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Genetic Algorithms for the Geometry Optimization of Clusters and Nanoparticles

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Soft Computing Approaches in Chemistry

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 120))

Abstract

A review is presented of the design and application of Genetic Algorithms for the geometry optimization (energy minimization) of clusters and nanoparticles, where the interactions between atoms, ions or molecules are described by a variety of potential energy functions (force fields). A detailed description is presented of the Birmingham Cluster Genetic Algorithm Program, developed in our group, and a number of specific applications are highlighted. Finally, a number of recent innovations and possible future developments are discussed.

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Authors and Affiliations

  1. School of Chemical Sciences, University of Birmingham, Birmingham, B15 2TT, UK

    Roy L. Johnston & Christopher Roberts

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  1. Roy L. Johnston
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  2. Christopher Roberts
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Editors and Affiliations

  1. Physical and Theoretical Chemistry, Oxford University, South Parks Road, OX1 3QZ, Oxford, UK

    Hugh M. Cartwright

  2. CIS Department, Philadelphia University, 19144, Philadelphia, PA, USA

    Les M. Sztandera

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Johnston, R.L., Roberts, C. (2003). Genetic Algorithms for the Geometry Optimization of Clusters and Nanoparticles. In: Cartwright, H.M., Sztandera, L.M. (eds) Soft Computing Approaches in Chemistry. Studies in Fuzziness and Soft Computing, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36213-5_7

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  • DOI: https://doi.org/10.1007/978-3-540-36213-5_7

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