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Clustering methods for the optimization of atomic cluster structure
The Journal of Chemical Physics ( IF 4.4 ) Pub Date : 2018-04-10 , DOI: 10.1063/1.5020858
Francesco Bagattini 1 , Fabio Schoen 1 , Luca Tigli 1
Affiliation  

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

中文翻译:

优化原子团簇结构的聚类方法

在本文中,我们提出了一种改进的全局优化方法,并将其应用于大规模集群构象问题。在1990年代,所谓的聚类方法被认为是最有效的通用全局优化技术之一;然而,近年来它们的使用迅速下降,这主要是由于在大维空间中聚类方法的固有困难。受到机器学习文献的启发,我们重新设计了聚类方法,以便在减少的特征空间中处理分子结构。我们的目的是要表明,通过适当地选择良好的组加上一个非常有效的下降法的几何特征中,获得一个有效的工具,其能够发现的,具有非常高的成功率,为中等大小的集群的所有已知推定的最优没有任何先验信息,包括Lennard-Jones和Morse势。主要结果是,除了一种可靠的方法之外,所提出的方法,基于仅在似乎值得这样做时才开始计算上昂贵的深度本地搜索的思想,相对于不采用聚类阶段的类似算法,它可以节省大量搜索。在本文中,与特定的,精巧的,最先进的过程相比,我们并没有主张所提出的方法的优越性,而是通过一种简化的聚类方案,指出了一种非常简单的方法来保存本地搜索可变空间,当包含在许多现代方法中时,这可能会很有用。
更新日期:2018-04-14
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