Ismail Ahmed E, Stephanopoulos George, Rutledge Gregory C
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
J Chem Phys. 2005 Jun 15;122(23):234902. doi: 10.1063/1.1924481.
In the preceding paper [A. E. Ismail, G. C. Rutledge, and G. Stephanopoulos J. Chem. Phys. (in press)] we introduced wavelet-accelerated Monte Carlo (WAMC), a coarse-graining methodology based on the wavelet transform, as a method for sampling polymer chains. In the present paper, we extend our analysis to consider excluded-volume effects by studying self-avoiding chains. We provide evidence that the coarse-grained potentials developed using the WAMC method obey phenomenological scaling laws, and use simple physical arguments for freely jointed chains to motivate these laws. We show that coarse-grained self-avoiding random walks can reproduce results obtained from simulations of the original, more-detailed chains to a high degree of accuracy, in orders of magnitude less time.
在前一篇论文中[A. E. 伊斯梅尔、G. C. 拉特利奇和G. 斯特凡诺普洛斯，《化学物理杂志》（即将发表）]，我们介绍了小波加速蒙特卡罗方法（WAMC），这是一种基于小波变换的粗粒化方法，用于聚合物链的采样。在本文中，我们通过研究自回避链来扩展分析，以考虑排除体积效应。我们提供证据表明，使用WAMC方法开发的粗粒化势遵循唯象标度律，并使用自由连接链的简单物理论据来推导这些定律。我们表明，粗粒化的自回避随机游走能够在比原始更详细的链模拟少几个数量级的时间内，高度准确地重现从原始链模拟中获得的结果。
