Ab initio global optimization of clusters
Ab initio global optimization of clusters
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DOI：
10.1039/9781782622703-00249
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Jijun Zhao;Xiaoming Huang;Ruili Shi;Lingli Tang;Yan Su;Linwei Sai
中科院分区：
文献类型：
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作者：
Jijun Zhao;Xiaoming Huang;Ruili Shi;Lingli Tang;Yan Su;Linwei Sai
由于高维势能面的复杂性，确定团簇的基态结构具有挑战性。近年来，遗传算法、流域跳跃、拓扑法、粒子群优化、禁忌搜索、最小跳跃等从头算全局优化方法的发展取得了显著进展。其基本思想是避免陷入局部极小，探索整个势能面区域。所有这些方法在寻找各种团簇的基态结构方面都表现出了显著的性能。在本章中，我们将对这些方法进行概述。
Due to the complexity of high-dimensional potential energy surface, determining the ground state structure of a cluster is challenging. In recent years, there has been significant progress in the development of ab initio global optimization methods, such as genetic algorithm, basin hopping, topological methods, particle swarm optimization, tabu search, and minima hopping. The essential idea is to avoid being trapped in local minimum and to explore the entire region of potential energy surface. All these methods show remarkable performance in finding the ground state structures of various kinds of clusters. In this chapter, we present an overview of these methods.