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Predicting structural models for silicon clusters
Author(s) -
Zacharias Carlos Renato,
Lemes Maurício Ruv,
Dal Pino Arnaldo,
Santo Orcero David
Publication year - 2003
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.10199
Subject(s) - computer science , silicon , algorithm , value (mathematics) , materials science , machine learning , metallurgy
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. © 2003 Wiley Periodicals, Inc. J Comput Chem 24: 869–875, 2003