An efficient genetic algorithm for structure prediction at the nanoscale

摘要

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 (LJ(38)). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)(1-32), metallic Ni-13 and covalently bonded C-60. 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 LJ(38), find new local and global minima for ZnO clusters, extensively explore the Ni-13 and C-60 (the buckminsterfullerene, or buckyball) potential energy surfaces.