Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
J Comput Chem. 2011 Jul 15;32(9):1869-75. doi: 10.1002/jcc.21769. Epub 2011 Apr 4.
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, 15, 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.
测试了由随机生成原子坐标集，然后进行密度泛函理论几何优化组成的最简单的异构体搜索程序的效率，该程序应用于硅团簇系列（Si(5-10, 15, 20)）。使用产率、异构体分布和重现性等标准，明确了该方法在增加团簇尺寸方面的性能。还研究了消除非物理候选结构以及使用不同盒形状和理论水平的情况。对于较小的 Si(n)（n=5-10）团簇，在球形盒内生成随机坐标被发现是一种合理的替代方法，因为它可以直接识别每个已知的低能局域最小值。然而，对于更大的团簇（即 Si(15)和 Si(20)）的简单随机搜索，由于低能和高能最小值的数量呈指数级增加，导致结果相当随意且不全面。
