Abstract
Global optimization of clusters is a subject of intense interest in computational chemistry. Especially for large clusters, locating the global minima is a challenging problem. Two strategies are generally used for the problem, i.e., the stochastic optimization and the static modeling strategy. The former is known as unbiased global optimization method, while the latter is more efficient but biased. This review describes the development of a dynamic lattice searching (DLS) approach. In DLS, the lattices are constructed dynamically and optimization is achieved by searching these lattices. Therefore, DLS possesses the characteristics of both the stochastic and static methods. With the aim of improving the efficiency of DLS for optimization of large clusters, several variants of the method have been developed. The results show that DLS methods may be promising tools for fast modeling of large clusters. With this review, greater interests are expected for global optimization of atomic or molecular clusters.
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Xueguang SHAO was born in Shandong province in 1963. He graduated from Liaocheng University in 1984 and received his Ph. D. in chemistry from University of Science and Technology of China (USTC) in 1992. He had been working in USTC from 1992 to 2005, and now is a professor of chemistry at the College of Chemistry, Nankai University. His research interests are the development of chemometric methods for chemical studies, including analytical signal processing and molecular simulation.
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Shao, X., Wu, X. & Cai, W. Dynamic lattice searching methods for optimization of clusters. Front. Chem. China 4, 335–342 (2009). https://doi.org/10.1007/s11458-009-0104-x
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DOI: https://doi.org/10.1007/s11458-009-0104-x