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Issue 11, 2017

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

Article information


Submitted
21 Nov 2016
Accepted
18 Jan 2017
First published
23 Jan 2017

This article is Open Access

Nanoscale, 2017,9, 3850-3864
Article type
Paper

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

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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