Atomistic/molecular-level modeling

Classical molecular simulation methods such as MC and MD represent atomistic/molecular-level modeling, which discards the electronic degrees of freedom while utilizing parameters transferred from quantum level simulation as force field information. A molecule in the simulation is composed of beads representing atoms, where the interactions are described by classical potential functions. Each bead has a dispersive pair-wise interaction as described by the Lennard-Jones (LJ) potential, ULj(Ly) ... 

Figure 1. Atomistic and molecular level models for rod-shaped (top) and disc-shaped (bottom) mesogens. Examples shown 4/-n-pentyl-4-cyanobiphenyl (5CB) (rod-shaped) and hexakis(n-hexyloxy)triphenylene (HAT6) (disc-shaped). (This figure is Reproduced from Ref. 16.)...

Molecular dynamics (MD) simulation has recently enjoyed considerable success in modeling proteins since the MD methodology based on molecular mechanics (MM) enable us to probe the motions of proteins at the atomistic/molecular level [1-4]. With the improvement offeree fields as well as the increase of computational power, MD simulations are able to provide accurate description of protein motions efficiently. Therefore, MD simulation has been widely accepted as a key complementary tool to experimental techniques, such as nuclear magnetic resonance (NMR) and X-ray crystallography, which provide very limited d3mamical information about proteins. 

This extremely cursory overview suffices to show that there are several molecular and atomistic level interactions that are involved in any single corrosion event and that there is a large number of possible mechanisms that still require a rigorous theoretical treatment. In the following section, we introduce a number of molecular-level modeling techniques that have been, or could be, applied to the further detailed study of these mechanisms for materials presently used in industry and materials yet to be discovered and/or applied. 

Both aerosol modeling and more fundamental atomistic and molecular level models have been applied to this problem. Aerosol dynamics modeling has lead to a better understanding of the individual steps that comprise the formation of particles, all the way from nucleation to subsequent growth. Both molecualar orbital and reaction rate theory was used as sources of fundamental data for input to the aerosol dynamics model. A simplistic molecular dynamics computation has been used to explain the particle morphology observed. 

Various modelling approaches exist. The modelling may focus on individual thermal-mechanical, flow, chemical, and electrochemical subsystems or on coupled integrated systems. Because the subsystems are typically characterised by different length scales, modelling may also take place on different levels, ranging from the atomistic/molecular-level via the cell component-level, the cell-level to the stack-level, and finally to the system-level performance simulations. 

Within the last two decades, enormous progress has been achieved in the ability to calculate the structures, the properties (e.g., thermodynamic, mechanical, transportation properties), and the reactivity of solids starting from atomistic approaches. The molecular-level models can be classified into three categories. 

Microkinetic modeling assembles molecular-level information obtained from quantum chemical calculations, atomistic simulations and experiments to quantify the kinetic behavior at given reaction conditions on a particular catalyst surface. In a postulated reaction mechanism the rate parameters are specified for each elementary reaction. For instance adsorption preexponential terms, which are in units of cm3 mol"1 s"1, have been typically assigned the values of the standard collision number (1013 cm3 mol"1 s 1). The pre-exponential term (cm 2 mol s 1) of the bimolecular surface reaction in case of immobile or moble transition state is 1021. The same number holds for the bimolecular surface reaction between one mobile and one immobile adsorbate producing an immobile transition state. However, often parameters must still be fitted to experimental data, and this limits the predictive capability that microkinetic modeling inherently offers. A detailed account of microkinetic modelling is provided by P. Stoltze, Progress in Surface Science, 65 (2000) 65-150. 

Coarse-grained (CG) models have proven to be useful in many fields of chemical research [1-10], allowing molecular simulations to be performed on larger system sizes and access longer timescales than is possible with atomistic-level models. 

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