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arXiv 1802.06177cond-mat.str-el
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Holstein模型的对称强制自学习Monte Carlo方法

Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model

Chuang Chen, Xiao Yan Xu, Junwei Liu, George Batrouni, Richard Scalettar, Zi Yang Meng

摘要:

自学习蒙特卡罗方法(Self-learning Monte Carlo method,SLMC)是近年来发展起来的一种用于加速多体系统模拟的通用数值方法。在这项工作中,我们进一步提高SLMC的效率,通过实施物理对称性的有效模型。我们证明了它的有效性的荷斯坦哈密顿量,最基本的多体描述的电子-声子耦合。由于费米子蒙特卡罗的典型立方标度和极长的自相关时间的存在,荷尔斯泰因模型的模拟是出了名的困难。我们的方法解决了这两个瓶颈。这使得能够在最困难的参数区域中对大晶格进行模拟,并以高精度评估半填充时电荷密度波跃迁的临界点。我们认为,我们的工作开辟了一个新的研究领域的量子蒙特卡罗(QMC),提供了一个通用的程序来处理遍历的情况下,涉及多个,不同的低能态的哈密顿。

英文摘要:

Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems. In this work, we further improve the efficiency of SLMC by enforcing physical symmetries on the effective model. We demonstrate its effectiveness in the Holstein Hamiltonian, one of the most fundamental many-body descriptions of electron-phonon coupling. Simulations of the Holstein model are notoriously difficult due to the combination of the typical cubic scaling of fermionic Monte Carlo and the presence of extremely long autocorrelation times. Our method addresses both bottlenecks. This enables simulations on large lattices in the most difficult parameter regions, and evaluation of the critical point for the charge density wave transition at half-filling with high precision. We argue that our work opens a new research area of quantum Monte Carlo (QMC), providing a general procedure to deal with ergodicity in situations involving Hamiltonians with multiple, distinct low energy states.

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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