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arXiv 0706.3255cond-mat.str-elcond-mat.stat-mech
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数字链接聚类算法。II。正方格子上的t-J模型

Numerical Linked-Cluster Algorithms. II. t-J models on the square lattice

Marcos Rigol, Tyler Bryant, Rajiv R. P. Singh

摘要:

我们讨论了应用最近推出的数值连接集群(NLC)算法强相关巡回模型。特别是,我们提出了一个研究的热力学观测:化学势,熵,比热,和均匀磁化率的t-J模型的正方形晶格上,与J/t=0.5和0.3。我们的NLC结果进行了比较,从高温膨胀(HTE)和有限温度Lanczos方法(FTLM)。我们发现,有一个相当大的窗口,在温度下,NLC的结果收敛,而没有外推HTE发散。外推后,NLC、HTE和FTLM之间的总体一致性在某些情况下非常好,低至0.25 t。在中等温度下,NLC结果比其他方法更好地控制,使得更容易判断该方法的收敛性和数值精度。

英文摘要:

We discuss the application of a recently introduced numerical linked-cluster (NLC) algorithm to strongly correlated itinerant models. In particular, we present a study of thermodynamic observables: chemical potential, entropy, specific heat, and uniform susceptibility for the t-J model on the square lattice, with J/t=0.5 and 0.3. Our NLC results are compared with those obtained from high-temperature expansions (HTE) and the finite-temperature Lanczos method (FTLM). We show that there is a sizeable window in temperature where NLC results converge without extrapolations whereas HTE diverges. Upon extrapolations, the overall agreement between NLC, HTE, and FTLM is excellent in some cases down to 0.25t. At intermediate temperatures NLC results are better controlled than other methods, making it easier to judge the convergence and numerical accuracy of the method.

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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