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Close to a solid surface, the properties of a fluid deviate significantly from their bulk values. In this context, we study the surface adsorption profiles of a symmetric binary liquid confined to a slit pore by means of molecular dynamics…

Soft Condensed Matter · Physics 2025-01-15 Sutapa Roy , Felix Höfling

Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the…

Chemical Physics · Physics 2022-11-16 Ian Rouse , Vladimir Lobaskin

Connectedness percolation phenomena in the two-dimensional (2D) packing of binary mixtures of disks with different diameters were studied numerically. The packings were produced using random sequential adsorption (RSA) model with…

Soft Condensed Matter · Physics 2024-04-02 N. I. Lebovka , M. R. Petryk , N. V. Vygornitskii

In this article we propose a novel MCMC method based on deterministic transformations T: X x D --> X where X is the state-space and D is some set which may or may not be a subset of X. We refer to our new methodology as Transformation-based…

Computation · Statistics 2013-10-21 Somak Dutta , Sourabh Bhattacharya

Background: Designing amino acid sequences that are stable in a given target structure amounts to maximizing a conditional probability. A straightforward approach to accomplish this is a nested Monte Carlo where the conformation space is…

Soft Condensed Matter · Physics 2016-08-31 Anders Irbäck , Carsten Peterson , Frank Potthast , Erik Sandelin

The standard DLVO theory offers a limited description of ionic-surfacted magnetic colloids in near aggregation regimes. Correcting the electrical double layer term for ionic surfactants is not enough to successfully simulate the systems.…

In this comprehensive and detailed study, vacancy-mediated self-diffusion of A- and B-elements in 'triple-defect' B2-ordered ASB(1-S) binaries is simulated by means of a kinetic Monte Carlo (KMC) algorithm involving atomic jumps to…

Materials Science · Physics 2019-10-16 Jan Betlej , Piotr Sowa , Rafal Kozubski , Graeme E. Murch , Irina V. Belova

The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective…

Methodology · Statistics 2024-08-06 Philip Schär , Michael Habeck , Daniel Rudolf

A lattice gas model of adsorption inside cylindrical pores is evaluated with Monte Carlo simulations. The model incorporates two kinds of site: (a line of) ``axial'' sites and surrounding ``cylindrical shell'' sites, in ratio 1:7. The…

Statistical Mechanics · Physics 2009-11-10 Raluca A. Trasca , M. Mercedes Calbi , Milton W. Cole , Jose L. Riccardo

Structural and kinetic aspects of 2-D irreversible metal deposition under potentiostatic conditions are analyzed by means of dynamic Monte Carlo simulations employing embedded atom potentials for a model system. Three limiting models, all…

Materials Science · Physics 2007-05-23 M. Cecilia Gimenez , Mario G. Del Popolo , Ezequiel P. M. Leiva

Density-dependent diffusion is a widespread phenomenon in nature. We have examined the density-dependent diffusion behavior of some biological processes such as tumor growth and invasion [23]. Here, we extend our previous work by developing…

Statistical Mechanics · Physics 2022-02-14 Ahmed M. Fouad , Marwa M. Fouad

We propose an efficient Monte Carlo method for the computation of the volumes of high-dimensional bodies with arbitrary shape. We start with a region of known volume within the interior of the manifold and then use the multistate Bennett…

Disordered Systems and Neural Networks · Physics 2016-09-21 Stefano Martiniani , K. Julian Schrenk , Jacob D. Stevenson , David J. Wales , Daan Frenkel

We have used the Monte Carlo (MC) simulation method with Metropolis algorithm to study the finite temperature phase transition properties of a binary alloy spherical nanoparticle with radius $r$ of the type $A_{p}B_{1-p}$. The system…

Statistical Mechanics · Physics 2017-03-08 Z. D. Vatansever , E. Vatansever

Simulation studies are undertaken for the system Ag/Au(100) by means of grand canonical Monte Carlo applied to a large lattice system. The interactions are calculated using the embedded atom model. The formation of adsorbed Ag phases of low…

Materials Science · Physics 2008-04-09 M. Cecilia Gimenez , Mario G. Del Popolo , Ezequiel P. M. Leiva

Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions. However, a major limitation of HMC is its inability to be applied to discrete domains due to the lack of gradient signal.…

Machine Learning · Computer Science 2021-03-02 Priyank Jaini , Didrik Nielsen , Max Welling

We simulate structural phase behavior of polymer-grafted colloidal particles by molecular Monte Carlo technique. Interparticle potential, which has a finite repulsive square-step outside a rigid core of the colloid, was previously confirmed…

Soft Condensed Matter · Physics 2015-03-19 Yuki Norizoe , Toshihiro Kawakatsu

Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schr\"odinger equation, Diffusion Monte Carlo,…

Quantum Physics · Physics 2017-09-07 Ilkka Ruokosenmäki , Tapio T. Rantala

The adsorption of highly charged flexible polycations and polyanions on a charged cylindrical substrate is investigated by means of Monte Carlo (MC) simulations. A detailed structural study, including monomer and fluid charge distributions,…

Soft Condensed Matter · Physics 2007-05-23 Rene Messina

We propose a new sampling algorithm combining two quite powerful ideas in the Markov chain Monte Carlo literature -- adaptive Metropolis sampler and two-stage Metropolis-Hastings sampler. The proposed sampling method will be particularly…

Computation · Statistics 2021-01-05 Anirban Mondal , Kai Yin , Abhijit Mandal

Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept--reject version with the more common sampling importance resampling…

Statistics Theory · Mathematics 2007-06-13 Hans R. Künsch
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