Search results for "Monte Carlo molecular modeling"
showing 10 items of 63 documents
Error estimation and reduction with cross correlations
2010
Besides the well-known effect of autocorrelations in time series of Monte Carlo simulation data resulting from the underlying Markov process, using the same data pool for computing various estimates entails additional cross correlations. This effect, if not properly taken into account, leads to systematically wrong error estimates for combined quantities. Using a straightforward recipe of data analysis employing the jackknife or similar resampling techniques, such problems can be avoided. In addition, a covariance analysis allows for the formulation of optimal estimators with often significantly reduced variance as compared to more conventional averages.
CLUSTER MONTE CARLO ALGORITHMS IN STATISTICAL MECHANICS
1992
The cluster Monte Carlo method, where variables are updated in groups, is very efficient at second order phase transitions. Much better results can be obtained with less computer time. This article reviews the method of Swendsen and Wang and some of its applications.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Theoretical Foundations of the Monte Carlo Method and Its Applications in Statistical Physics
2002
In this chapter we first introduce the basic concepts of Monte Carlo sampling, give some details on how Monte Carlo programs need to be organized, and then proceed to the interpretation and analysis of Monte Carlo results.
Monte Carlo Simulations of Alloy Phase Transformations
1994
The use of Monte Carlo simulation methods for study of order-disorder phase transitions in lattice models of alloys is reviewed, with an emphasis on interfacial phenomena and the kinetics of ordering and/or phase separation. Topics discussed include the attempt to predict the phase diagram of Fe-Al alloys from recent measurements of effective interaction parameters, competition between magnetic and crystallographic ordering in such alloys, and the structure of their antiphase domain boundaries. Both an interfacial roughening transition of this domain wall and interfacial enrichment phenomena are predicted. Then simulations of alloy-vacuum surfaces are discussed, and it is shown that both ca…
Simulation of Transport in Partially Miscible Binary Fluids: Combination of Semigrandcanonical Monte Carlo and Molecular Dynamics Methods
2004
Binary Fluids that exhibit a miscibility gap are ubiquitous in nature (glass melts, polymer solutions and blends, mixtures of molten metals, etc.) and exhibit a delicate interplay between static and dynamic properties. This is exemplified for a simple model system, the symmetrical AB Lennard-Jones mixture. It is shown how semigrandcanonical Monte Carlo methods, that include A→B (B→A) identity switches as Monte Carlo moves, can yield the phase diagram, the interfacial tension between coexisting phases, and various pair correlation functions and structure factors. In addition to the build-up of long-ranged concentration correlations near the critical point, unmixing is also accompanied by the…
Monte Carlo Simulations of Polymer Systems
1988
The impact of Monte Carlo “computer experiments” in polymer physics is described, emphasizing three examples taken from the author’s research group. The first example is a test of the classical Flory—Huggins theory for polymer mixtures, including a discussion of cricital phenomena. Also “technical aspects” of such simulations (“grand-canonical” ensemble, finite—size scaling, etc.) are explained briefly. The second example refers to configurational statistics and dynamics of chains confined to cylindrical tubes; the third example deals with the adsorption of polymers at walls. These simulations check scaling concepts developed along the lines of de Gennes.
Monte Carlo Simulations in Polymer Science
2012
Monte Carlo methods are useful for computing the statistical properties of both single macromolecules of various chemical architectures and systems containing many polymers (solutions, melts, blends, etc.). Starting with simple models (lattice models such as the self-avoiding walk or the bond fluctuation model, as well as coarse-grained or chemically realistic models in the continuum) various algorithms exist to generate conformations typical for thermal equilibrium, but dynamic Monte Carlo methods can also model diffusion and relaxation processes (as described by the Rouse and the reptation models for polymer melt dynamics). Limitations of the method are explained, and also the measures to…
Monte Carlo Simulations of Growth Kinetics and Phase Transitions at Interfaces: Some Recent Results
1991
ABSTRACTIn the first part Monte Carlo studies of the kinetics of multilayer adsorption (without screening) are described. The approach to the jamming coverage in each layer is asymptotically exponential. The jamming coverages approach the infinite-layer limit value according to a power law. In the second part, studies of phase transitions in two dimensional fluids are reviewed. With a combination of Monte Carlo and finite size scaling block analysis techniques, accurate values are obtained for the critical temperatures, coexistence densities and the compressibilities of an adsorbed fluid layer in an NVT ensemble.
Isotropic–isotropic phase separation in mixtures of rods and spheres: Some aspects of Monte Carlo simulation in the grand canonical ensemble
2008
Abstract In this article we consider mixtures of non-adsorbing polymers and rod-like colloids in the isotropic phase, which upon the addition of polymers show an effective attraction via depletion forces. Above a certain concentration, the depletant causes phase separation of the mixture. We performed Monte Carlo simulations to estimate the phase boundaries of isotropic–isotropic coexistence. To determine the phase boundaries we simulated in the grand canonical ensemble using successive umbrella sampling [J. Chem. Phys. 120 (2004) 10925]. The location of the critical point was estimated by a finite size scaling analysis. In order to equilibrate the system efficiently, we used a cluster move…