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The efficiency of the simplest isomeric search procedure consisting in random generation of sets of atomic coordinates followed by density functional theory geometry optimization is tested on the silicon cluster series (Si-5-10,Si-15,Si-20). Criteria such as yield, isomer distributions and recurrences are used to clearly establish the performance of the approach with respect to increasing cluster size. The elimination of unphysical candidate structures and the use of distinct box shapes and theoretical levels are also investigated. For the smaller Si-n (n = 5-10) clusters, the generation of random coordinates within a spherical box is found to offer a reasonable alternative to more complex algorithms by allowing straightforward identification of every known low-lying local minima. The simple stochastic search of larger clusters (i.e. Si-15 and Si-20) is however complicated by the exponentially increasing number of both low-and high-lying minima leading to rather arbitrary and non-comprehensive results. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 1869-1875, 2011 |
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