Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

Steen Lysgaard; David Dominic Landis; Thomas  Bligaard; Tejs Vegge

doi:10.1007/s11244-013-0160-9

Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

Steen Lysgaard, David Dominic Landis, Thomas Bligaard, Tejs Vegge

Research output: Contribution to journal › Journal article › Research › peer-review

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.

Original language	English
Journal	Topics in Catalysis
Volume	57
Issue number	1-4
Pages (from-to)	33-39
ISSN	1022-5528
DOIs	https://doi.org/10.1007/s11244-013-0160-9
Publication status	Published - 2014

Keywords

Nanoparticle catalysts
Genetic algorithms
Cu–Ni
Copper–Nickel

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title = "Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts",

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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author = "Steen Lysgaard and Landis, {David Dominic} and Thomas Bligaard and Tejs Vegge",

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AU - Lysgaard, Steen

AU - Landis, David Dominic

AU - Bligaard, Thomas

AU - Vegge, Tejs

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Nanoparticle catalysts

KW - Genetic algorithms

KW - Cu–Ni

KW - Copper–Nickel

U2 - 10.1007/s11244-013-0160-9

DO - 10.1007/s11244-013-0160-9

M3 - Journal article

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SP - 33

EP - 39

JO - Topics in Catalysis

JF - Topics in Catalysis

IS - 1-4

ER -

Genetic Algorithm Procreation Operators for Alloy Nanoparticle Catalysts

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Keywords

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