Kinetic Monte Carlo approach

Kinetic Monte Carlo approaches. One scheme that has found widespread use in the consideration of diffusive processes is the kinetic Monte Carlo approach in... 

A new coarse-grained chemical reaction model (CGCRM) has been proposed by Garrison and coworkers [56,57]. In this model a kinetic Monte Carlos approache that includes a probabilistic element is used to predict when reactions occur. It is thereby possible to avoid the use of a chemically correct interaction potential. The CGCRM uses known chemical reactions along with their probabilities and exother-micities for a specific material to estimate the effect of chemical reactions on the ablation process. 

The Monte Carlo method as described so far is useful to evaluate equilibrium properties but says nothing about the time evolution of the system. However, it is in some cases possible to construct a Monte Carlo algorithm that allows the simulated system to evolve like a physical system. This is the case when the dynamics can be described as thermally activated processes, such as adsorption, desorption, and diffusion. Since these processes are particularly well defined in the case of lattice models, these are particularly well suited for this approach. The foundations of dynamical Monte Carlo (DMC) or kinetic Monte Carlo (KMC) simulations have been discussed by Eichthom and Weinberg (1991) in terms of the theory of Poisson processes. The main idea is that the rate of each process that may eventually occur on the surface can be described by an equation of the Arrhenius type ... 

Kinetic theory of gases (collision model or Monte Carlo approach)... 

In more detail, our approach can be briefly summarized as follows gas-phase reactions, surface structures, and gas-surface reactions are treated at an ab initio level, using either cluster or periodic (plane-wave) calculations for surface structures, when appropriate. The results of these calculations are used to calculate reaction rate constants within the transition state (TS) or Rice-Ramsperger-Kassel-Marcus (RRKM) theory for bimolecular gas-phase reactions or unimolecular and surface reactions, respectively. The structure and energy characteristics of various surface groups can also be extracted from the results of ab initio calculations. Based on these results, a chemical mechanism can be constructed for both gas-phase reactions and surface growth. The film growth process is modeled within the kinetic Monte Carlo (KMC) approach, which provides an effective separation of fast and slow processes on an atomistic scale. The results of Monte Carlo (MC) simulations can be used in kinetic modeling based on formal chemical kinetics. 

Currently only Monte Carlo approaches can handle the wide range of surface geometries, reflection models and support complex atomic and molecular processes that occur in real fusion edge plasmas. Therefore the neutral particle transport (ionization, dissociation, etc.) as well as impurity ion transport in the edge region of fusion plasmas is often treated by Monte Carlo simulation on a kinetic level. 

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. ...

It is often stated that MC methods lack real time and results are usually reported in MC events or steps. While this is immaterial as far as equilibrium is concerned, following real dynamics is essential for comparison to solutions of partial differential equations and/or experimental data. It turns out that MC simulations follow the stochastic dynamics of a master equation, and with appropriate parameterization of the transition probabilities per unit time, they provide continuous time information as well. For example, Gillespie has laid down the time foundations of MC for chemical reactions in a spatially homogeneous system.f His approach is easily extendable to arbitrarily complex computational systems when individual events have a prescribed transition probability per unit time, and is often referred to as the kinetic Monte Carlo or dynamic Monte Carlo (DMC) method. The microscopic processes along with their corresponding transition probabilities per unit time can be obtained via either experiments such as field emission or fast scanning tunneling microscopy or shorter time scale DFT/MD simulations discussed earlier. The creation of a database/lookup table of transition... 

Statistical Approach with Kinetic Monte Carlo Simulation... 

Two model approaches are compared by simulating reactive transport of acenaphthene in a heterogeneous porous medium. In a Monte Carlo approach a Lagrangian onedimensional streamtube model is used to assess the transport behaviour at field scale for distances of up to 800 m. Aquifer properties are taken from results of field experiments characterising a test site in a shallow quaternary sand and gravel aquifer. The results of the streamtube model are compared to model results of a two-dimensional Eulerian model. Both models account for kinetic sorption, described as diffusive transport in intra-particle pores. 

Investigation of the motion of adsorbed molecules, which give mechanisms and rates of re-orientation and diffusion, require alternative approaches. For systems that contain highly mobile species. Molecular Dynamics (MD) techniques are widely used. However, for many adsorbates the timescales of motion are much longer than can feasibly be simulated, so that MD is only relevant either for small molecules or at high temperatures. In order to simulate slower diffusion, the process must be considered in terms of rare events with significant activations. The activated processes are then usefully treated by transition state theory, and the associated processes treated over extended timescales and volumes by, for example, Kinetic Monte Carlo (KMQ techniques. 

The purpose of this chapter is to selectively summarize recent advances in the molecular modeling of anode and cathode electrocatalytic reactions employing different computational approaches, ranging from first-principles quantum-chemical calculations (based on density functional theory, DFT), ab initio and classical molecular dynamics simulations to kinetic Monte Carlo simulations. Each of these techniques is associated with a proper system size and timescale that can be adequately treated and will therefore focus on different aspects of the reactive system under consideration. 

In a staged multi-scale approach, the energetics and reaction rates obtained from these calculations can be used to develop coarse-grained models for simulating kinetics and thermodynamics of complex multi-step reactions on electrodes (for example see [25, 26, 27, 28, 29, 30]). Varying levels of complexity can be simulated on electrodes to introduce defects on electrode surfaces, composition of alloy electrodes, distribution of alloy electrode surfaces, particulate electrodes, etc. Monte Carlo methods can also be coupled with continuum transport/reaction models to correctly describe surfaces effects and provide accurate boundary conditions (for e.g. see Ref. [31]). In what follows, we briefly describe density functional theory calculations and kinetic Monte Carlo simulations to understand CO electro oxidation on Pt-based electrodes. 

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. 

This chapter discusses a staged multi-scale approach for understanding CO electrooxidation on Pt-based electrodes. In this approach, density functional theory (DFT) is used to obtain an atomistic view of reactions on Pt-based surfaces. Based on results from experiments and quantum chemistry calculations, a consistent coarse-grained lattice model is developed. Kinetic Monte Carlo (KMC) simulations are then used to study complex multi-step reaction kinetics on the electrode surfaces at much larger lengthscales and timescales compared to atomistic dimensions. These simulations are compared to experiments. We review KMC results on Pt and PtRu alloy surfaces. 

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