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arXiv 0712.1735cond-mat.mtrl-sci
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Si(553)-Au表面结构的第一性原理研究

Systematic investigation of the structure of the Si(553)-Au surface from first principles

Sampsa Riikonen, Daniel Sanchez-Portal

摘要:

本文对Si(553)-Au重构结构进行了全面的研究。两百多个不同的试验结构进行了研究,使用第一性原理密度泛函计算与SIESTA代码。一个迭代程序,逐步增加的准确性和计算成本的计算,被用来允许研究这一大量的配置。我们考虑了限制在最顶层双层的重建,并研究了两种类型:i)“平坦”表面双层模型,其中最顶层双层的原子与底层主体呈现不同的配位和登记,ii)九种不同的模型,基于Si(553)表面的$π$键合链重建的不同位置上的硅原子被金原子取代。我们已经开发了一个紧凑的符号,使我们能够标记和识别所有这些结构。这对于自动生成试验几何形状和计算不等价结构的数量非常有用,即,具有不同的键合拓扑结构。最稳定的模型是那些在台阶边缘呈现蜂窝链结构的模型。我们的一个模型(模型f2)再现了室温光电发射和扫描隧道显微镜数据的主要特征。因此,f2结构是Si(553)-Au表面高温结构的一个很好的候选者。

英文摘要:

We present here a comprehensive search for the structure of the Si(553)-Au reconstruction. More than two hundred different trial structures have been studied using first-principles density-functional calculations with the SIESTA code. An iterative procedure, with a step-by-step increase of the accuracy and computational cost of the calculations, was used to allow for the study of this large number of configurations. We have considered reconstructions restricted to the topmost bilayer and studied two types: i) "flat" surface-bilayer models, where atoms at the topmost bilayer present different coordinations and registries with the underlying bulk, and ii) nine different models based on the substitution of a silicon atom by a gold atom in different positions of a $π$-bonded chain reconstruction of the Si(553) surface. We have developed a compact notation that allows us to label and identify all these structures. This is very useful for the automatic generation of trial geometries and counting the number of inequivalent structures, i.e., structures having different bonding topologies. The most stable models are those that exhibit a honeycomb-chain structure at the step edge. One of our models (model f2) reproduces the main features of the room temperature photoemission and scanning-tunneling microscopy data. Thus we conclude that f2 structure is a good candidate for the high temperature structure of the Si(553)-Au surface.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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