Title
Cluster diversity and entropy on the percolation model : the lattice animal identification algorithm Cluster diversity and entropy on the percolation model : the lattice animal identification algorithm
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Lancaster, Pa ,
Subject
Physics
Source (journal)
Physical review : E : statistical physics, plasmas, fluids, and related interdisciplinary topics. - Lancaster, Pa, 1993 - 2000
Volume/pages
62(2000) :5 Part a , p. 6004-6014
ISSN
1063-651X
ISI
000165341700025
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
We present an algorithm to identify and count different lattice animals (LA's) in the site-percolation model. This algorithm allows a definition of clusters based on the distinction of cluster shapes, in contrast with the well-known Hoshen-Kopelman algorithm, in which the clusters are differentiated by their sizes. It consists in coding each unit. cell of a cluster according to the nearest neighbors (NN) and ordering the codes in a proper sequence. In this manner, a LA is represented by a specific code sequence. In addition, with some modification the algorithm is capable of differentiating between fixed and free LA's. The enhanced Hoshen-Kopelman algorithm [J. Hoshen, M. W. Berry, and K. S. Minser, Phys. Rev. E 56, 1455 (1997)] is used to compose the set of NN code sequences of each cluster. Using Monte Carlo simulations on planar square lattices up to 2000x2000, we apply this algorithm to the percolation model. We calculate the cluster diversity and cluster entropy of the system, which leads to the determination of probabilities associated with the maximum of these functions. We show that these critical probabilities are associated with the percolation transition and with the complexity of the system.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/2db18b/6087.pdf
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