Improved Monte Carlo distribution

Bowen, Paul; Burke, John; Corsten, P. G.; Crowell, K. J.; Farrell, Kevin; Macdonald, Jack; Macdonald, Rainer; MacIsaac, A. B.; MacIsaac, S C; Poole, Peter H.; Jan, Naeem

doi:10.1103/physrevb.40.7439

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…• the use of multihistograms to extend the coupling range over which extrapolation of information is reliable [61,62].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…These improvements include • the advent of non-local algorithms [14,58] which greatly reduce critical slowing down, • the rediscovery and development of the spectral density method which allows extrapolation away from the simulation point [59,60], • the use of multihistograms to extend the coupling range over which extrapolation of information is reliable [61,62].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In order to be able to find the Lee-Yang zeroes at temperatures other than the simulation points β 0 , we employ a multihistogram reweighting technique (the spectral density method [59,60,61,62]) which accomodates extrapolation between β 0 values. In view of the large amounts of data now involved it is necessary to introduce binning.…”

Section: Multihistograms and The Spectral Density Methodsmentioning

confidence: 99%

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The Kosterlitz-Thouless universality class

Kenna

Irving

1997

Nuclear Physics B

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We examine the Kosterlitz-Thouless universality class and show that essential scaling at this type of phase transition is not selfconsistent unless multiplicative logarithmic corrections are included. In the case of specific heat these logarithmic corrections are identified analytically. To identify those corresponding to the susceptibility we set up a numerical method involving the finite-size scaling of LeeYang zeroes. We also study the density of zeroes and introduce a new concept called index scaling. We apply the method to the XY -model and the closely related step model in two dimensions. The critical parameters (including logarithmic corrections) of the step model are compatable with those of the XY -model indicating that both models belong to the same universality class. This result then raises questions over how a vortex binding scenario can be the driving mechanism for the phase transition. Furthermore, the logarithmic corrections identified numerically by our methods of fitting are not in agreement with the renormalization group predictions of Kosterlitz and Thouless.

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“…• the use of multihistograms to extend the coupling range over which extrapolation of information is reliable [61,62].…”

Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Multihistograms and The Spectral Density Methodsmentioning

confidence: 99%

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The Kosterlitz-Thouless universality class

Kenna

Irving

1997

Nuclear Physics B

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“…the data for each lattice size were cut to produce 10 subsamples leading to different multihistograms and thus to different results, whence the variance and bias were calculated [29].…”

Section: Numerical Calculations and Resultsmentioning

confidence: 99%

Renormalization group analysis of finite-size scaling in the ø44 model

Kenna

Lang

1993

Nuclear Physics B

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A finite-size scaling theory for the φ 4 4 model is derived using renormalization group methods. Particular attention is paid to the partition function zeroes, in terms of which all thermodynamic observables can be expressed. While the leading scaling behaviour is identical to that of mean field theory, there exist multiplicative logarithmic corrections too. A non-perturbative test of these formulae in the form of a high precision Monte Carlo analysis reveals good quantitative agreement with the analytical predictions. * Supported by Fonds zur Förderung der Wissenschaftlichen Forschung inÖsterreich, project P7849.

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“…However, they can all be merged into one big histogram. The individual histograms are matched to the same amplitude in the regions where they overlap (Bowen et al, 1989;Ivanova and Heimburg, 2001). After matching all 3000 histograms (we used an automized computer procedure), one obtains one fourdimensional histogram that covers the whole accessible phase space, which can now be normalized such that the sum of all probabilities is unity.…”

Section: Methodsmentioning

confidence: 99%

Analyzing Heat Capacity Profiles of Peptide-Containing Membranes: Cluster Formation of Gramicidin A

Ivanova

Makarov

Schäffer

et al. 2003

Biophysical Journal

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The analysis of peptide and protein partitioning in lipid membranes is of high relevance for the understanding of biomembrane function. We used statistical thermodynamics analysis to demonstrate the effect of peptide mixing behavior on heat capacity profiles of lipid membranes with the aim to predict peptide aggregation from c(P)-profiles. This analysis was applied to interpret calorimetric data on the interaction of the antibiotic peptide gramicidin A with lipid membranes. The shape of the heat capacity profiles was found to be consistent with peptide clustering in both gel and fluid phase. Applying atomic force microscopy, we found gramicidin A aggregates and established a close link between thermodynamics data and microscopic imaging. On the basis of these findings we described the effect of proteins on local fluctuations. It is shown that the elastic properties of the membrane are influenced in the peptide environment.

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Improved Monte Carlo distribution

Cited by 19 publications

References 10 publications

The Kosterlitz-Thouless universality class

The Kosterlitz-Thouless universality class

Renormalization group analysis of finite-size scaling in the ø44 model

Analyzing Heat Capacity Profiles of Peptide-Containing Membranes: Cluster Formation of Gramicidin A

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