A fast cluster counting algorithm for percolation on and off lattices

Stoll, E.

doi:10.1016/s0010-4655(97)00142-2

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…Therefore, two distant molecules are assigned to a single cluster if they are connected through other atoms whose state numbers are the same as theirs. By applying the clustering algorithm by Stoll, 36 we could analyze the percolation behavior of solid-like and gas-like structures in supercritical argon. Figure 2b shows the probability of finding an infinite cluster in a configuration (p inf ).…”

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confidence: 99%

Topological Characterization of Rigid–Nonrigid Transition across the Frenkel Line

Yoon

Lazar

et al. 2018

J. Phys. Chem. Lett.

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The dynamics of supercritical fluids, a state of matter beyond the gas–liquid critical point, changes from diffusive to oscillatory motions at high pressure. This transition is believed to occur across a locus of thermodynamic states called the Frenkel line. The Frenkel line has been extensively investigated from the viewpoint of the dynamics, but its structural meaning is still not well-understood. This Letter interprets the mesoscopic picture of the Frenkel line entirely based on a topological and geometrical framework. This discovery makes it possible to understand the mechanism of rigid–nonrigid transition based not on the dynamics of individual atoms but on their instantaneous configurations. The topological classification method reveals that the percolation of solid-like structures occurs above the rigid–nonrigid crossover densities.

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confidence: 99%

Topological Characterization of Rigid–Nonrigid Transition across the Frenkel Line

Yoon

Lazar

et al. 2018

J. Phys. Chem. Lett.

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“…1. Note that in the percolation theory similar definition was used for determination of off-lattice percolation clusters (Stoll, Stern and Stucki, 1996;Stoll, 1998).…”

Section: Resultsmentioning

confidence: 99%

Super-individual based model for fish migration

Babak

2014

Preprint

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One of the most remarkable characteristics of collective motion of fish is the emergence of complex migration patterns in which swimming fish are synchronised by remaining together and moving in the same direction. These migration patterns, referred to as fish schools, are often explained using individual based models (IBM’s) that focus on interactions between single individuals. The IBM’s appear to be realistic and robust; however, they are computationally unable to efficiently describe migration of large groups of fish. Here, an approach for developing computationally efficient super-individual based models from simple individual based models for fish migration is proposed. This approach accentuates on ecological mechanisms underlying collective motion of fish, and interaction between them; it explicitly incorporates such important mechanisms in collective motion of fish as fish school splitting and merging.

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“…For lattice systems, however, HK should still be the best choice due to the low memory use and the N log(N ) dependence on lattice size. Very recently, an interesting modification of the HK-algorithm has been described by Stoll which also works for continuous systems [27].…”

Section: Volume Fraction (Yo)mentioning

confidence: 99%

Cluster Identification and Percolation Analysis Using a Recursive Algorithm

Edvinsson

Råsmark

Elvingson

1999

Molecular Simulation

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A recursive algorithm for sampling properties of physical clusters such as size distribution and percolation is presented. The approach can be applied to any system with periodic boundary conditions, given a spatial definition of a cluster. We also introduce some modifications in the algorithm that increases the efficiency considerably if one is only interested in percolation analysis. The algorithm is implemented in Fortran 90 and is compared with a number of iterative algorithms. The recursive cluster identification algorithm is somewhat slower than the iterative methods at low volume fraction but is at least as fast at high densities. The percolation analysis, however, is considerably faster using recursion, for all systems studied. We also note that the CPU time using recursion is independent on the static allocation of arrays, whereas the iterative method strongly depends on the size of the initially allocated arrays.

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A fast cluster counting algorithm for percolation on and off lattices

Cited by 16 publications

References 13 publications

Topological Characterization of Rigid–Nonrigid Transition across the Frenkel Line

Topological Characterization of Rigid–Nonrigid Transition across the Frenkel Line

Super-individual based model for fish migration

Cluster Identification and Percolation Analysis Using a Recursive Algorithm

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