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.
Highlights
In the search for new tunable materials, in recent years, there has been growing interest in nanoclusters that show a strong correlation between their size, morphology, and physical and chemical properties.[1]
We report significant developments to KLMC and demonstrate its improved performance on predicting atomic structures of four chemically different systems
Previous results are labelled using the notation from the original publication, i.e. lower/upper case letters indicate the rank with regard to rigid ion model (RM)/ Shell Model (SM), respectively, whereas any missing local minima (LM) found by KLMC 2.0 are marked with a star after the size number
Summary
In the search for new tunable materials, in recent years, there has been growing interest in nanoclusters that show a strong correlation between their size, morphology, and physical and chemical properties.[1] Nanoclusters, or small nanoparticles, have typical dimensions below 2–5 nm, a size regime where current experimental techniques are insufficient for accurate and comprehensive structure characterisation It is where computational approaches can usefully complement and aid experimental studies. New developments in the GA module address the well-known issue of creating and maintaining structural diversity within the GA population of nanoclusters The power of this approach is demonstrated by searching for atomic structures of LJ38; an example of a challenging double funnel energy landscape. We conclude this paper with a summary of methodological developments and analysis of the chosen applications
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