Complex prediction methodology

This complex prediction methodology is dramatically different from other approaches to stmcture-activity analysis in that it simultaneonsly employs ... 

Rapid advances are taking place in the application of DFT to describe complex chemical reactions. Researchers in different fields working in the domain of quantum chemistry tend to have different perspectives and to use different computational approaches. DFT owes its popularity to recent developments in predictive powers for physical and chemical properties and its ability to accurately treat large systems. Both theoretical content and computational methodology are developing at a pace, which offers scientists working in diverse fields of quantum chemistry, cluster science, and solid state physics. 

More recently, mathematically defined, structure controlled, covalent megamers have been reported. They are a major subclass of megamers also referred to as core-shell tecto dendrimers) [126-128], Synthetic methodologies to these new architectures have been reported to produce precise megameric structures that adhere to mathematically defined bonding rules [91, 129], It appears that structure controlled complexity beyond dendrimers is now possible. The demonstrated structure control within the dendrimer modules, and now the ability to mathematically predict and synthesize precise assemblies of these modules, provide a broad concept for the systematic construction of nanostructures with dimensions that could span the entire nanoscale region (Figure 1.24). 

Whether the prediction scheme is a simple chart, a formula, or a complex numerical procedure, there are three basic elements that must be considered meteorology, source emissions, and atmospheric chemical interactions. Despite the diversity of methodologies available for relating emissions to ambient air quality, there are two basic types of models. Those based on a fundamental description of the physics and chemistry occurring in the atmosphere are classified as a priori approaches. Such methods normally incorporate a mathematical treatment of the meteorological and chemical processes and, in addition, utilize information about the distribution of source emissions. Another class of methods involves the use of a posteriori models in which empirical relationships are deduced from laboratory or atmospheric measurements. These models are usually quite simple and typically bear a close relationship to the actual data upon which they are based. The latter feature is a basic weakness. Because the models do not explicitly quantify the causal phenomena, they cannot be reliably extrapolated beyond the bounds of the data from which they were derived. As a result, a posteriori models are not ideally suited to the task of predicting the impacts of substantial changes in emissions. 

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