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arXiv 2201.03531cond-mat.stat-mech
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利用张量网络态研究多粒子布朗棘轮

Using tensor network states for multi-particle Brownian ratchets

Nils E. Strand, Hadrien Vroylandt, Todd R. Gingrich

摘要:

对布朗棘轮的研究告诉我们,时间周期驱动如何支持产生非平衡输运的时间周期稳态。当单个粒子在一维中传输时,可以根据电势来合理化电流,但是实验努力已经超越了单体情况,进入了具有许多相互作用的载体的系统。工作与晶格模型的体积排除粒子在一个维度上,我们分析了相互作用的影响上的闪烁棘轮的电流。为了克服多体问题,我们采用了时间相关的变分原理与二叉树张量网络,方法在一个配套文件中详细讨论。张量网络方法不是传播单个轨迹,而是通过可控的变分近似在多体构型上传播分布。的计算,再现吉莱斯皮轨迹采样,确定和解释的最大电流的频率的移位,以更高的驱动频率的晶格占有率增加。

英文摘要:

The study of Brownian ratchets has taught how time-periodic driving supports a time-periodic steady state that generates nonequilibrium transport. When a single particle is transported in one dimension, it is possible to rationalize the current in terms of the potential, but experimental efforts have ventured beyond that single-body case to systems with many interacting carriers. Working with a lattice model of volume-excluding particles in one dimension, we analyze the impact of interactions on a flashing ratchet's current. To surmount the many-body problem, we employ the time-dependent variational principle with a binary tree tensor network, methods discussed at length in a companion paper. Rather than propagating individual trajectories, the tensor network approach propagates a distribution over many-body configurations via a controllable variational approximation. The calculations, which reproduce Gillespie trajectory sampling, identify and explain a shift in the frequency of maximum current to higher driving frequency as the lattice occupancy increases.

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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