星团(航天器)
遗传算法
计算机科学
算法
比例(比率)
纳米-
金红石
化学计量学
材料科学
统计物理学
物理
化学
物理化学
机器学习
复合材料
有机化学
程序设计语言
量子力学
作者
Lasse B. Vilhelmsen,Bjørk Hammer
摘要
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA’s with a description of optimal parameters to use. New results for the adsorption of M8 clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO2(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
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