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arXiv 2303.02458cond-mat.stat-mechphysics.comp-ph
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基于L形团簇的方格子模型数值链式团簇展开

L-based numerical linked cluster expansion for square lattice models

Mahmoud Abdelshafy, Marcos Rigol

AI总结:

本文提出基于L形团簇的数值链式团簇展开,用于方格子模型,其收敛性优于或相当于方形团簇,强嵌入版本更优,且可研究连接方形与三角形格子的模型。

摘要:

我们提出了一种针对方格子模型的数值链式团簇展开方法,其构建单元为L形团簇。对于本工作中研究的自旋-1/2模型,我们发现该展开在裸求和上的收敛性与(更大的)方形团簇相当或更优,并且可与重求和技术(如基于格点和基于键的展开)结合使用,以在更低温度下获得结果。我们比较了该展开的弱嵌入和强嵌入版本在各种自旋-1/2模型中的表现,结果表明强嵌入版本因其收敛特性和较低的计算成本而更受青睐。最后,我们展示了基于L形团簇的展开可自然地用于研究平滑连接方形和三角形格子几何的格子模型的性质。

英文摘要:

We introduce a numerical linked cluster expansion for square-lattice models whose building block is an L-shape cluster. For the spin-1/2 models studied in this work, we find that this expansion exhibits a similar or better convergence of the bare sums than that of the (larger) square-shaped clusters, and can be used with resummation techniques (like the site- and bond-based expansions) to obtain results at even lower temperatures. We compare the performance of weak- and strong-embedding versions of this expansion in various spin-1/2 models, and show that the strong-embedding version is preferable because of its convergence properties and lower computational cost. Finally, we show that the expansion based on the L-shape cluster can be naturally used to study properties of lattice models that smoothly connect the square and triangular lattice geometries.

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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