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Dynamic Monte Carlo method
About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.
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TL;DR: This work proposes a method which allows the parallel generation of MC moves, and which is especially useful for simulations with unavoidably low acceptance rates, such as for long chain molecules.
Abstract: The Monte Carlo (MC) method is an important tool in sampling the state space of a chosen statistical ensemble. It allows the study of thermodynamic averages of configurational properties by generating ``moves'' in a system and accepting or rejecting the thus generated new state depending on the energy of the new system and/or a random choice. These moves are intrinsically sequential and complicate parallel implementation. We propose a method which allows the parallel generation of MC moves, and which is especially useful for simulations with unavoidably low acceptance rates, such as for long chain molecules.
115 citations
TL;DR: An off-lattice Monte Carlo calculation of the equilibrium properties of a monodisperse polymer brush in a good solvent finds that the density profile is in agreement with the results of self-consistent field theory.
Abstract: We report an off-lattice Monte Carlo calculation of the equilibrium properties of a monodisperse polymer brush in a good solvent. We find that the density profile, in general, is in agreement with the results of self-consistent field theory, with some discrepancies observed near the wall and at the tail of the profile. Other quantities, such as the probability distribution of monomers, the average bond orientation, and the relative mean square displacement of monomers, are also compared with the results of the self-consistent field theory.
115 citations
TL;DR: There are many kinetic Monte Carlo approaches that can simulate chemical vapor deposition, ranging from coarse-grained model systems with hypothetical input parameters to physically realistic atomic simulations with accurate chemical kinetic input.
Abstract: ▪ Abstract The kinetic Monte Carlo method is a powerful tool for exploring the evolution and properties of a wide range of problems and systems. Kinetic Monte Carlo is ideally suited for modeling the process of chemical vapor deposition, which involves the adsorption, desorption, evolution, and incorporation of vapor species at the surface of a growing film. Deposition occurs on a time scale that is generally not accessible to fully atomistic approaches such as molecular dynamics, whereas an atomically resolved Monte Carlo method parameterized by accurate chemical kinetic data is capable of exploring deposition over long times (min) on large surfaces (mm2). There are many kinetic Monte Carlo approaches that can simulate chemical vapor deposition, ranging from coarse-grained model systems with hypothetical input parameters to physically realistic atomic simulations with accurate chemical kinetic input. This article introduces the kinetic Monte Carlo technique, reviews some of the major approaches, details ...
115 citations
TL;DR: In this article, the determinantal quantum Monte Carlo method for fermionic systems is reviewed, using the Hubbard model as a case study and the Green's function is used in the updating process.
Abstract: We tutorially review the determinantal Quantum Monte Carlo method for fermionic systems, using the Hubbard model as a case study. Starting with the basic ingredients of Monte Carlo simulations for classical systems, we introduce aspects such as importance sampling, sources of errors, and finite-size scaling analyses. We then set up the preliminary steps to prepare for the simulations, showing that they are actually carried out by sampling discrete Hubbard-Stratonovich auxiliary fields. In this process the Green’s function emerges as a fundamental tool, since it is used in the updating process, and, at the same time, it is directly related to the quantities probing magnetic, charge, metallic, and superconducting behaviours. We also discuss the as yet unresolved ‘minus-sign problem’, and two ways to stabilize the algorithm at low temperatures.
115 citations
TL;DR: In this article, the equilibrium between vapour and liquid in a square-well system has been determined by a hybrid simulation approach combining chemical potentials calculated via the Gibbs ensemble Monte Carlo technique with pressures calculated by the standard NVT Monte Carlo method.
Abstract: The equilibrium between vapour and liquid in a square-well system has been determined by a hybrid simulation approach combining chemical potentials calculated via the Gibbs ensemble Monte Carlo technique with pressures calculated by the standard NVT Monte Carlo method. The phase equilibrium was determined from the thermodynamic conditions of equality of pressure and chemical potential between the two phases. The results of this hybrid approach were tested by independent NPT and μPT calculations and are shown to be of much higher accuracy than those of conventional GEMC simulations. The coexistence curves, vapour pressures and critical points were determined for SW systems of interaction ranges λ = 1.25, 1.5, 1.75 and 2. The new results show a systematic dependence on the range λ, in agreement with results from perturbation theory where previous work had shown more erratic behaviour.
115 citations