A new strategy for global geometry optimization of clusters is presented.Important features are a restriction of search space to favorablenearest-neighbor distance ranges, a suitable cluster growth representation withdiminished correlations, and easy transferability of the results to largerclusters. The strengths and possible limitations of the method are demonstratedfor Si10 using an empirical potential.
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