Predicting structural models for silicon clusters
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Data
2003-05-01
Autores
Zacharias, Carlos Renato
Lemes, Maurício Ruv
Dal Pino Júnior, Arnaldo
Orcero, David Santo
Título da Revista
ISSN da Revista
Título de Volume
Editor
Wiley-Blackwell
Resumo
This article introduces an efficient method to generate structural models for medium-sized silicon clusters. Geometrical information obtained from previous investigations of small clusters is initially sorted and then introduced into our predictor algorithm in order to generate structural models for large clusters. The method predicts geometries whose binding energies are close (95%) to the corresponding value for the ground-state with very low computational cost. These predictions can be used as a very good initial guess for any global optimization algorithm. As a test case, information from clusters up to 14 atoms was used to predict good models for silicon clusters up to 20 atoms. We believe that the new algorithm may enhance the performance of most optimization methods whenever some previous information is available. (C) 2003 Wiley Periodicals, Inc.
Descrição
Palavras-chave
classifier system, optimization, cluster, structural models, genetic algorithm
Como citar
Journal of Computational Chemistry. Hoboken: John Wiley & Sons Inc., v. 24, n. 7, p. 869-875, 2003.