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Issue 11, 2017
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An efficient genetic algorithm for structure prediction at the nanoscale

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Abstract

We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ38). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)1–32, metallic Ni13 and covalently bonded C60. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ38, find new local and global minima for ZnO clusters, extensively explore the Ni13 and C60 (the buckminsterfullerene, or buckyball) potential energy surfaces.

Graphical abstract: An efficient genetic algorithm for structure prediction at the nanoscale

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Publication details

The article was received on 21 Nov 2016, accepted on 18 Jan 2017 and first published on 23 Jan 2017


Article type: Paper
DOI: 10.1039/C6NR09072A
Citation: Nanoscale, 2017,9, 3850-3864
  • Open access: Creative Commons BY license
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    An efficient genetic algorithm for structure prediction at the nanoscale

    T. Lazauskas, A. A. Sokol and S. M. Woodley, Nanoscale, 2017, 9, 3850
    DOI: 10.1039/C6NR09072A

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