Showing papers on "Monte Carlo molecular modeling published in 2001"

Quantum Monte Carlo simulations of solids

Abstract: This article describes the variational and fixed-node diffusion quantum Monte Carlo methods and how they may be used to calculate the properties of many-electron systems. These stochastic wave-function-based approaches provide a very direct treatment of quantum many-body effects and serve as benchmarks against which other techniques may be compared. They complement the less demanding density-functional approach by providing more accurate results and a deeper understanding of the physics of electronic correlation in real materials. The algorithms are intrinsically parallel, and currently available high-performance computers allow applications to systems containing a thousand or more electrons. With these tools one can study complicated problems such as the properties of surfaces and defects, while including electron correlation effects with high precision. The authors provide a pedagogical overview of the techniques and describe a selection of applications to ground and excited states of solids and clusters.

Multilevel Monte Carlo Methods

Abstract: We study Monte Carlo approximations to high dimensional parameter dependent integrals. We survey the multilevel variance reduction technique introduced by the author in [4] and present extensions and new developments of it. The tools needed for the convergence analysis of vector-valued Monte Carlo methods are discussed, as well. Applications to stochastic solution of integral equations are given for the case where an approximation of the full solution function or a family of functionals of the solution depending on a parameter of a certain dimension is sought.

Monte Carlo Methods in Bayesian Computation

Abstract: This is an important contribution to a modern area of applied probability that deals with nonstationary Markov chains in continuous time. This area is becoming increasingly useful in engineering, economics, communication theory, active networking, and so forth, where the Markov-chain system is subject to frequent  uctuations with clusters of states such that the chain  uctuates very rapidly among different states of a cluster but changes less rapidly from one cluster to another. The authors use the setting of singular perturbations, which allows them to study both weak and strong interactions among the states of the chain. This leads to simpliŽ cations through the averaging principle, aggregation, and decomposition. The main results include asymptotic expansions of the corresponding probability distributions, occupations measures, limiting normality, and exponential rates. These results give the asymptotic behavior of many controlled stochastic dynamic systems when the perturbation parameter tends to 0. The classical analytical method employs the asymptotic expansions of onedimensional distributions of the Markov chain as solutions to a system of singularly perturbed ordinary differential equations. Indeed, the asymptotic behavior of solutions of such equations is well studied and understood. A more probabilistic approach also used by the authors is based on the tightness of the family of probability measures generated by the singularly perturbed Markov chain with the corresponding weak convergence properties. Both of these methods are illustrated by practical dynamic optimization problems, in particular by hierarchical production planning in a manufacturing system. An important contribution is the last chapter, Chapter 10, which describes numerical methods to solve various control and optimization problems involving Markov chains. Altogether the monograph consists of three parts, with Part I containing necessary, technically rather demanding facts about Markov processes (which in the nonstationary case are deŽ ned through martingales.) Part II derives the mentioned asymptotic expansions, and Part III deals with several applications, including Markov decision processes and optimal control of stochastic dynamic systems. This technically demanding book may be out of reach of many readers of Technometrics. However, the use of Markov processes has become common for numerous real-life complex stochastic systems. To understand the behavior of these systems, the sophisticated mathematical methods described in this book may be indispensable.

Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table

Abstract: We present a method for carrying out long time scale dynamics simulations within the harmonic transition state theory approximation. For each state of the system, characterized by a local minimum on the potential energy surface, multiple searches for saddle points are carried out using random initial directions. The dimer method is used for the saddle point searches and the rate for each transition mechanism is estimated using harmonic transition state theory. Transitions are selected and the clock advanced according to the kinetic Monte Carlo algorithm. Unlike traditional applications of kinetic Monte Carlo, the atoms are not assumed to sit on lattice sites and a list of all possible transitions need not be specified beforehand. Rather, the relevant transitions are found on the fly during the simulation. A multiple time scale simulation of Al(100) crystal growth is presented where the deposition event, occurring on the time scale of picoseconds, is simulated by ordinary classical dynamics, but the time i...

Monte Carlo Experiments: Design and Implementation

Abstract: The use of Monte Carlo simulations for the empirical assessment of statistical estimators is becoming more common in structural equation modeling research. Yet, there is little guidance for the researcher interested in using the technique. In this article we illustrate both the design and implementation of Monte Carlo simulations. We present 9 steps in planning and performing a Monte Carlo analysis: (1) developing a theoretically derived research question of interest, (2) creating a valid model, (3) designing specific experimental conditions, (4) choosing values of population parameters, (5) choosing an appropriate software package, (6) executing the simulations, (7) file storage, (8) troubleshooting and verification, and (9) summarizing results. Throughout the article, we use as a running example a Monte Carlo simulation that we performed to illustrate many of the relevant points with concrete information and detail.

Extended ensemble monte carlo

Abstract: "Extended Ensemble Monte Carlo" is a generic term that indicates a set of algorithms, which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo) and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here, we give a cross-disciplinary survey of these algorithms with special emphasis on the great flexibility of the underlying idea. In Sec. 2, we discuss the background of Extended Ensemble Monte Carlo. In Secs. 3, 4 and 5, three types of the algorithms, i.e., Exchange Monte Carlo, Simulated Tempering, Multicanonical Monte Carlo, are introduced. In Sec. 6, we give an introduction to Replica Monte Carlo algorithm by Swendsen and Wang. Strategies for the construction of special-purpose extended ensembles are discussed in Sec. 7. We stress that an extension is not necessary restricted to the space of energy or tempe...

Implementations of the Monte Carlo EM Algorithm

Abstract: The Monte Carlo EM (MCEM) algorithm is a modification of the EM algorithm where the expectation in the E-step is computed numerically through Monte Carlo simulations. The most exible and generally applicable approach to obtaining a Monte Carlo sample in each iteration of an MCEM algorithm is through Markov chain Monte Carlo (MCMC) routines such as the Gibbs and Metropolis–Hastings samplers. Although MCMC estimation presents a tractable solution to problems where the E-step is not available in closed form, two issues arise when implementing this MCEM routine: (1) how do we minimize the computational cost in obtaining an MCMC sample? and (2) how do we choose the Monte Carlo sample size? We address the first question through an application of importance sampling whereby samples drawn during previous EM iterations are recycled rather than running an MCMC sampler each MCEM iteration. The second question is addressed through an application of regenerative simulation. We obtain approximate independent and identi...

Precise determination of the critical percolation threshold for the three- dimensional ''Swiss cheese'' model using a growth algorithm

Abstract: Precise values for the critical threshold for the three-dimensional “Swiss cheese” continuum percolation model have been calculated using extensive Monte Carlo simulations. These simulations used a growth algorithm and memory blocking scheme similar to what we used previously in three-dimensional lattice percolation. The simulations yield a value for the critical number density nc=0.652 960±0.000 005, which confirms recent work but extends the precision by two significant figures.

Evolutionary Monte Carlo for protein folding simulations

Abstract: We demonstrate that evolutionary Monte Carlo (EMC) can be applied successfully to simulations of protein folding on simple lattice models, and to finding the ground state of a protein. In all cases, EMC is faster than the genetic algorithm and the conventional Metropolis Monte Carlo, and in several cases it finds new lower energy states. We also propose one method for the use of secondary structures in protein folding. The numerical results show that it is drastically superior to other methods in finding the ground state of a protein.

Hadronic final state predictions from CCFM: The Hadron level Monte Carlo generator CASCADE

Abstract: We discuss a practical formulation of backward evolution for the CCFM small-x evolution equation and show results from its implementation in the new Monte Carlo event-generator Cascade.

Fast Monte Carlo algorithm for supercooled soft spheres

Abstract: We present a nonlocal Monte Carlo algorithm with particle swaps that greatly accelerates thermalization of soft sphere binary mixtures in the glassy region. Our first results show that thermalization of systems of hundreds of particles is achievable, and find behavior compatible with a thermodynamic glass transition.

Chapter 56 - Computationally Intensive Methods for Integration in Econometrics*

Abstract: Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new algorithms that permit accurate Monte Carlo evaluation of high dimensional integrals, have greatly expanded the range of models that can be considered. This chapter presents the methodology for several of the most important Monte Carlo methods, supplemented by a set of concrete examples that show how the methods are used. Some of the examples are new to the econometrics literature. They include inference in multinomial discrete choice models and selection models in which the standard normality assumption is relaxed in favor of a multivariate mixture of normals assumption. Several Monte Carlo experiments indicate that these methods are successful at identifying departures from normality when they are present. Throughout the chapter the focus is on inference in parametric models that permit rich variation in the distribution of disturbances. The chapter first discusses Monte Carlo methods for the evaluation of high dimensional integrals, including integral simulators like the GHK method, and Markov Chain Monte Carlo methods like Gibbs sampling and the Metropolis–Hastings algorithm. It then turns to methods for approximating solutions to discrete choice dynamic optimization problems, including the methods developed by Keane and Wolpin, and Rust, as well as methods for circumventing the integration problem entirely, such as the approach of Geweke and Keane. The rest of the chapter deals with specific examples: classical simulation estimation for multinomial probit models, both in the cross sectional and panel data contexts; univariate and multivariate latent linear models; and Bayesian inference in dynamic discrete choice models in which the future component of the value function is replaced by a flexible polynomial.

Aggregation-volume-bias Monte Carlo simulations of vapor-liquid nucleation barriers for Lennard-Jonesium

Abstract: A combination of the aggregation-volume-bias Monte Carlo algorithm and the umbrella sampling technique is applied to investigate homogeneous vapor–liquid nucleation. This combined approach is simple, general, and robust. Its efficiency is demonstrated for nucleation of Lennard-Jonesium, for which the precise calculation of the nucleation barriers takes only a few minutes at higher supersaturations to a few hours at lower supersaturations. Comparison of the simulation results to the classical nucleation theory (CNT) shows that CNT overestimates the barrier heights by a value nearly independent of the supersaturation, but provides a reasonable description of the critical cluster sizes.

Monte Carlo simulations of spin glasses at low temperatures

Abstract: We report the results of Monte Carlo simulations on several spin-glass models at low temperatures. By using the parallel tempering (exchange Monte Carlo) technique we are able to equilibrate down to low temperatures, for moderate sizes, and hence the data should not be affected by critical fluctuations. Our results for short-range models are consistent with a picture proposed earlier that there are large-scale excitations which cost only a finite energy in the thermodynamic limit, and these excitations have a surface whose fractal dimension is less than the space dimension. For the infinite range Viana-Bray model, our results obtained for a similar number of spins, are consistent with standard replica symmetry breaking.

A Brief Introduction to Monte Carlo Simulation

Abstract: Simulation affects our life every day through our interactions with the automobile, airline and entertainment industries, just to name a few The use of simulation in drug development is relatively new, but its use is increasing in relation to the speed at which modern computers run One well known example of simulation in drug development is molecular modelling Another use of simulation that is being seen recently in drug development is Monte Carlo simulation of clinical trials Monte Carlo simulation differs from traditional simulation in that the model parameters are treated as stochastic or random variables, rather than as fixed values The purpose of this paper is to provide a brief introduction to Monte Carlo simulation methods

Improving the Efficiency of the Aggregation−Volume−Bias Monte Carlo Algorithm

Abstract: The aggregation−volume−bias Monte Carlo (AVBMC) algorithm is reanalyzed, and on the basis of this analysis, two extensions of the AVBMC algorithm with improved sampling efficiency for super-strongly associating fluids are presented. The new versions of the AVBMC algorithm are based on the principle of super-detailed balance and retain the simplicity, generality, and robustness of the original AVBMC algorithm. The performances of the various versions of the AVBMC algorithm are compared via applications to the simple ideal-association model of van Roij and to the superheated vapor phase of hydrogen fluoride.

Optimization of Ground- and Excited-State Wave Functions and van der Waals Clusters

Abstract: A quantum Monte Carlo method is introduced to optimize excited-state trial wave functions. The method is applied in a correlation function Monte Carlo calculation to compute ground- and excited-state energies of bosonic van der Waals clusters of up to seven particles. The calculations are performed using trial wave functions with general three-body correlations.

Monte Carlo update for chain molecules: Biased Gaussian steps in torsional space

Abstract: We develop a new elementary move for simulations of polymer chains in torsion angle space. The method is flexible and easy to implement. Tentative updates are drawn from a (conformation-dependent) Gaussian distribution that favors approximately local deformations of the chain. The degree of bias is controlled by a parameter b. The method is tested on a reduced model protein with 54 amino acids and the Ramachandran torsion angles as its only degrees of freedom, for different b. Without excessive fine tuning, we find that the effective step size can be increased by a factor of 3 compared to the unbiased b=0 case. The method may be useful for kinetic studies, too.

Population Monte Carlo algorithms

Abstract: We give a cross-disciplinary survey on “population” Monte Carlo algorithms.In these algorithms, a set of “walkers” or “particles” is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms — “quantum Monte Carlo”, “transfer-matrix Monte Carlo”, “Monte Carlo filter (particle filter)”, “sequential Monte Carlo” and “PERM” etc. Here we discuss them in a coherent framework. We also touch on related algorithms —genetic algorithms and annealed importance sampling.

Time Relaxed Monte Carlo Methods for the Boltzmann Equation

Abstract: A new family of Monte Carlo schemes is introduced for the numerical solution of the Boltzmann equation of rarefied gas dynamics. The schemes are inspired by the Wild sum expansion of the solution of the Boltzmann equation for Maxwellian molecules and consist of a novel time discretization of the equation. In particular, high order terms in the expansion are replaced by the equilibrium Maxwellian distribution. The two main features of the schemes are high order accuracy in time and asymptotic preservation. The first property allows to recover accurate solutions with time steps larger than those required by direct simulation Monte Carlo (DSMC), while the latter guarantees that for the vanishing Knudsen number, the numerical solution relaxes to the local Maxwellian. Conservation of mass, momentum, and energy are preserved by the scheme. Numerical results on several space homogeneous problems show the improvement of the new schemes over standard DSMC. Applications to a one-dimensional shock wave problem are also presented.

Monte Carlo Smoothing and Self-Organising State-Space Model

Abstract: A Monte Carlo method for nonlinear non-Gaussian filtering and smoothing and its application to self-organising state-space models are shown in this paper.