Percolation and cluster Monte Carlo dynamics for spin models

Cataudella, V.; Franzese, Giancarlo; Nicodemi, Mario; Scala, Antonio; Coniglio, Antonio

doi:10.1103/physreve.54.175

Cited by 31 publications

(29 citation statements)

References 46 publications

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“…To each molecules we associate a cell on a square lattice. The Wolff algorithm is based on the definition of a cluster of variables chosen in such a way to be thermodynamically correlated [18,19]. To define the Wolff cluster, a bond index (arm) of a molecule is randomly selected; this is the initial element of a stack.…”

Section: The Simulation With the Wolff's Clusters Monte Carlo Algorithmmentioning

confidence: 99%

Cluster Monte Carlo and numerical mean field analysis for the water liquid–liquid phase transition

Mazza

Stokely

Strekalova

et al. 2009

Computer Physics Communications

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Using Wolff's cluster Monte Carlo simulations and numerical minimization within a mean field approach, we study the low temperature phase diagram of water, adopting a cell model that reproduces the known properties of water in its fluid phases. Both methods allow us to study the thermodynamic behavior of water at temperatures, where other numerical approaches -both Monte Carlo and molecular dynamics -are seriously hampered by the large increase of the correlation times. The cluster algorithm also allows us to emphasize that the liquid-liquid phase transition corresponds to the percolation transition of tetrahedrally ordered water molecules.Published by Elsevier B.V.

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Section: The Simulation With the Wolff's Clusters Monte Carlo Algorithmmentioning

confidence: 99%

Cluster Monte Carlo and numerical mean field analysis for the water liquid–liquid phase transition

Mazza

Stokely

Strekalova

et al. 2009

Computer Physics Communications

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“…Different algorithms have been developed to optimize the MC simulation of spin models and cell models [56,57]. The Wolff cluster algorithm [58] has become a particularly useful tool for the simulation of water when coarse-grained or cell models are utilized [53,[59][60][61].…”

Section: Simulations Of a Coarse-grained Model Of Water Confined Imentioning

confidence: 99%

Effect of hydrophobic environments on the hypothesized liquid-liquid critical point of water

et al. 2011

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The complex behavior of liquid water, along with its anomalies and their crucial role in the existence of life, continue to attract the attention of researchers. The anomalous behavior of water is more pronounced at subfreezing temperatures and numerous theoretical and experimental studies are directed towards developing a coherent thermodynamic and dynamic framework for understanding supercooled water. The existence of a liquid-liquid critical point in the deep supercooled region has been related to the anomalous behavior of water. However, the experimental study of supercooled water at very low temperatures is hampered by the homogeneous nucleation of the crystal. Recently, water confined in nanoscopic structures or in solutions has attracted interest because nucleation can be delayed. These systems have a tremendous relevance also for current biological advances; e.g., supercooled water is often confined in cell membranes and acts as a solvent for biological molecules. In particular, considerable attention has been recently devoted to understanding hydrophobic interactions or the behavior of water in the presence of apolar interfaces due to their fundamental role in self-assembly of micelles, membrane formation and protein folding. This article reviews and compares two very recent computational works aimed at elucidating the changes in the thermodynamic behavior in the supercooled region and the liquid-liquid critical point phenomenon for water in contact with hydrophobic environments. The results are also compared to previous reports for water in hydrophobic environments.

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“…To each molecules we associate a cell on a square lattice. The Wolff's algorithm is based on the definition of a cluster of 41,42 To selected; this is the initial element of a stack. The cluster is grown by first same Potts state, then they are added to the stack with probability p same ≡ min σ 43 where β ≡ (k B T) −1 .…”

Section: K Stokely Et Almentioning

confidence: 99%

“…This choice for the probability p same σ guarantees that the connected arms are thermodynamically correlated. 41 Next, guarantee that connected facing arms correspond to thermodynamically correlated variables, is necessary 42 proposed in 10 which gives rise to the SF scenario (Fig. 3a).…”

Section: K Stokely Et Almentioning

confidence: 99%

Metastable Water Under Pressure

Stokely

Mazza

Stanley

et al. 2010

Metastable Systems Under Pressure

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for bulk, confined and interfacial water. By analyzing a cell model within a mean field approximation and with Monte Carlo simulations, we have showed that all the scenarios proposed for water's P-T phase diagram may be viewed as special cases of a more general scheme. In particular, our study shows that it is the relationship between H bond strength and H bond cooperativity that governs which scenario is valid. The investigation of the properties of metastable liquid water under pressure could provide essential information that could allow us to understanding could, ultimately, lead us to the explanation of the reasons why water is such an essential liquid for life.

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Percolation and cluster Monte Carlo dynamics for spin models

Cited by 31 publications

References 46 publications

Cluster Monte Carlo and numerical mean field analysis for the water liquid–liquid phase transition

Cluster Monte Carlo and numerical mean field analysis for the water liquid–liquid phase transition

Effect of hydrophobic environments on the hypothesized liquid-liquid critical point of water

Metastable Water Under Pressure

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