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Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

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Abstract

The long-term stability of binary nanoparticles and clusters is one of the main challenges in the development of novel (electro-)catalysts for e.g. CO2 reduction. Here, we present a method for predicting the optimal composition and structure of alloy nanoparticles and clusters, with particular focus on the surface properties. Based on a genetic algorithm (GA) we introduce and discuss efficient permutation operations that work by interchanging positions of elements depending on their local environment and position in the cluster. We discuss the fact that in order to be efficient, the operators have to be dynamic, i.e. change their behavior during the course of an algorithm run. The implementation of the GA including the customized operators is freely available at http://svn.fysik.dtu.dk/projects/pga.

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Authors and Affiliations

  1. Department of Energy Conversion and Storage, Technical University of Denmark, Frederiksborgvej 399, 4000, Roskilde, Denmark

    Steen Lysgaard & Tejs Vegge

  2. Center for Atomic-scale Materials Design, Department of Physics, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark

    David D. Landis

  3. SUNCAT Center for Interface Science & Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA

    Thomas Bligaard

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  1. Steen Lysgaard
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  2. David D. Landis
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  3. Thomas Bligaard
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  4. Tejs Vegge
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Correspondence to Steen Lysgaard.

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Lysgaard, S., Landis, D.D., Bligaard, T. et al. Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts. Top Catal 57, 33–39 (2014). https://doi.org/10.1007/s11244-013-0160-9

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