Computer simulation thermodynamic properties, simple

The basic idea behind an atomistic-level simulation is quite simple. Given an accurate description of the energetic interactions between a collection of atoms and a set of initial atomic coordinates (and in some cases, velocities), the positions (velocities) of these atoms are advanced subject to a set of thermodynamic constraints. If the positions are advanced stochastically, we call the simulation method Monte Carlo or MG [10]. No velocities are required for this technique. If the positions and velocities are advanced deterministically, we call the method molecular dynamics or MD [10]. Other methods exist which are part stochastic and part deterministic, but we need not concern ourselves with these details here. The important point is that statistical mechanics teUs us that the collection of atomic positions that are obtained from such a simulation, subject to certain conditions, is enough to enable aU of the thermophysical properties of the system to be determined. If the velocities are also available (as in an MD simulation), then time-dependent properties may also be computed. If done properly, the numerical method that generates the trajectories... 

The methods of computer simulation of adsorption (and diffusion) in micro-porous solids were described in Chapter 4 a summary is given in Table 4.1. These techniques are now sufficiently well developed for physisorption that thermodynamic properties can be predicted routinely for relatively simple adsorbates, once the structural details of the host are known. Molecular mechanics using standard forcelields are very successful for zeolitic systems, which take into account dispersive interactions satisfactorily, but it is also possible to use higher level calculations. 

The problem of predicting the thermodynamic properties of such a classical system becomes the problem of evaluation of the configuration integral, the integral over exp(— O). This is still a difficult task. In general, it can be done only through computer simulations (Monte Carlo and molecular dynamics methods). However, there are a few simple approximations which are helpful. 

In molecular force fields, the interaction energy between sites can be divided into contributions from intramolecular and intermolecular interactions. The significance of the different contributions to the force field varies depending on the required application. E.g., for industrial engineering applications, simple models with a low computational cost are required that are nonetheless able to predict accurately thermodynamic properties. Numerous force fields of varying complexity are currently available. The simplest force fields include only potentials that describe the intermolecular interactions and are frequently used for small molecules. More complex force fields include intramolecular interactions that are necessary for the simulation of larger molecules such as polymers. 

With the ongoing increase of computer performance, molecular modeling and simulation is gaining importance as a tool for predicting the thermodynamic properties for a wide variety of fluids in the chemical industry. One of the major issues of molecular simulation is the development of adequate force fields that are simple enough to be computationally efficient, but complex enough to consider the relevant inter- and intramolecular interactions. There are different approaches to force field development and parameterization. Parameters for molecular force fields can be determined both bottom-up from quantum chemistry and top-down from experimental data. 

On the other hand, the analysis of experimental shockwave data for water has shown (Ree 1982) that at the limit of high temperatures and pressures intermolecular interactions of water become simpler. In this case, it becomes even possible to use a spherically-symmetric model potential for the calculations of water properties either from computer simulations (Belonoshko and Saxena 1991, 1992) or from thermodynamic perturbation theory in a way similar to simple liquids (Hansen and McDonald 1986). However, such simplifications exclude the possibility of understanding many important and complex phenomena in aqueous fluids on a true molecular level, which is, actually, the strongest advantage and the main objective of molecular computer simulations. 

We start with the simplest model of the interface, which consists of a smooth charged hard wall near a ionic solution that is represented by a collection of charged hard spheres, all embedded in a continuum of dielectric constant c. This system is fairly well understood when the density and coupling parameters are low. Then we replace the continuum solvent by a molecular model of the solvent. The simplest of these is the hard sphere with a point dipole[32], which can be treated analytically in some simple cases. More elaborate models of the solvent introduce complications in the numerical discussions. A recently proposed model of ionic solutions uses a solvent model with tetrahedrally coordinated sticky sites. This model is still analytically solvable. More realistic models of the solvent, typically water, can be studied by computer simulations, which however is very difficult for charged interfaces. The full quantum mechanical treatment of the metal surface does not seem feasible at present. The jellium model is a simple alternative for the discussion of the thermodynamic and also kinetic properties of the smooth interface [33, 34, 35, 36, 37, 38, 39, 40]. 

The goal of extending classical thermostatics to irreversible problems with reference to the rates of the physical processes is as old as thermodynamics itself. This task has been attempted at different levels. Description of nonequilibrium systems at the hydrodynamic level provides essentially a macroscopic picture. Thus, these approaches are unable to predict thermophysical constants from the properties of individual particles in fact, these theories must be provided with the transport coefficients in order to be implemented. Microscopic kinetic theories beginning with the Boltzmann equation attempt to explain the observed macroscopic properties in terms of the dynamics of simplified particles (typically hard spheres). For higher densities kinetic theories acquire enormous complexity which largely restricts them to only qualitative and approximate results. For realistic cases one must turn to atomistic computer simulations. This is particularly useful for complicated molecular systems such as polymer melts where there is little hope that simple statistical mechanical theories can provide accurate, quantitative descriptions of their behavior. 

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