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arXiv 2103.09001cond-mat.stat-mechcond-mat.soft
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活性物质系统的动力学蒙特卡罗算法

Kinetic Monte-Carlo Algorithms for Active-Matter systems

Juliane U. Klamser, Olivier Dauchot, Julien Tailleur

摘要:

我们研究了活性粒子的动力学Monte-Carlo(KMC)描述。KMC算法依靠大的离散时间步长,加速主动系统的松弛动力学向其稳态。然而,我们发现,他们的连续时间限制是不明确的,导致消失的商标行为的活性物质,如运动诱导的相分离,棘轮效应,以及分散的机械压力。我们将展示如何混合被动步骤与主动步骤规范化这种行为,导致一个定义良好的连续时间限制。我们提出了新的AKMC算法,其连续时间的限制导致主动奥恩斯坦乌伦贝克,主动布朗,和运行和翻滚粒子的主动动力学。

英文摘要:

We study kinetic Monte-Carlo (KMC) descriptions of active particles. By relying on large discrete time steps, KMC algorithms accelerate the relaxational dynamics of active systems towards their steady-state. We show, however, that their continuous-time limit is ill-defined, leading to the vanishing of trademark behaviors of active matter such as the motility-induced phase separation, ratchet effects, as well as to a diverging mechanical pressure. We show how mixing passive steps with active ones regularizes this behavior, leading to a well-defined continuous-time limit. We propose new AKMC algorithms whose continuous-time limits lead to the active dynamics of Active-Ornstein Uhlenbeck, Active Brownian, and Run-and-Tumbles particles.

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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