Throughout chemistry, computational techniques are becoming increasingly important. Problem solving and modelling is quickly achievable but as the power of computers increases so does the complexity of the tasks and the speed at which they are solved. A basic understanding of the theory and concepts underpinning computational chemistry is therefore essential to all chemical science students, and Essentials of Computational Chemistry: Theory and Models delivers a thorough insight in a readable and approachable manner. Accessible as a text to advanced undergraduate students as well as graduate students Emphasises the basic concepts and applications rather than theory and mathematics Firmly places the subject in context, including many examples and applications Includes coverage and examples from inorganic, organic and biological chemistry and covers quantum mechanical and classical mechanical models Includes case histories that serve as an ideal basis for classroom discussion Delivers information in a readable and approachable manner
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"...This book does the job better than any other 'chemistry for the future' book I have read..." -- Chemistry Industry, 1 July 2002
"...an excellent introduction to the field...this is a fine and practical book..." -- Cell Biology International, August 2002
"...this is a first rate book...I am happy to recommend it... -- Chemistry in Britain, July 2002
All modern chemists need at least a cursory understanding of theory/computation/modeling even if they are not practitioners of the art. Because of the broad array of theoretical tools now available, it is the rare problem of interest that does not occupy the attention of both experimental and theoretical chemists. Indeed, the synergy between theory and experiment has vastly accelerated progress in any number of areas (as one example, it is hard to imagine a modern paper on the matrix isolation of a reactive intermediate and its identification by infrared spectroscopy not making a comparison of the experimental spectrum to one obtained from theory/calculation).
To take advantage of readily accessible theoretical tools, and to understand the results reported by theoretical collaborators (or competitors), even the wettest of wet chemists can benefit from some familiarity with theoretical chemistry. My objective in this book is to provide a survey of computational chemistry—its underpinnings, its jargon, its strengths and weaknesses—that will be accessible to both the experimental and theoretical communities. The level of the presentation assumes exposure to quantum and statistical mechanics; particular topics/examples span the range of inorganic, organic, and biological chemistry. As such, this text could be used in a course populated by senior undergraduates and/or beginning graduate students without regard to specialization.
The scope of theoretical methodologies presented in the text reflects my judgement of the degree to which these methodologies impact on a broad range of chemical problems, i.e., the degree to which a practicing chemist may expect to encounter them repeatedly in the literature and thus should understand their applicability (or lack thereof). In some instances, methodologies that do not find much modern use are discussed because they help to illustrate in an intuitive fashion how more contemporary models developed their current form. Indeed, one of my central goals in this book is to render less opaque the fundamental natures of the various theoretical models. By understanding the assumptions implicit in a theoretical model, and the concomitant limitations imposed by those assumptions, one can make informed judgements about the trustworthiness of theoretical results (and economically sound choices of models to apply, if one is about to embark on a computational project).
To make the connection between the pedagogy and the real world, each chapter dealing with a new level of theory contains many specific performance details, and in addition a specific case study taken from the literature highlighting the application of the methodology.
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