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arXiv 1005.1685cond-mat.str-elcond-mat.supr-con
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细胞动力学场论中的浴槽优化

Bath optimization in the Cellular Dynamical Mean Field Theory

David Senechal

摘要:

在细胞动力学平均场论(CDMFT)中,一个强相关系统由一小群相关位点表示,耦合到一个可调节的不相关位点池,模拟集群的环境;控制池的参数由涉及局部格林函数和晶格电子色散的自洽条件设定。用精确对角化方法解决簇问题仅适用于小浴尺寸(8个位点)。在这种情况下,自洽条件不能完全满足,并由一个最小化过程取代。在“价值函数”的定义方面有一定的自由度,可以进行优化。我们使用Potthoff的自能函数方法的一维和二维的哈伯德模型,以获得洞察到最佳的选择,这个价值函数。我们认为,应使用几个价值函数和优先考虑的一个,导致最小梯度的Potthoff自能功能。我们提出了一个新的价值函数加权的自能,似乎适合莫特过渡在二维比其他价值函数。

英文摘要:

In the Cellular Dynamical Mean Field Theory (CDMFT), a strongly correlated system is represented by a small cluster of correlated sites, coupled to an adjustable bath of uncorrelated sites simulating the cluster's environment; the parameters governing the bath are set by a self-consistency condition involving the local Green function and the lattice electron dispersion. Solving the cluster problem with an exact diagonalization method is only practical for small bath sizes (8 sites). In that case the self-consistency condition cannot be exactly satisfied and is replaced by a minimization procedure. There is some freedom in the definition of the `merit function' to optimize. We use Potthoff's Self-Energy Functional Approach on the one- and two-dimensional Hubbard models to gain insight into the best choice for this merit function. We argue that several merit functions should be used and preference given to the one that leads to the smallest gradient of the Potthoff self-energy functional. We propose a new merit function weighted with the self-energy that seems to fit the Mott transition in two dimensions better than other merit functions.

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