Model systems molecular structure

Paddison, S. J. 2001. The modeling of molecular structure and ion transport in sulfonic acid based ionomer membranes. Journal of New Materials for Electrochemical Systems 4 197-207. 

Neurock, M., C. Libanati, A. Nigam, and M.T. Klein, Monte Carlo Simulation of Complex Reaction Systems Molecular Structure and Reactivity in Modelling of Heavy Oils., Chem. Eng. Sci., 45, (8), 2083-2088,1990. 

The concept of the chemical bond as an agent for holding atoms together in molecules—analogous to the connections between planets in our solar system— was formulated around the middle of the 19th century. In the 1860s, the chemists August Wilhelm Hofmann and Edward Frankland used three-dimensional arrays of colored wooden balls as models for molecular structure in their lectures in Berlin and London. In 1875, the Dutch chemist Jacobus van t Hoff popularized... 

Neurock, M., Libatani, C., Nigam, A., and Klein, M. T., Monte Carlo simulation of complex reaction systems Molecular structure and reactivity in modelling heavy oils, Chem. Eng. Sci. 45(8), 2083-2088 (1990). 

The traditional model of atomic structure has been the quantum-mechanical analogue of a solar system, with electrons perturbing each other only a little from their individual states of definite energy and angular momentum. On the other hand, the traditional model for molecular structure has been the quantum-mechanical counterpart of balls held together by springs in a fairly rigid, well-defined structure. These two pictures are so different and lend themselves to such different computational methods that they have remained as separated, almost unrelated fields within chemical physics. 

Kollman and coworkers apphed a variety of computational methods to this mechanistic problem—including quantum mechanics on small model systems, molecular dynamics simulations with the AMBER force field on the whole ODCase-substrate system, and MM-PBSA free energy calculations on ODCase with bound OMP [38]. Based on their results, they proposed a decarboxylation mechanism for ODCase that involves C5 protonation. Their calculations at the MP2/6-31+G //HF/6-31+G level showed that C5 has a greater intrinsic proton affinity than C6, 02, and even 04. This, coupled with the fact that Lys72 (M. thermoautotrophicum numbering see Table 2) is near C5 and C6 in the inhibitor-bound crystal structures, prompted the authors to embrace a C5 protonation mechanism. However, the authors themselves acknowledged the uncertainties of their calculations because of approximations employed in representing the enzyme active site. 

A. A. Frost, J, Chem. Phys., 47, 3707 (1967). Floating Spherical Gaussian Orbital Model of Molecular Structure. 1. Computational Procedure. LiH as an Example and A. A. Frost, J. Chem. Phys, 47, 3714 (1967). Floating Spherical Gaussian Orbital Model of Molecular Structure. II. One- and Two-Electron-Pair Systems. 

RAMSES is usually generated from molecular structures in a VB representation. The details of the connection table (localized charges, lone pairs, and bond orders) are kept within the model and are accessible for further processes. Bond orders are stored with the n-systems, while the number of free electrons is stored with the atoms. Upon modification oF a molecule (e.g., in systems dealing with reactions), the VB representation has to be generated in an adapted Form from the RAMSES notation. 

The next and very important step is to make a decision about the descriptors we shall use to represent the molecular structures. In general, modeling means assignment of an abstract mathematical object to a real-world physical system and subsequent revelation of some relationship between the characteristics of the object on the one side, and the properties of the system on the other. 

Obviously, to model these effects simultaneously becomes a very complex task. Hence, most calculation methods treat the effects which are not directly related to the molecular structure as constant. As an important consequence, prediction models are valid only for the system under investigation. A model for the prediction of the acidity constant pfQ in aqueous solutions cannot be applied to the prediction of pKj values in DMSO solutions. Nevertheless, relationships between different systems might also be quantified. Here, Kamlet s concept of solvatochro-mism, which allows the prediction of solvent-dependent properties with respect to both solute and solvent [1], comes to mind. 

A hands-on experience with the method is possible via the SPINUS web service [48. This service uses a client-server model. The user can draw a molecular structure within the web browser workspace (the client), and send it to a server where the predictions are computed by neural networks. The results are then sent back to the user in a few seconds and visualised with the same web browser. Several operations and different types of technology arc involved in the system ... 

To go from experimental observations of solvent effects to an understanding of them requires a conceptual basis that, in one approach, is provided by physical models such as theories of molecular structure or of the liquid state. As a very simple example consider the electrostatic potential energy of a system consisting of two ions of charges Za and Zb in a medium of dielectric constant e. 

A theoretical model ought to be unbiased. It should rely on no presuppositions about molecular structure or chemical processes which would make it inapplicable to classes of systems or phenomena where these assumptions did not apply. It should not in general invoke special procedures for specific types of molecules. 

In this chapter, we will consider the other half of a model chemistry definition the theoretical method used to model the molecular system. This chapter will serve as an introductory survey of the major classes of electronic structure calculations. The examples and exercises will compare the strengths and weaknesses of various specific methods in more detail. The final section of the chapter considers the CPU, memory and disk resource requirements of the various methods. 

Two hundred years were required before the molecular structure of the double layer could be included in electrochemical models. The time spent to include the surface structure or the structure of three-dimensional electrodes at a molecular level should be shortened in order to transform electrochemistry into a more predictive science that is able to solve the important technological or biological problems we have, such as the storage and transformation of energy and the operation of the nervous system, that in a large part can be addressed by our work as electrochemists. 

This article reviews progress in the field of atomistic simulation of liquid crystal systems. The first part of the article provides an introduction to molecular force fields and the main simulation methods commonly used for liquid crystal systems molecular mechanics, Monte Carlo and molecular dynamics. The usefulness of these three techniques is highlighted and some of the problems associated with the use of these methods for modelling liquid crystals are discussed. The main section of the article reviews some of the recent science that has arisen out of the use of these modelling techniques. The importance of the nematic mean field and its influence on molecular structure is discussed. The preferred ordering of liquid crystal molecules at surfaces is examined, along with the results from simulation studies of bilayers and bulk liquid crystal phases. The article also discusses some of the limitations of current work and points to likely developments over the next few years. 

The rapid rise in computer speed over recent years has led to atom-based simulations of liquid crystals becoming an important new area of research. Molecular mechanics and Monte Carlo studies of isolated liquid crystal molecules are now routine. However, care must be taken to model properly the influence of a nematic mean field if information about molecular structure in a mesophase is required. The current state-of-the-art consists of studies of (in the order of) 100 molecules in the bulk, in contact with a surface, or in a bilayer in contact with a solvent. Current simulation times can extend to around 10 ns and are sufficient to observe the growth of mesophases from an isotropic liquid. The results from a number of studies look very promising, and a wealth of structural and dynamic data now exists for bulk phases, monolayers and bilayers. Continued development of force fields for liquid crystals will be particularly important in the next few years, and particular emphasis must be placed on the development of all-atom force fields that are able to reproduce liquid phase densities for small molecules. Without these it will be difficult to obtain accurate phase transition temperatures. It will also be necessary to extend atomistic models to several thousand molecules to remove major system size effects which are present in all current work. This will be greatly facilitated by modern parallel simulation methods that allow molecular dynamics simulations to be carried out in parallel on multi-processor systems [115]. 

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