论文标题
有效的ruderman-kittel-kasuya-yosida样互动中的稀释双交换模型：自学习的蒙特卡洛方法
Effective Ruderman-Kittel-Kasuya-Yosida-like interaction in diluted double-exchange model: self-learning Monte Carlo approach
论文作者
论文摘要
我们研究了现场稀释的双重交换（DE）模型及其有效的Ruderman-Kiter-kasuya-Yosida样相互作用，并使用自学习的蒙特卡洛（SLMC）方法随机分布局部旋转。 SLMC方法是使用可训练有效模型的马尔可夫链蒙特卡洛模拟的加速技术。我们将SLMC方法应用于站点稀释的DE模型，以探索SLMC方法对随机系统的实用性。我们检查接受率并研究有效模型在强耦合方案中的特性。位点量下的DE模型中有效的两体自旋旋转相互作用可以描述具有高接收率的原始DE模型，该模型取决于温度和自旋浓度。这些结果支持了SLMC方法可以在临界温度附近或随机系统中降低临界问题的系统中的独立配置的可能性。
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.
