Effective Ruderman–Kittel–Kasuya–Yosida-like Interaction in Diluted Double-exchange Model: Self-learning Monte Carlo Approach

Hidehiko Kohshiro, Y. Nagai
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引用次数: 6

Abstract

We study the site-diluted double exchange (DE) model and its effective Ruderman-Kittel-Kasuya-Yosida-like interactions, where localized spins are randomly distributed, with the use of the Self-learning Monte Carlo (SLMC) method. The SLMC method is an accelerating technique for Markov chain Monte Carlo simulation using trainable effective models. We apply the SLMC method to the site-diluted DE model to explore the utility of the SLMC method for random systems. We check the acceptance ratios and investigate the properties of the effective models in the strong coupling regime. The effective two-body spin-spin interaction in the site-diluted DE model can describe the original DE model with a high acceptance ratio, which depends on temperatures and spin concentration. These results support a possibility that the SLMC method could obtain independent configurations in systems with a critical slowing down near a critical temperature or in random systems where a freezing problem occurs in lower temperatures.
稀释双交换模型中有效的Ruderman-Kittel-Kasuya-Yosida-like相互作用:自学习蒙特卡罗方法
本文利用自学习蒙特卡罗(SLMC)方法研究了位置稀释双交换(DE)模型及其有效的ruderman - kittel - kasuya - yosida -类相互作用,其中定域自旋是随机分布的。SLMC方法是一种利用可训练有效模型进行马尔可夫链蒙特卡罗仿真的加速技术。我们将SLMC方法应用于站点稀释DE模型,以探索SLMC方法在随机系统中的实用性。我们检查了接受率,并研究了强耦合状态下有效模型的性质。在位置稀释DE模型中有效的两体自旋-自旋相互作用可以描述原始DE模型,并且具有较高的接受率,这取决于温度和自旋浓度。这些结果支持了一种可能性,即SLMC方法可以在临界温度附近具有临界慢化的系统或在较低温度下发生冻结问题的随机系统中获得独立构型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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