Monte Carlo simulations of biomolecules: The MC module in CHARMM

We describe the implementation of a general and flexible Monte Carlo (MC) module for the program CHARMM, which is used widely for modeling biomolecular systems with empirical energy functions. Construction and use of an almost arbitrary move set with only a few commands is made possible by providing several predefined types of moves that can be combined. Sampling can be enhanced by noncanonical acceptance criteria, automatic optimization of step sizes, and energy minimization. A systematic procedure for improving MC move sets is introduced and applied to simulations of two peptides. The resulting move sets allow MC to sample the configuration spaces of these systems much more rapidly than Langevin dynamics. The rate of convergence of the difference in free energy between ethane and methanol in explicit solvent is also examined, and comparable performances are observed for MC and the Nosé-Hoover algorithm. Its ease of use combined with its sampling efficiency make the MC module in CHARMM an attractive alternative for exploring the behavior of biomolecular 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.     

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.     

Avoiding unphysical kinetic traps in Monte Carlo simulations of strongly attractive particles

We introduce a "virtual-move" Monte Carlo algorithm for systems of pairwise-interacting particles. This algorithm facilitates the simulation of particles possessing attractions of short range and arbitrary strength and geometry, an important realization being self-assembling particles endowed with strong, short-ranged, and angularly specific ("patchy") attractions. Standard Monte Carlo techniques employ sequential updates of particles and can suffer from low acceptance rates when attractions are strong. In this event, collective motion can be strongly suppressed. Our algorithm avoids this problem by proposing simultaneous moves of collections (clusters) of particles according to gradients of interaction energies. One particle first executes a "virtual" trial move. We determine which of its neighbors move in a similar fashion by calculating individual bond energies before and after the proposed move. We iterate this procedure and update simultaneously the positions of all affected particles. Particles move according to an approximation of realistic dynamics without requiring the explicit computation of forces and without the step size restrictions required when integrating equations of motion. We employ a size- and shape-dependent damping of cluster movements, motivated by collective hydrodynamic effects neglected in simple implementations of Brownian dynamics. We discuss the virtual-move algorithm in the context of other Monte Carlo cluster-move schemes and demonstrate its utility by applying it to a model of biological self-assembly.     

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.     

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.     

Approaching chemical accuracy with quantum Monte Carlo

A quantum Monte Carlo study of the atomization energies for the G2 set of molecules is presented. Basis size dependence of diffusion Monte Carlo atomization energies is studied with a single determinant Slater-Jastrow trial wavefunction formed from Hartree-Fock orbitals. With the largest basis set, the mean absolute deviation from experimental atomization energies for the G2 set is 3.0 kcal/mol. Optimizing the orbitals within variational Monte Carlo improves the agreement between diffusion Monte Carlo and experiment, reducing the mean absolute deviation to 2.1 kcal/mol. Moving beyond a single determinant Slater-Jastrow trial wavefunction, diffusion Monte Carlo with a small complete active space Slater-Jastrow trial wavefunction results in near chemical accuracy. In this case, the mean absolute deviation from experimental atomization energies is 1.2 kcal/mol. It is shown from calculations on systems containing phosphorus that the accuracy can be further improved by employing a larger active space.     

Nonequilibrium Monte Carlo simulation of lattice block copolymer chains subject to oscillatory shear flow

This paper has extended nonequilibrium Monte Carlo (MC) approach to simulate oscillatory shear flow in a lattice block copolymer system. Phase transition and associated rheological behaviors of multiple self-avoiding chains have been investigated. Stress tensor has been obtained based upon sampled configuration distribution functions. At low temperatures, micellar structures have been observed and the underlying frequency-dependent rheological properties exhibit different initial slopes. The simulation outputs are consistent with the experimental observations in literature. Chain deformation during oscillatory shear flow has also been revealed. Although MC simulation cannot account for hydrodynamic interaction, the highlight of our simulation approach is that it can, at small computing cost, investigate polymer chains simultaneously at different spatial scales, i.e., macroscopic rheological behaviors, mesoscopic self-assembled structures, and microscopic chain configurations.     

Concurrent dual-resolution Monte Carlo simulation of liquid methane

We conduct molecular simulations of liquid methane in a system where molecular resolution fluctuates between atomically explicit and spherically symmetric united atoms. An appropriate dual-resolution canonical ensemble is constructed using (a) effective united atom pair potentials and (b) resolution-control potentials that confine explicit and united atoms chiefly to different slabs in the simulation domain. A Monte Carlo simulation is developed to sample this ensemble. We show that compatibility of the united-atom potentials with the explicit potentials in a concurrent simulation can be tuned by adjusting the width of the interface between the two resolution regions and by direct modification of the united-atom pair potentials. Our results lay the groundwork for treatment of larger atomically specific molecules with similar concurrent multiresolution techniques.     

MOCASSIN: Metropolis and kinetic Monte Carlo for solid electrolytes

The combination of density functional theory and Monte Carlo simulations is a powerful approach for the atomistic modeling of defect transport in solid electrolytes. The present contribution introduces the MOCASSIN software (Monte Carlo for Solid State Ionics) for kinetic and Metropolis Monte Carlo simulations of crystalline materials. MOCASSIN combines model building, visualization, and simulation, aiming to provide accessible MC for end users. Developed for the investigation of solid electrolytes, MOCASSIN is ideal for screening common variation parameters, such as temperature and doping fraction. The input effort is minimized using space groups for processing symmetry. The graphical interface for model building allows complex model input, including multiple mobile species, multiple migration paths, small polaron hopping, vehicle movements, multiple complex migration mechanisms, and custom interaction clusters. The software is provided free of charge for noncommercial usage.     

A rapidly convergent simulation method: Mixed Monte Carlo/stochastic dynamics

Although Monte Carlo and molecular dynamics are the primary methods used for free energy simulations of molecular systems, their application to molecules that have multiple conformations separated by energy barriers of ≥ 3 kcal/mol is problematic because of slow rates of convergence. In this article we introduce a hybrid simulation method termed MC-SD which mixes Monte Carlo (MC) and stochastic dynamics (SD). This new method generates a canonical ensemble via alternating MC and SD steps and combines the local exploration strengths of dynamics with the barrier-crossing ability of large-step Monte Carlo. Using calculations on double-well potentials and long simulations (10⁸ steps of MC and 1 μs of SD) of the simple, conformationally flexible molecule n-pentane, we find that MC-SD simulations converage faster than either MC or SD alone and generate ensembles which are equivalent to those created by classical MC or SD. Using pure SD at 300 K, the conformational populations of n-pentane are shown to be poorly converged even after a full microsecond of simulation. © 1994 by John Wiley & Sons, Inc.     

Delayed rejection variational Monte Carlo

An acceleration algorithm to address the problem of multiple time scales in variational Monte Carlo simulations is presented. After a first attempted move has been rejected, the delayed rejection algorithm attempts a second move with a smaller time step, so that even moves of the core electrons can be accepted. Results on Be and Ne atoms as test cases are presented. Correlation time and both average accepted displacement and acceptance ratio as a function of the distance from the nucleus evidence the efficiency of the proposed algorithm in dealing with the multiple time scales problem.     

Random End‐Switching Configurational Bias Monte Carlo for Long Chain Molecules

Summary: The exponential attrition of configurational bias Monte Carlo for long chains can be reduced to almost quadratic by a simple modification of the basic move. Each trial move begins on the side of the remaining sub chain opposite to the cut location. This type of move is akin to large‐scale reptation and in the limit of one‐segment cut is reptation. The extension is shown to require the same Rosenbluth weighting scheme as the original algorithm. Several examples are used to demonstrate the reliability and the improved performance of the proposed method.

     

Combining reactive and configurational-bias Monte Carlo: confinement influence on the propene metathesis reaction system in various zeolites

In order to efficiently calculate chemical equilibria of large molecules in a confined environment the reactive Monte Carlo technique is combined with the configurational-bias Monte Carlo approach. To prove that detailed balance is fulfilled the acceptance rule for this combination of particular Monte Carlo techniques is derived in detail. Notably, by using this derivation all other acceptance rules of any Monte Carlo trial moves usually carried out in combination with the configurational-bias Monte Carlo approach can be deduced from it. As an application of the combination of reactive and configurational-bias Monte Carlo the influence of different zeolitic confinements (MFI, TON, LTL, and FER) on the reaction equilibrium and the selectivity of the propene metathesis reaction system was investigated. Compared to the bulk phase the conversion is increased significantly. The authors study this reaction system in the temperature range between 300 and 600 K, and the pressure range from 1 to 7 bars. In contrast to the bulk phase, pressure and temperature have a strong influence on the composition of the reaction mixture in confinement. At low pressures and temperatures both conversion and selectivity are highest. Furthermore, the equilibrium composition is strongly dependent on the type of zeolite. This demonstrates the important role of the host structure in catalytic systems.     

Cool walking: a new Markov chain Monte Carlo sampling method

Effective relaxation processes for difficult systems like proteins or spin glasses require special simulation techniques that permit barrier crossing to ensure ergodic sampling. Numerous adaptations of the venerable Metropolis Monte Carlo (MMC) algorithm have been proposed to improve its sampling efficiency, including various hybrid Monte Carlo (HMC) schemes, and methods designed specifically for overcoming quasi-ergodicity problems such as Jump Walking (J-Walking), Smart Walking (S-Walking), Smart Darting, and Parallel Tempering. We present an alternative to these approaches that we call Cool Walking, or C-Walking. In C-Walking two Markov chains are propagated in tandem, one at a high (ergodic) temperature and the other at a low temperature. Nonlocal trial moves for the low temperature walker are generated by first sampling from the high-temperature distribution, then performing a statistical quenching process on the sampled configuration to generate a C-Walking jump move. C-Walking needs only one high-temperature walker, satisfies detailed balance, and offers the important practical advantage that the high and low-temperature walkers can be run in tandem with minimal degradation of sampling due to the presence of correlations. To make the C-Walking approach more suitable to real problems we decrease the required number of cooling steps by attempting to jump at intermediate temperatures during cooling. We further reduce the number of cooling steps by utilizing "windows" of states when jumping, which improves acceptance ratios and lowers the average number of cooling steps. We present C-Walking results with comparisons to J-Walking, S-Walking, Smart Darting, and Parallel Tempering on a one-dimensional rugged potential energy surface in which the exact normalized probability distribution is known. C-Walking shows superior sampling as judged by two ergodic measures.     

Time-dependent quantum Monte Carlo and the stochastic quantization

We examine the relation between the recently proposed time-dependent quantum Monte Carlo (TDQMC) method and the principles of stochastic quantization. In both TDQMC and stochastic quantization, particle motion obeys stochastic guidance equations to preserve quantum equilibrium. In this way the probability density of the Monte Carlo particles corresponds to the modulus square of the many-body wave function at all times. However, in TDQMC, the motion of particles and guide waves occurs in physical space unlike in stochastic quantization where it occurs in configuration space. Hence, the practical calculation of time evolution of many-body fully correlated quantum systems becomes feasible within the TDQMC methodology. We illustrate the TDQMC technique by calculating the symmetric and antisymmetric ground state of a model one-dimensional helium atom, and the time evolution of the dipole moment when the atom is irradiated by a strong ultrashort laser pulse.     

Reactive Monte Carlo and grand-canonical Monte Carlo simulations of the propene metathesis reaction system

The influence of silicalite-1 pores on the reaction equilibria and the selectivity of the propene metathesis reaction system in the temperature range between 300 and 600 K and the pressure range from 0.5 to 7 bars has been investigated with molecular simulations. The reactive Monte Carlo (RxMC) technique was applied for bulk-phase simulations in the isobaric-isothermal ensemble and for two phase systems in the Gibbs ensemble. Additionally, Monte Carlo simulations in the grand-canonical ensemble (GCMC) have been carried out with and without using the RxMC technique. The various simulation procedures were combined with the configurational-bias Monte Carlo approach. It was found that the GCMC simulations are superior to the Gibbs ensemble simulations for reactions where the bulk-phase equilibrium can be calculated in advance and does not have to be simulated simultaneously with the molecules inside the pore. The confined environment can increase the conversion significantly. A large change in selectivity between the bulk phase and the pore phase is observed. Pressure and temperature have strong influences on both conversion and selectivity. At low pressure and temperature both conversion and selectivity have the highest values. The effect of confinement decreases as the temperature increases.     

Theory and algorithms for mixed Monte Carlo-stochastic dynamics simulations

The recently introduced mixed MC-SD method is a fundamentally new procedure which essentially eliminates the distinction between Monte Carlo and dynamics. Unlike other methods which utilize forces, Brownian motion or dynamical steps to generate new trial configurations in a Monte Carlo search, mixed MC-SD does stochastic dynamics on the cartesian space of a molecule and Monte Carlo on the torsion space of the molecule simultaneously. After each dynamical step, a random deformation of a rotatable torsion is performed and accepted or rejected according to the Metropolis criteria. The next dynamical step is performed from the most recent configuration and the velocities from the previous dynamical step. The smooth merging of Monte Carlo and dynamics requires the use of the stochastic velocity Verlet integration scheme. Here, the velocity Verlet stochastic dynamics method is derived, and the reasons why it can be joined with Metropolis Monte Carlo in a continuous fashion are explored.     

Thermodynamic properties of model solids with short-ranged potentials from Monte Carlo simulations and perturbation theory

Monte Carlo simulations have been performed to determine the excess energy and the equation of state of fcc solids with Sutherland potentials for wide ranges of temperatures, densities, and effective potential ranges. The same quantities have been determined within a perturbative scheme by means of two procedures: (i) Monte Carlo simulations performed on the reference hard-sphere system and (ii) second-order Barker-Henderson perturbation theory. The aim was twofold: on the one hand, to test the capability of the "exact" MC-perturbation theory of reproducing the direct MC simulations and, on the other hand, the reliability of the Barker-Henderson perturbation theory, as compared with direct MC simulations and MC-perturbation theory, to determine the thermodynamic properties of these solids depending on temperature, density, and potential range. We have found that the simulation data for the excess energy obtained from the two procedures are in close agreement with each other. For the equation of state, the results from the MC-perturbation procedure also agree well with the direct MC simulations except for very low temperatures and extremely short-ranged potentials. Regarding the Barker-Henderson perturbation theory, we have found that in general the second-order approximation does not provide significant improvement over the first-order one.