On the Sampling Method for Grand Canonical Monte Carlo Simulations

Abstract

A bulk Lennard-Jones fluid was simulated using the grand canonical Monte Carlo method. Three different sampling methods were used in the transition matrix, namely the Metropolis, Barker and a third novel method. While it can be shown that the Metropolis method will give the most accurate ensemble averages in the limit of an infinitely long run, the new method termed “Modified Barker Sampling” (MBS), is shown to be superior for the runs of practical length for the particular system studied.     

Estimation of Free Energy Systematic Errors in Molecular Simulations of Globular Proteins Surrounded by Finite Water Clusters. One Center Multipole Expansion of Reaction Field Differences

Free energy calculated in simulations on the atomic level (Monte Carlo or Molecular Dynamics) has a systematic error, if the water shell surrounding a globular protein is finite. The error (“cluster error”) is equal to a difference of free energies obtained in simulations with an infinite and finite water shell. In this work a continuum dielectric model was used to estimate the “cluster error”. A multipole expansion of the estimate was performed for a water shell with a spherical outer boundary. The expansion has very simple form. Each term is a product of two functions, one of them depending only on the charge's conformation, and the other one only on dielectric properties of the system. There are two practical uses of the expansion. First, it may be used to estimate the “cluster error” in a simulation already made; second, it may be used to plan a simulation in such a way that the “cluster error” is minimal. Numerical values of the largest terms in the multipole expansion corresponding to a typical system in simulations of globular proteins are given.     

Simulations on the Primitive Electrolyte Environment of a High Charge-density Polyelectrolyte. A Sampling Problem and its Solution

Abstract

We show that the classical Metropolis Monte Carlo (MMC) algorithm converges very slowly when applied to the primitive electrolyte environment for a high charge-density polyelectrolyte. This slowness of convergence, which is due to the large density inhomogeneity around the polyelectrolyte, produces noticeable errors in the ion distribution functions for MMC runs of 1.3 × 10⁶ trial steps started from nonequilibrium distributions. We report that an algorithm which we call DSMC (for density-scaled Monte Carlo) overcomes this problem and provides relatively rapid convergence in this application. We suggest that DSMC should be well-suited for other Monte Carlo simulations on physical systems where large density inhomogeneities occur.     

Test of the Inverse Monte Carlo Method for the Calculation of Interatomic Potential Energies in Atomic Liquids

Abstract

The principle purpose of this paper is to demonstrate the use of the Inverse Monte Carlo technique for calculating pair interaction energies in monoatomic liquids from a given equilibrium property. This method is based on the mathematical relation between transition probability and pair potential given by the fundamental equation of the “importance sampling” Monte Carlo method. In order to have well defined conditions for the test of the Inverse Monte Carlo method a Metropolis Monte Carlo simulation of a Lennard Jones liquid is carried out to give the equilibrium pair correlation function determined by the assumed potential. Because an equilibrium configuration is prerequisite for an Inverse Monte Carlo simulation a model system is generated reproducing the pair correlation function, which has been calculated by the Metropolis Monte Carlo simulation and therefore representing the system in thermal equilibrium. This configuration is used to simulate virtual atom displacements. The resulting changes in atom distribution for each single simulation step are inserted in a set of non-linear equations defining the transition probability for the virtual change of configuration. The solution of the set of equations for pair interaction energies yields the Lennard Jones potential by which the equilibrium configuration has been determined.     

Calculation of Vapour-Liquid Equilibria of Lennard-Jones Binary Systems by the Gibbs Ensemble Monte Carlo Simulation

Abstract

The Gibbs ensemble Monte Carlo simulation has been used to calculate vapour-liquid equilibria of a Lennard-Jones (LJ) binary mixture. The mixture studied is the LB-2-1 model which has been used in our previous calculations on PVT relation and density-dependent local composition. The P-x-y relation has been established at two different temperatures and used to determine vapour-liquid coexistence region in the PVTx space.     

Adsorption of Binary Mixtures in a Zeolite Micropore

Abstract

We investigate the selective adsorption of xenon, argon, and methane in zeolite NaA by applying the grand canonical ensemble Monte Carlo simulation technique to an adsorbed binary mixture and to two reference systems: i) an adsorbed single component system and ii) a bulk mixture. We define and calculate selectivities and excess densities due to i) mixing and ii) adsorption in terms of differences between the binary adsorbed system and these reference systems. We observe that xenon selectively adsorbs in both xenon-argon and xenon-methane mixtures at low chemical potential (low pressure) due to its greater energetic interaction with the zeolite. However, a reversal in selectivity occurs at higher chemical potential in both of these mixtures. This is due in large part to the greater efficiency in which the smaller component “packs” in the pore as compared to the bulk. We show that the crossover in selectivity occurs at a lower chemical potential for a mixture where one component can occupy regions of the porespace inaccessible to the other. We suggest that this crossover in selectivity may be a general feature of microporous adsorption.     

Monte Carlo Simulations of the Chemical Potential and Free Energy for Trimer and Hexamer Rings

Abstract

The chemical potential of a trimer and hexamer model ring system was determined by computer simulation over a range of temperatures and densities. Such ring molecules are important as model aromatic and naphthenic hydrocarbons. Thermodynamic integration of the pressure along a reversible path, Widom's ghost particle insertion method and Kirkwood's charging parameter method were used over a molecular density range of 0.05 to 0.30. Data were obtained by Monte Carlo simulation of a 96 molecule system that was modelled with a Lennard-Jones 6-12 truncated potential. The original insertion method, which does not take into account the orientation of the molecule when it is inserted, gives results for the chemical potential which deviate from that obtained using the thermodynamic pressure integration. At high density or temperature the deviation is significant. We have modified the Widom insertion technique to account for this short range orientation and find good agreement between this technique and the thermodynamic integration method for the chemical potential. We also calculated the free energy difference between our model ring molecules and ring molecules made up of hard spheres.     

The design of a peptide sequence to inhibit HIV replication: a search algorithm combining Monte Carlo and self-consistent mean field techniques

We developed a search algorithm combining Monte Carlo (MC) and self-consistent mean field techniques to evolve a peptide sequence that has good binding capability to the anticodon stem and loop (ASL) of human lysine tRNA species, tRNA^Lys3, with the ultimate purpose of breaking the replication cycle of human immunodeficiency virus-1. The starting point is the 15-amino-acid sequence, RVTHHAFLGAHRTVG, found experimentally by Agris and co-workers to bind selectively to hypermodified tRNA^Lys3. The peptide backbone conformation is determined via atomistic simulation of the peptide-ASL^Lys3 complex and then held fixed throughout the search. The proportion of amino acids of various types (hydrophobic, polar, charged, etc.) is varied to mimic different peptide hydration properties. Three different sets of hydration properties were examined in the search algorithm to see how this affects evolution to the best-binding peptide sequences. Certain amino acids are commonly found at fixed sites for all three hydration states, some necessary for binding affinity and some necessary for binding specificity. Analysis of the binding structure and the various contributions to the binding energy shows that: 1) two hydrophilic residues (asparagine at site 11 and the cysteine at site 12) “recognize” the ASL^Lys3 due to the VDW energy, and thereby contribute to its binding specificity and 2) the positively charged arginines at sites 4 and 13 preferentially attract the negatively charged sugar rings and the phosphate linkages, and thereby contribute to the binding affinity.     

Comparing vector–host and SIR models for dengue transmission

Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.