Mesoscale Monte Carlo

Mesoscale simulations model a material as a collection of units, called beads. Each bead might represent a substructure, molecule, monomer, micelle, micro-crystalline domain, solid particle, or an arbitrary region of a fluid. Multiple beads might be connected, typically by a harmonic potential, in order to model a polymer. A simulation is then conducted in which there is an interaction potential between beads and sometimes dynamical equations of motion. This is very hard to do with extremely large molecular dynamics calculations because they would have to be very accurate to correctly reflect the small free energy differences between microstates. There are algorithms for determining an appropriate bead size from molecular dynamics and Monte Carlo simulations. 

Figure 5 Multiscale approach to understand rate of CO2 diffusion into and CH4 diffusion out of a structure I hydrate, (left) Molecular simulation for individual hopping rates, (middle) Mesoscale kinetic Monte Carlo simulation of hopping on the hydrate lattice to determine dependence of diffusion constants on vacancy, CO2 and CH4 concentrations, (right) Macroscopic coupled non-linear diffusion equations to describe rate of CO2 infusion and methane displacement. Graph from Stockie. ...

In the last section, we discussed the use of QC calculations to elucidate reaction mechanisms. First-principle atomistic calculations offer valuable information on how reactions happen by providing detailed PES for various reaction pathways. Potential energy surfaces can also be obtained as a function of electrode potential (for example see Refs. [16, 18, 33, 38]). However, these calculations do not provide information on the complex reaction kinetics that occur on timescales and lengthscales of electrochemical experiments. Mesoscale lattice models can be used to address this issue. For example, in Refs. [25, 51, 52] kinetic Monte Carlo (KMC) simulations were used to simulate voltammetry transients in the timescale of seconds to model Pt(l 11) and Pt(lOO) surfaces containing up to 256x256 atoms. These models can be developed based on insights obtained from first-principle QC calculations and experiments. Theory and/or experiments can be used to parameterize these models. For example, rate theories [22, 24, 53, 54] can be applied on detailed potential energy surfaces from accurate QC calculations to calculate electrochemical rate constants. On the other hand, approximate rate constants for some reactions can be obtained from experiments (for example see Refs. [25, 26]). This chapter describes the later approach. 

As shown in Figure 26.3, both lattice Boltzmann gas (LBG) [37,38], direct simulation Monte-Carlo (DSM-C) [41], and off-grid particle methods such as DPD [42], and fluid particle method (FPM) [43] can be treated within a common methodological framework. This framework in the mesoscale consists the successive coarse-graining of the underlying molecular dynamics system. We can list its components as ... 

Some of the most widely used computational approaches will be briefly described below, namely some quantum chemical methods, classical simulations by Monte Carlo and Molecular Dynamics techniques and a few mesoscale methods. 

This chapter is organized as follows. In section 1.1, we introduce our notation and present the details of the molecular and mesoscale simulations the expanded ensemble-density of states Monte Carlo method,and the evolution equation for the tensor order parameter [5]. The results of both approaches are presented and compared in section 1.2 for the cases of one or two nanoscopic colloids immersed in a confined liquid crystal. Here the emphasis is on the calculation of the effective interaction (i.e. potential of mean force) for the nanoparticles, and also in assessing the agreement between the defect structures found by the two approaches. In section 1.3 we apply the mesoscopic theory to a model LC-based sensor and analyze the domain coarsening process by monitoring the equal-time correlation function for the tensor order parameter, as a function of the concentration of adsorbed nanocolloids. We present our conclusions in Section 1.4. 

Misawa, M. (2002). Mesoscale structure and fractal nature of 1-propanol aqueous solution A reverse Monte Carlo analysis of small angle neutron scattering intensity. J. Chem. Phys., 116, 8463-8468. 

In order to overcome these difficulties, considerable effort has been devoted to the development of mesoscale simulation methods such as Dissipative Particle Dynamics [1-3], Lattice-Boltzmann [4-6], and Direct Simulation Monte Carlo [7-9]. The common approach of all these methods is to average out irrelevant microscopic details in order to achieve high computational efficiency while keeping the essential features of the microscopic physics on the length scales of interest. Applying these ideas to suspensions leads to a simplified, coarse-grained description of the solvent degrees of freedom, in which embedded maaomolecules such as polymers are treated by conventional molecular dynamics simulations. 

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