Accelerating molecular simulations by reversible mapping between local minima

A new framework is presented for performing Monte Carlo simulations of condensed matter based on a recently developed bijective mapping between local energy minima. The framework is used to implement a range of new multiparticle Monte Carlo moves, which are investigated by simulating atomic Lennard-Jones fluids in the canonical and grand canonical ensembles. Important aspects of the simulation protocol and their effect on performance are analyzed in detail. Using the mapping accelerates the simulations by many orders of magnitude when compared to the equivalent moves without the mapping, and leads to particularly efficient configurational sampling at low temperatures and high densities. The method appears to be suitable for adapting to quantitative simulations of more complex molecular systems over long effective time scales.     

Free energies and phase equilibria of chain molecules

In this Article, a review is presented of recent developments in Monte Carlo simulations of chain molecules. The Rosenbluth chain insertion technique is used to calculate the free energy of the chain molecules. Furthermore, this insertion method is used to generate biased Monte Carlo moves. It is shown that this bias can be removed by adjusting the acceptance rules such that configurations are generated with their correct Boltzmann weight. This configurational-bias Monte Carlo method can be combined with the Gibbs-ensemble technique which results in an efficient method to simulate phase equilibria of chain molecules.     

Monte Carlo simulation of polarizable systems: Early rejection scheme for improving the performance of adiabatic nuclear and electronic sampling Monte Carlo simulations

An early rejection scheme for trial moves in adiabatic nuclear and electronic sampling Monte Carlo simulation (ANES-MC) of polarizable intermolecular potential models is presented. The proposed algorithm is based on Swendsen–Wang filter functions for prediction of success or failure of trial moves in Monte Carlo simulations. The goal was to reduce the amount of calculations involved in ANES-MC electronic moves, by foreseeing the success of an attempt before making those moves. The new method was employed in Gibbs ensemble Monte Carlo (GEMC) simulations of the polarizable simple point charge-fluctuating charge (SPC-FQ) model of water. The overall improvement in GEMC depends on the number of swap attempts (transfer molecules between phases) in one Monte Carlo cycle. The proposed method allows this number to increase, enhancing the chemical potential equalization. For a system with 300 SPC-FQ water molecules, for example, the fractions of early rejected transfers were about 0.9998 and 0.9994 at 373 and 423 K, respectively. This means that the transfer moves consume only a very small part of the overall computing effort, making GEMC almost equivalent to a simulation in the canonical ensemble.     

Multiparticle moves in acceptance rate optimized monte carlo

Molecular Dynamics (MD) and Monte Carlo (MC) based simulation methods are widely used to investigate molecular and nanoscale structures and processes. While the investigation of systems in MD simulations is limited by very small time steps, MC methods are often stifled by low acceptance rates for moves that significantly perturb the system. In many Metropolis MC methods with hard potentials, the acceptance rate drops exponentially with the number of uncorrelated, simultaneously proposed moves. In this work, we discuss a multiparticle Acceptance Rate Optimized Monte Carlo approach (AROMoCa) to construct collective moves with near unit acceptance probability, while preserving detailed balance even for large step sizes. After an illustration of the protocol, we demonstrate that AROMoCa significantly accelerates MC simulations in four model systems in comparison to standard MC methods. AROMoCa can be applied to all MC simulations where a gradient of the potential is available and can help to significantly speed up molecular simulations. © 2015 Wiley Periodicals, Inc.     

Mode excitation Monte Carlo simulations of mesoscopically large membranes

Solvent-free coarse grained models represent one of the most promising approaches for molecular simulations of mesoscopically large membranes. In these models, the size of the simulated membrane is limited by the slow relaxation time of the longest bending mode. Here, we present a Monte Carlo algorithm with update moves in which all the lipids are simultaneously displaced. These collective moves result in fast excitation and relaxation of the long wavelength thermal fluctuations. We apply the method to simulations of a bilayer membrane with linear size of approximately 50 nm and show reduction in the relaxation time by two orders of magnitudes when compared to conventional Monte Carlo.     

Using internal and collective variables in Monte Carlo simulations of nucleic acid structures: chain breakage/closure algorithm and associated Jacobians

This article describes a method for solving the geometric closure problem for simplified models of nucleic acid structures by using the constant bond lengths approximation. The resulting chain breakage/closure equations, formulated in the space of variable torsion and bond angles, are easy to solve, and have only two solutions. The analytical simplicity is in contrast with the high complexity of the closure problem in the torsion angle space with at most 16 solutions, which has been dealt with by several authors and was solved analytically by Wu and Deem (J. Chem. Phys. 1999, 111, 6625). The discussion on the choice of variables and associated Jacobians is focussed on the question of how conformational equilibration is affected in Monte Carlo simulations of molecular systems. In addition to the closure of the phosphate backbone, it is necessary to also solve the closure problem for the five-membered flexible furanose sugar ring. Explicit closure equations and the resulting Jacobians are given both for the complete four-variable model of the furanose ring and simulations in the phase-amplitude space of the five-membered ring, which are based on the approximate two-variable model of furanose introduced by Gabb et al. (J. Comput. Chem. 1995, 16, 667). The suggested closure algorithm can be combined with collective variables defined by translations and rotations of the monomeric nucleotide units. In comparison with simple internal coordinate moves, the resulting concerted moves describe local structural changes that have high acceptance rates and enable fast conformational equilibration. Appropriate molecular models are put forward for prospective Monte Carlo simulations of nucleic acids, but can be easily adapted to other biomolecular systems, such as proteins and lipid structures in biological membranes.     

Improvement in molecule exchange efficiency in Gibbs ensemble Monte Carlo: development and implementation of the continuous fractional component move

The continuous fractional component Monte Carlo (CFC MC) move (J Chem Theory Comput, 2007, 3, 1451) is extended to the Gibbs ensemble. The algorithm is validated against conventional simulations for the Lennard Jones fluid and a flexible water model. The method is also used to compute the vapor-liquid coexistence densities of a model for SO(2). The CFC molecule exchange move relies on the gradual insertion and deletion of molecules in conjunction with a self-adapting bias potential. As a result, the method does not require the formation of spontaneous voids in the dense fluid phase to be successful, leading to molecule exchange acceptance probabilities that are nearly independent of temperature. For example, over 1% of the vapor-liquid molecule exchange moves are successful for water at 280 K, whereas advanced rotational and configurational bias methods have success rates of less than 0.03%. The CFC move can be combined with other Monte Carlo moves to enable efficient simulation of dense strongly associating fluids that are to this point infeasible to model with standard methods.     

A rare event sampling method for diffusion Monte Carlo using smart darting

We identify a set of multidimensional potential energy surfaces sufficiently complex to cause both the classical parallel tempering and the guided or unguided diffusion Monte Carlo methods to converge too inefficiently for practical applications. The mathematical model is constructed as a linear combination of decoupled Double Wells [(DDW)(n)]. We show that the set (DDW)(n) provides a serious test for new methods aimed at addressing rare event sampling in stochastic simulations. Unlike the typical numerical tests used in these cases, the thermodynamics and the quantum dynamics for (DDW)(n) can be solved deterministically. We use the potential energy set (DDW)(n) to explore and identify methods that can enhance the diffusion Monte Carlo algorithm. We demonstrate that the smart darting method succeeds at reducing quasiergodicity for n ? 100 using just 1 × 10(6) moves in classical simulations (DDW)(n). Finally, we prove that smart darting, when incorporated into the regular or the guided diffusion Monte Carlo algorithm, drastically improves its convergence. The new method promises to significantly extend the range of systems computationally tractable by the diffusion Monte Carlo algorithm.     

Monte Carlo simulation of freely jointed chains with variable bond lengths

Monte Carlo simulation of freely jointed off-lattice chains with variable bond length is usually done with local random displacements of beads and with reptation moves (displacements of a bead along a chain). In dense systems, the acceptance ratio of reptations decreases strongly with density. We discuss versions of reptation moves, which are effective in dense systems. The idea, which comes from lattice systems, is to use a pseudovacancy (walker), which has the same size as a bead of a chain. The walker is attached to a neighbor chain and then another bead of that chain is cleaved. This is equivalent to a reptation move and a nonlocal displacement of the walker and since no free volume is needed, the move can be used with advantage in dense systems. A related technique are cooperative motions, which were introduced by T. Pakula for lattice models, where several chains change their conformation concomitantly. Such cooperative loops are implemented in the Monte Carlo algorithm by creating a temporary walker by cleaving a bead from a chain, moving it with reptations and finally annihilating the walker by attaching it to the same chain it was cleaved from. These moves and the condition of detailed balance are discussed in detail. As an example, we study the integrated autocorrelation time τ_int for the radius of gyration for a two-dimensional system. For reduced densities larger than 0,4, we find that with standard reptations and local bead displacements τ_int increases strongly with density. If reptations with either a permanent or a temporary walker are used in addition to local moves, the integrated autocorrelation time changes only very little with density and very dense systems can still be simulated efficiently.     

Hybrid Monte Carlo implementation of the Fourier path integral algorithm

This paper formulates a hybrid Monte Carlo implementation of the Fourier path integral (FPI-HMC) approach with partial averaging. Such a hybrid Monte Carlo approach allows one to generate collective moves through configuration space using molecular dynamics while retaining the computational advantages associated with the Fourier path integral Monte Carlo method. In comparison with the earlier Metropolis Monte Carlo implementations of the FPI algorithm, the present HMC method is shown to be significantly more efficient for quantum Lennard-Jones solids and suggests that such algorithms may prove useful for efficient simulations of a range of atomic and molecular systems.     

Novel Monte Carlo scheme for systems with short-ranged interactions

We propose a Monte Carlo (MC) sampling algorithm to simulate systems of particles interacting via very short-ranged discontinuous potentials. Such models are often used to describe protein solutions or colloidal suspensions. Most normal MC algorithms fail for such systems because, at low temperatures, they tend to get trapped in local potential-energy local minima due to the short range of the pair potential. To circumvent this problem, we have devised a scheme that changes the construction of trial moves in such a way that the potential-energy difference between initial and final states drops out of the acceptance rule for the Monte Carlo trial moves. This approach allows us to simulate systems with short-ranged attraction under conditions that were unreachable up to now.     

Density distributions in the water shell of myoglobin

Two different approaches were used to explain the distribution of water coordinates in the crystallographically invisible part of the unit cell. Monte Carlo calculations were done starting from different initial water structures. Extended Monte Carlo calculations using equal initial structures were also used to obtain two further water structures. The differences between the Monte Carlo water structures were used to calculate the mean-square displacements of the water molecules. Monte Carlo calculations starting from different structures lead to a mean-square displacement of 0.58 Ų, whereas extended Monte Carlo moves using the same initial structure only show a mean-square displacement of 0.17 Ų. The mean-square displacement of 0.58 Ų can be used to explain the experimental data. © 1996 John Wiley & Sons, Inc.     

Cluster algorithm to perform parallel Monte Carlo simulation of atomistic systems

We propose an efficient algorithm to perform Monte Carlo simulations of dense systems using multiple particle moves. The method is intended to be used in the atomistic simulation of complex systems, where the computing requirements for a single simulation run make advisable the use of parallel computing. The algorithm is based on the use of steps in which all the particle positions of the system are perturbed simultaneously. A division of the system in clusters of particles is performed, using a bonding criterion which makes feasible that the acceptance or rejection of the new particle coordinates can be carried out independently for each cluster.     

Adsorption of liquid-phase alkane mixtures in silicalite: simulations and experiment

A combination of experimental and computational studies of adsorption from liquid-phase mixtures of linear alkanes in the zeolite silicalite is presented here. Configurational biased grand canonical Monte Carlo simulations combined with identity-swap moves are used to equilibrate the simulations in reasonable times. Interesting trends observed in experiments have been captured quantitatively by simulations. A siting analysis of the simulation data reveals that, during adsorption from a liquid mixture, shorter alkanes prefer the zigzag channels and longer alkanes concentrate in the straight channels of silicalite.     

Light Distributions from Point, Line and Plane Sources for Photochemical Reactions and Fluorescence in Turbid Biological Tissues

Light distributions in biological tissues are summarized in simple expressions for sphewrical, cylindrical and planar geometries due to point sources, line sources and planar sources. The goal is to provide workable tools for computing light distributions that govern the amount and distribution of photochemical reactions in experimental solutions, films and biological tissues. Diffusion theory expressions are compared with Monte Carlo simulations. Analytic expressions that mimic accurate Monte Carlo simulations are presented. Application to fluorescence measurements and prediction of necrotic zones in photodynamic therapy are outlined.     

Efficient combination of Wang-Landau and transition matrix Monte Carlo methods for protein simulations

An efficient combination of the Wang-Landau and transition matrix Monte Carlo methods for protein and peptide simulations is described. At the initial stage of simulation the algorithm behaves like the Wang-Landau algorithm, allowing to sample the entire interval of energies, and at the later stages, it behaves like transition matrix Monte Carlo method and has significantly lower statistical errors. This combination allows to achieve fast convergence to the correct values of density of states. We propose that the violation of TTT identities may serve as a qualitative criterion to check the convergence of density of states. The simulation process can be parallelized by cutting the entire interval of simulation into subintervals. The violation of ergodicity in this case is discussed. We test the algorithm on a set of peptides of different lengths and observe good statistical convergent properties for the density of states. We believe that the method is of general nature and can be used for simulations of other systems with either discrete or continuous energy spectrum.     

Theoretical and computational investigations on thermodynamic properties, effective site diameters, and molecular free volume of carbon disulfide fluid

A newly proposed theory [R. Laghaei et al., J. Chem. Phys. 124, 154502 (2006)] was extended to polyatomics and applied to compute the density and temperature dependence of the effective site diameters of carbon disulfide fluids. The generic van der Waals (GvdW) theory was also extended to polyatomics in order to calculate the GvdW parameters and the molecular free volume using the effective site diameters as the repulsion-attraction separation distance. A three-site Lennard-Jones potential available in the literature was slightly modified and used in Monte Carlo simulations to obtain the functions appearing in the effective site diameter and GvdW expressions. The interaction potential was examined to reproduce the fluid phase thermodynamic properties using Gibbs ensemble Monte Carlo simulations and also the equation of state in the liquid phase using NVT Monte Carlo (NVT-MC) simulations. Comparison between the simulation results and experimental data shows excellent agreement for the densities of the coexisting phases, the vapor pressure, properties of the predicted critical point, and the equation of state. NVT-MC simulations were performed over a wide range of densities and temperatures in sub- and supercritical regions to compute the effective site diameters, the GvdW parameters, and the molecular free volume. The molecular structure in terms of the site-site pair correlation functions, the density dependence of the effective site diameters, and the density and temperature dependence of the GvdW parameters and molecular free volume were studied and discussed. The GvdW parameters were fitted to empirical expressions as a function of density and temperature. The computed molecular free volume will be used in future investigations to study the transport properties of carbon disulfide.     

Optimization of Monte Carlo trial moves for protein simulations

Closed rigid-body rotations of residue segments under bond-angle restraints are simple and effective Monte Carlo moves for searching the conformational space of proteins. The efficiency of these moves is examined here as a function of the number of moving residues and the magnitude of their displacement. It is found that the efficiency of folding and equilibrium simulations can be significantly improved by tailoring the distribution of the number of moving residues to the simulation temperature. In general, simulations exploring compact conformations are more efficient when the average number of moving residues is smaller. It is also demonstrated that the moves do not require additional restrictions on the magnitude of the rotation displacements and perform much better than other rotation moves that do not restrict the bond angles a priori. As an example, these results are applied to the replica exchange method. By assigning distributions that generate a smaller number of moving residues to lower temperature replicas, the simulation times are decreased as long as the higher temperature replicas are effective.     

Monte Carlo simulations for predicting the precision of results and for optimizing data acquisition schedules

Monte Carlo simulations can be used to determine the precision of an analytical method if the standard deviations of the component unit operations are estimated accurately. Alternative methods for estimating the standard deviation have been compared by evaluating the success of Monte Carlo simulations to predict the precision of a second-order rate constant determined by spectrophotometry and of an equivalent weight and acid dissociation constant determined by potentiometry. Monte Carlo simulation has also been used with simplex optimization to predict a data acquisition schedule which gives high precision in the equivalent weight determination. By comparison with a naive design, a 22-fold improvement was predicted. A 15-fold improvement was observed experimentally.     

Collective-variable Monte Carlo simulation of DNA

Monte Carlo simulations have been carried out on DNA oligomers using an internal coordinate model associated with a pseudorotational representation of sugar repuckering. It is shown that when this model is combined with the scaled collective variable approach of Noguti and Go, much more efficient simulations are obtained than with simple single variable steps. Application of this method to a DNA oligomer containing a recognition site for the TATA-box binding protein leads to striking similarities with results recently obtained from a 1-ns molecular dynamics simulation using explicit solvent and counterions. In particular, large amplitude bending fluctuations are observed directed toward the major groove. Conformational analysis of the Monte Carlo simulation shows clear base sequence effects on conformational fluctuations and also that the DNA energy hypersurface, like that of proteins, is complex with many local, conformational substates. © 1997 John Wiley & Sons, Inc. J Comput Chem 18 : 2001–2011, 1997