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.

A Novel Monte Carlo Scheme for the Rapid Equilibration of Atomistic Model Polymer Systems of Precisely Defined Molecular Architecture

Abstract: Two novel connectivity-altering atomistic Monte Carlo moves are presented for the fast equilibration of condensed phases of long-chain systems with a variety of chain architectures. With the new moves, isotropic or oriented melts of linear or long-chain branched polymers, dense brushes of terminally grafted macromolecules, and cyclic peptides can be simulated. Results concerning the structural, conformational, and volumetric properties of linear, monodisperse polyethylene melts, simulated with a new united-atom molecular model, are in excellent agreement with experimental data.

Quantum Monte Carlo for molecules: Green’s function and nodal release

Abstract: A random walk algorithm is presented which exactly calculates the properties of a many‐electron system. For that purpose both the Green’s function Monte Carlo method and nodal relaxation have been employed and both are described in detail. The scheme is applied to several small molecules, (H3, LiH, Li2, H20) and with modest computational effort and simple importance functions, ground state energies are obtained which agree with experimental energies within statistical error bars. The small energy decrease due to nodal release is accurately evaluated by a difference method.

Monte Carlo Simulation of Gas Flows

Abstract: It is almost certain that the first Monte Carlo simulation of a gas was carried out by William Anderson, the secretary and assistant to Lord Kelvin. As reported by Kelvin (1901), Anderson generated random numbers by shuffling decks of numbered cards and calculated· with "unfailingly faithful perseverance" a total of five thousand molecular impacts with surfaces and three hundred intermolecular collisions. The use of random numbers is the distinguishing feature of a Monte Carlo procedure, and the essentially probabilistic nature of a gas flow at the molecular level makes it an obvious subject for a simulation approach based directly on the physics of the individual molecular interactions. However, prior to the advent. of the digital computer, the approach was effectively ruled out by the enormous number of repetitive arithmetical computations that are required for its application, even to the simplest problem. Typical computer runs of Monte Carlo simulation programs now involve the computation of as many as ten million intermolecular collisions, together with millions of molecule-surface interactions. The molecular or microscopic model of a gas flow must, of course, be viewed against the familiar macroscopic or continuum model. This requires the introduction of the Knudsen number Kn as a distinct dimensionless parameter. The usual definition is

A deterministic alternative to the full configuration interaction quantum Monte Carlo method

Abstract: Development of exponentially scaling methods has seen great progress in tackling larger systems than previously thought possible. One such technique, full configuration interaction quantum Monte Carlo, is a useful algorithm that allows exact diagonalization through stochastically sampling determinants. The method derives its utility from the information in the matrix elements of the Hamiltonian, along with a stochastic projected wave function, to find the important parts of Hilbert space. However, the stochastic representation of the wave function is not required to search Hilbert space efficiently, and here we describe a highly efficient deterministic method that can achieve chemical accuracy for a wide range of systems, including the difficult Cr2 molecule. We demonstrate for systems like Cr2 that such calculations can be performed in just a few cpu hours which makes it one of the most efficient and accurate methods that can attain chemical accuracy for strongly correlated systems. In addition our method also allows efficient calculation of excited state energies, which we illustrate with benchmark results for the excited states of C2.