BIKE project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Action-International Training Network(MSCA-ITN),grant agreement 813748;The authors also thank the Villum Fonden for funding through the project V-sustain(No.9455);the Niflheim Linux super-computer cluster installed at the Department of Physics at the Technical University of Denmark for providing computational resources.
摘 要:Surface phase diagrams(SPDs)are essential for understanding the dependence of surface chemistry on reaction condition.For multi-component systems such as metal alloys,the derivation of such diagrams often relies on separate first-principles global optimization tasks under different reaction conditions.Here we show that this can be significantly accelerated by leveraging the fact that all tasks essentially share a unified configurational search space,and only a single expensive electronic structure calculation is required to evaluate the stabilities of a surface structure under all considered reaction conditions.As a general solution,we propose a Bayesian evolutionary multitasking(BEM)framework combining Bayesian statistics with evolutionary multitasking,which allows efficient mapping of SPDs even for very complex surface systems.As proofs of concept,we showcase the performance of our methods in deriving the alloy SPDs for two heterogeneous catalytic systems:the electrochemical oxygen reduction reaction(ORR)and the gas phase steam methane reforming(SMR)reaction.
分 类 号:TG13[一般工业技术—材料科学与工程]
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