Biomedical subjects

Wolfhard Janke

Publications and source records attributed to Wolfhard Janke.

8 recordsLinked to original sources

PubMed2004

Thermodynamics of lattice heteropolymers.

We calculate thermodynamic quantities of hydrophobic-polar (HP) lattice proteins by means of a multicanonical chain-growth algorithm that connects the new variants of the Pruned-Enriched Rosenbluth Method and flat histogram sampling of the entire energy space. Since our method directly simulates the density of states, we obtain results for thermodynamic quantities of the system for all temperatures. In particular, this algorithm enables us to accurately simulate the usually difficult accessible low-temperature region. Therefore, it becomes possible to perform detailed analyses of the low-temperature transition between ground states and compact globules.

Algorithms↗

PubMed2003

Multicanonical chain-growth algorithm.

We present a temperature-independent Monte Carlo method for the determination of the density of states of lattice proteins that combines the fast ground-state search strategy of the new pruned-enriched Rosenbluth chain-growth method and multicanonical reweighting for sampling the complete energy space. Since the density of states contains all energetic information of a statistical system, we can directly calculate the mean energy, specific heat, Helmholtz free energy, and entropy for all temperatures. We apply this method to lattice proteins consisting of hydrophobic and polar monomers, and for the examples of sequences considered, we identify the transitions between native, globule, and random coil states. Since no special properties of heteropolymers are involved in this algorithm, the method applies to polymer models as well.

Algorithms↗

PubMed2003

Star-graph expansions for bond-diluted Potts models.

We derive high-temperature series expansions for the free energy and the susceptibility of random-bond q-state Potts models on hypercubic lattices using a star-graph expansion technique. This method enables the exact calculation of quenched disorder averages for arbitrary uncorrelated coupling distributions. Moreover, we can keep the disorder strength p as well as the dimension d as symbolic parameters. By applying several series analysis techniques to the new series expansions, one can scan large regions of the (p,d) parameter space for any value of q. For the bond-diluted four-state Potts model in three dimensions, which exhibits a rather strong first-order phase transition in the undiluted case, we present results for the transition temperature and the effective critical exponent gamma as a function of p as obtained from the analysis of susceptibility series up to order 18. A comparison with recent Monte Carlo data [Chatelain et al., Phys. Rev. E 64, 036120 (2001)] shows signals for the softening to a second-order transition at finite disorder strength.

Journal Article↗

PubMed2003

Approximate calculation of the ground-state energy for Potts spin-glass models.

We consider the q-state Potts spin-glass model, with quenched couplings taking two different values only. As an approximation for this model a proper generalization of the random energy model is derived. Formulas of the resulting diluted generalized random energy model (DGREM) are applied to calculate the ground-state energy for the two-dimensional Potts spin-glass model. The semianalytical results are compared with numerical determinations of the ground-state energy, using multicanonical, random cost, and simulated annealing techniques.

Journal Article↗

PubMed2002

Overlap distribution of the three-dimensional Ising model.

We study the Parisi overlap probability density P(L)(q) for the three-dimensional Ising ferromagnet by means of Monte Carlo (MC) simulations. At the critical point, P(L)(q) is peaked around q=0 in contrast with the double peaked magnetic probability density. We give particular attention to the tails of the overlap distribution at the critical point, which we control over up to 500 orders of magnitude by using the multioverlap MC algorithm. Below the critical temperature, interface tension estimates from the overlap probability density are given and their approach to the infinite volume limit appears to be smoother than for estimates from the magnetization.

Journal Article↗

PubMed2002

Phase transition in complex /psi/(4) theory.

Motivated by recent claims for rather unconventional first-order phase transitions in the two- and three-dimensional complex /psi/(4) theory in certain parameter ranges we performed Monte Carlo simulation studies of this model. From our results in two and three dimensions we can unambiguously conclude that there is no evidence for a first-order transition, provided the measure of field fluctuations is treated properly. The origin of the discrepancy is traced by comparative simulations reproducing the erroneous results and by a transfer-matrix study of the one-dimensional case.

Journal Article↗

PubMed2002

Functional form of the Parisi overlap distribution for the three-dimensional Edwards-Anderson Ising spin glass.

Recently, it has been conjectured that the statistics of extremes is of relevance for a large class of correlated systems. For certain probability densities this predicts the characteristic large x falloff behavior f(x) approximately exp(-ae(x)), a>0. Using a multicanonical Monte Carlo technique, we have measured the Parisi overlap distribution P(q) for the three-dimensional Edward-Anderson Ising spin glass at and below the critical temperature We find that a probability distribution related to extreme-order statistics gives an excellent description of P(q) over about 80 orders of magnitude.

Journal Article↗

PubMed2002

Random-cluster multihistogram sampling for the q-state Potts model.

Using the random-cluster representation of the q-state Potts models we consider the pooling of data from cluster-update Monte Carlo simulations for different thermal couplings K and number of states per spin q. Proper combination of histograms allows for the evaluation of thermal averages in a broad range of K and q values, including noninteger values of q. Due to restrictions in the sampling process correct normalization of the combined histogram data is nontrivial. We discuss the different possibilities and analyze their respective ranges of applicability.

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