Molecular modelling computer simulation, concepts

The complexity of polymeric systems make tire development of an analytical model to predict tlieir stmctural and dynamical properties difficult. Therefore, numerical computer simulations of polymers are widely used to bridge tire gap between tire tlieoretical concepts and the experimental results. Computer simulations can also help tire prediction of material properties and provide detailed insights into tire behaviour of polymer systems. A simulation is based on two elements a more or less detailed model of tire polymer and a related force field which allows tire calculation of tire energy and tire motion of tire system using molecular mechanisms, molecular dynamics, or Monte Carlo teclmiques 1631. 

Monte Carlo (MC) techniques for molecular simulations have a long and rich history, and have been used to a great extent in studying the chemical physics of polymers. The majority of molecular modeling studies today do not involve the use of MC methods however, the sampling capabiUty provided by MC methods has gained some popularity among computational chemists as a result of various studies (95—97). Relevant concepts of MC are summarized herein. 

With respect to the various spectroscopic methods not discussed so far there are four methodologically different concepts of molecular modeling (i) quantum-mechanical methods which result structural and electronic information (ii) MM-based structural modeling, followed by a single-point MO-based computation of the electronic properties (iii) spectra simulation with given electronic parameters in cases, where structural parameters (e.g., distances such as used in protein modeling, see above) are involved in the simulation (iv) structure property correlations. The latter approach will be discussed briefly at the end of this Section (redox potentials) and in Section 4. 

Computer simulations, Monte Carlo or molecular dynamics, in fact appear to be the actual most effective way of introducing statistical averages (if one decides not to pass to continuous distributions), in spite of their computational cost. Some concepts, such as the quasi-structure model introduced by Yomosa (1978), have not evolved into algorithms of practical use. The numerous versions of methods based on virial expansion, on integral equation description of correlation functions, on the application of perturbation theory to simple reference systems (the basic aspects of these... 

Chapter 3 by Robert M. Whitnell and Kent R. Wilson extends some of the concepts delineated in Chapter 2. The chapter on computational molecular dynamics of chemical reactions in solution is a definitive, long-awaited bridge between the organic and chemical physics communities. Techniques for simulating reaction dynamics are covered in nonmathematical language. Work on thermally activated reactions, such as isomerization, atom exchange, 5 2, and S l reactions, as well as ion-pair association, and proton transfers, are reviewed. For nonthermally activated reactions, a variety of photodissociations and isomerizations are discussed. The interplay of computer simulations of solution reaction dynamics and models of the reactions is explained. 

To tackle these problems successfully, new concepts will be required for developing systematic modeling techniques that can describe parts of the chemical supply chain at different levels of abstraction. A specific example is the integration of molecular thermodynamics in process simulation computations. This would fulfill the objective of predicting the properties of new chemical products when designing a new manufacturing plant. However, such computations remain unachievable at the present time and probably will remain so for the next decade. The challenge is how to abstract the details and description of a complex system into a reduced dimensional space. 

The long-term goal in the science of thermochemical conversion of a solid fuel is to develop comprehensive computer codes, herein referred to as a bed model or CFSD (computational fluid-solid dynamics). Firstly, this CFSD code must be able to simulate basic conversion concepts, with respect to the mode, movement, composition and configuration of the fuel bed. The conversion concept has a great effect on the behaviour of the thermochemical conversion process variables, such as the molecular composition and mass flow of conversion gas. Secondly, the bed model must also consider the fuel-bed structure on both micro- and macro-scale. This classification refers to three structures, namely interstitial gas phase, intraparticle gas phase, and intraparticle solid phase. Commonly, a packed bed is referred to as a two-phase system. 

Nevertheless, the concept of spatial dispersion provides a general background for a qualitative understanding of those solvation effects which are beyond the scope of local continuum models. The nonlocal theory creates a bridge between conventional and well developed local approaches and explicit molecular level treatments such as integral equation theory, MC or MD simulations. The future will reveal whether it can survive as a computational tool competitive with these popular and more familiar computational schemes. 

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