Geometric Cluster Algorithm for Interacting Fluids

We discuss a new Monte Carlo algorithm for the simulation of complex fluids. This algorithm employs geometric operations to identify clusters of particles that can be moved in a rejection-free way. It is demonstrated that this geometric cluster algorithm (GCA) constitutes the continuum generalization of the Swendsen-Wang and Wolff cluster algorithms for spin systems. Because of its nonlocal nature, it is particularly well suited for the simulation of fluid systems containing particles of widely varying sizes. The efficiency improvement with respect to conventional simulation algorithms is a rapidly growing function of the size asymmetry between the constituents of the system. We study the cluster-size distribution for a Lennard-Jones fluid as a function of density and temperature and provide a comparison between the generalized GCA and the hard-core GCA for a size-asymmetric mixture with Yukawa-type couplings.Comment: To appear in "Computer Simulation Studies in Condensed-Matter Physics XVII". Edited by D.P. Landau, S.P. Lewis and H.B. Schuettler. Springer, Heidelberg, 200

Rejection-free Geometric Cluster Algorithm for Complex Fluids

We present a novel, generally applicable Monte Carlo algorithm for the simulation of fluid systems. Geometric transformations are used to identify clusters of particles in such a manner that every cluster move is accepted, irrespective of the nature of the pair interactions. The rejection-free and non-local nature of the algorithm make it particularly suitable for the efficient simulation of complex fluids with components of widely varying size, such as colloidal mixtures. Compared to conventional simulation algorithms, typical efficiency improvements amount to several orders of magnitude

Generalized Geometric Cluster Algorithm for Fluid Simulation

We present a detailed description of the generalized geometric cluster algorithm for the efficient simulation of continuum fluids. The connection with well-known cluster algorithms for lattice spin models is discussed, and an explicit full cluster decomposition is derived for a particle configuration in a fluid. We investigate a number of basic properties of the geometric cluster algorithm, including the dependence of the cluster-size distribution on density and temperature. Practical aspects of its implementation and possible extensions are discussed. The capabilities and efficiency of our approach are illustrated by means of two example studies.Comment: Accepted for publication in Phys. Rev. E. Follow-up to cond-mat/041274

A New Monte Carlo Method and Its Implications for Generalized Cluster Algorithms

We describe a novel switching algorithm based on a ``reverse'' Monte Carlo method, in which the potential is stochastically modified before the system configuration is moved. This new algorithm facilitates a generalized formulation of cluster-type Monte Carlo methods, and the generalization makes it possible to derive cluster algorithms for systems with both discrete and continuous degrees of freedom. The roughening transition in the sine-Gordon model has been studied with this method, and high-accuracy simulations for system sizes up to $1024^2$ were carried out to examine the logarithmic divergence of the surface roughness above the transition temperature, revealing clear evidence for universal scaling of the Kosterlitz-Thouless type.Comment: 4 pages, 2 figures. Phys. Rev. Lett. (in press

Influence of primary particle density in the morphology of agglomerates

Agglomeration processes occur in many different realms of science such as colloid and aerosol formation or formation of bacterial colonies. We study the influence of primary particle density in agglomerate structure using diffusion-controlled Monte Carlo simulations with realistic space scales through different regimes (DLA and DLCA). The equivalence of Monte Carlo time steps to real time scales is given by Hirsch's hydrodynamical theory of Brownian motion. Agglomerate behavior at different time stages of the simulations suggests that three indices (fractal exponent, coordination number and eccentricity index) characterize agglomerate geometry. Using these indices, we have found that the initial density of primary particles greatly influences the final structure of the agglomerate as observed in recent experimental works.Comment: 11 pages, 13 figures, PRE, to appea

Selective-pivot sampling of radial distribution functions in asymmetric liquid mixtures

We present a Monte Carlo algorithm for selectively sampling radial distribution functions and effective interaction potentials in asymmetric liquid mixtures. We demonstrate its efficiency for hard-sphere mixtures, and for model systems with more general interactions, and compare our simulations with several analytical approximations. For interaction potentials containing a hard-sphere contribution, the algorithm yields the contact value of the radial distribution function.Comment: 5 pages, 5 figure

Colloidal stabilization via nanoparticle haloing

We present a detailed numerical study of effective interactions between micron-sized silica spheres, induced by highly charged zirconia nanoparticles. It is demonstrated that the effective interactions are consistent with a recently discovered mechanism for colloidal stabilization. In accordance with the experimental observations, small nanoparticle concentrations induce an effective repulsion that counteracts the intrinsic van der Waals attraction between the colloids and thus stabilizes the suspension. At higher nanoparticle concentrations an attractive potential is recovered, resulting in reentrant gelation. Monte Carlo simulations of this highly size-asymmetric mixture are made possible by means of a geometric cluster Monte Carlo algorithm. A comparison is made to results obtained from the Ornstein-Zernike equations with the hypernetted-chain closure