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arXiv 1502.06447cond-mat.soft
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基于事件链算法的高密度Polymer熔体的蒙特卡罗模拟

Monte Carlo Simulation of Dense Polymer Melts Using Event Chain Algorithms

Tobias Alexander Kampmann, Horst-Holger Boltz, Jan Kierfeld

摘要:

我们提出了一个有效的蒙特卡罗算法的密集的硬球聚合物熔体使用集群移动,称为事件链,它允许一个拒绝免费治疗的排除量的非晶格模拟。事件链还允许在聚合物熔体中有效制备初始构型。我们并行化的事件链蒙特卡罗算法,以进一步提高模拟速度,并建议额外的本地拓扑变化的动作(“交换”的动作),以加速平衡。通过与其他Monte Carlo和分子动力学模拟的比较,我们验证了事件链算法再现了熔体中聚合物链的正确平衡行为。通过比较聚合物内扩散的时间尺度,我们表明,事件链蒙特卡罗算法可以实现模拟速度相比,优化的分子动力学模拟。事件链蒙特卡罗算法在短时间尺度上表现出劳斯动力学。在没有交换的情况下,我们发现爬行动力学的中间时间尺度长链。

英文摘要:

We propose an efficient Monte Carlo algorithm for the off-lattice simulation of dense hard sphere polymer melts using cluster moves, called event chains, which allow for a rejection-free treatment of the excluded volume. Event chains also allow for an efficient preparation of initial configurations in polymer melts. We parallelize the event chain Monte Carlo algorithm to further increase simulation speeds and suggest additional local topology-changing moves ("swap" moves) to accelerate equilibration. By comparison with other Monte Carlo and molecular dynamics simulations, we verify that the event chain algorithm reproduces the correct equilibrium behavior of polymer chains in the melt. By comparing intrapolymer diffusion time scales, we show that event chain Monte Carlo algorithms can achieve simulation speeds comparable to optimized molecular dynamics simulations. The event chain Monte Carlo algorithm exhibits Rouse dynamics on short time scales. In the absence of swap moves, we find reptation dynamics on intermediate time scales for long chains.

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