English

Percolation Threshold, Fisher Exponent, and Shortest Path Exponent for 4 and 5 Dimensions

Statistical Mechanics 2019-08-17 v1 Disordered Systems and Neural Networks

Abstract

We develop a method of constructing percolation clusters that allows us to build very large clusters using very little computer memory by limiting the maximum number of sites for which we maintain state information to a number of the order of the number of sites in the largest chemical shell of the cluster being created. The memory required to grow a cluster of mass s is of the order of sθs^\theta bytes where θ\theta ranges from 0.4 for 2-dimensional lattices to 0.5 for 6- (or higher)-dimensional lattices. We use this method to estimate dmind_{\scriptsize min}, the exponent relating the minimum path \ell to the Euclidean distance r, for 4D and 5D hypercubic lattices. Analyzing both site and bond percolation, we find dmin=1.607±0.005d_{\scriptsize min}=1.607\pm 0.005 (4D) and dmin=1.812±0.006d_{\scriptsize min}=1.812\pm 0.006 (5D). In order to determine dmind_{\scriptsize min} to high precision, and without bias, it was necessary to first find precise values for the percolation threshold, pcp_c: pc=0.196889±0.000003p_c=0.196889\pm 0.000003 (4D) and pc=0.14081±0.00001p_c=0.14081\pm 0.00001 (5D) for site and pc=0.160130±0.000003p_c=0.160130\pm 0.000003 (4D) and pc=0.118174±0.000004p_c=0.118174\pm 0.000004 (5D) for bond percolation. We also calculate the Fisher exponent, τ\tau, determined in the course of calculating the values of pcp_c: τ=2.313±0.003\tau=2.313\pm 0.003 (4D) and τ=2.412±0.004\tau=2.412\pm 0.004 (5D).

Keywords

Cite

@article{arxiv.cond-mat/0101136,
  title  = {Percolation Threshold, Fisher Exponent, and Shortest Path Exponent for 4 and 5 Dimensions},
  author = {Gerald Paul and Robert M. Ziff and H. Eugene Stanley},
  journal= {arXiv preprint arXiv:cond-mat/0101136},
  year   = {2019}
}