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A strategy to find minimal energy nanocluster structures
Author(s) -
Rogan José,
Varas Alejandro,
Valdivia Juan Alejandro,
Kiwi Miguel
Publication year - 2013
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.23419
Subject(s) - maxima and minima , potential energy surface , nanoclusters , cluster (spacecraft) , characterization (materials science) , set (abstract data type) , ab initio , computer science , relaxation (psychology) , potential energy , local search (optimization) , function (biology) , statistical physics , mathematical optimization , physics , algorithm , mathematics , materials science , nanotechnology , classical mechanics , quantum mechanics , mathematical analysis , psychology , social psychology , programming language , evolutionary biology , biology
An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard–Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ 13 , 10 new local minima for LJ 14 , and thousands of new local minima for 15 ≤ N ≤ 65 . Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low‐energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two‐ and three‐dimensional, that can be used as an input for refinement by means of ab initio methods. © 2013 Wiley Periodicals, Inc.