A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO2 reduction

Steen Lysgaard, Jón Steinar Garðarsson Mýrdal, Heine Anton Hansen, Tejs Vegge

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Using a DFT-based genetic algorithm (GA) approach, we have determined the most stable structure and stoichiometry of a 309-atom icosahedral AuCu nanoalloy, for potential use as an electrocatalyst for CO2 reduction. The identified core–shell nano-particle consists of a copper core interspersed with gold atoms having only copper neighbors and a gold surface with a few copper atoms in the terraces. We also present an adsorbate-dependent correction scheme, which enables an accurate determination of adsorption energies using a computationally fast, localized LCAO-basis set. These show that it is possible to use the LCAO mode to obtain a realistic estimate of the molecular chemisorption energy for systems where the computation in normal grid mode is not computationally feasible. These corrections are employed when calculating adsorption energies on the Cu, Au and most stable mixed particles. This shows that the mixed Cu135@Au174 core–shell nanoalloy has a similar adsorption energy, for the most favorable site, as a pure gold nano-particle. Cu, however, has the effect of stabilizing the icosahedral structure because Au particles are easily distorted when adding adsorbates.
Original languageEnglish
JournalPhysical Chemistry Chemical Physics
Pages (from-to)28270-28276
Number of pages7
Publication statusPublished - 2015

Bibliographical note

This article is published Open Access as part of the RSC's Gold for Gold initiative, licensed under a Creative Commons Attribution 3.0 Unported Licence.


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