Customer Reviews

Essentials of Computational Chemistry: Theories and Models

5 star:		(3)
4 star:		(3)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

This book is a follow-up to a previous release and is a great textbook for learning how to simulate atoms, molecules, and fluid mixtures using a variety of techniques. Its positive attributes includes the following:

1. The author makes it a point to explain the various phrases, acronyms, and terms common in the field, but which may confuse the novice or outsider. For example, the first chapter explains the concept of a potential energy surface, how it can be obtained, and the information that can be gleaned from it. These are simple concepts to those experienced in atomistic modeling but can be mysterious to newcomers.

2. The mathematics in the text are simple enough to be understood without the reader having to resort to proving things herself, but they are complete enough to understand how physical concepts are represented and solved. The equations are also set apart from the text such that they are easy to read.

3. There are a lot of diagrammatic figures that explain what is going on; i.e. how atoms interact via certain empirical potentials. One can also tell that the figures were made specifically to teach a concept, and are not reproductions from a publication.

4. The text is appropriate for first-year graduate students in physics, engineering, and chemistry, and the book provides chapters dedicated to quantum mechanics and thermodynamics, the two topics science and engineering students have the most difficulty in.

5. The case study at the end of each chapter are well laid out and do a good job of illustrating the concepts taught in that chapter.

6. There are a lot of flowcharts that show the process by which a calculation is carried out. See for example the appendix on determining the point symmetry of a molecule. Flowcharts are essentiall to understanding how software works, and is probably the biggest difference between computer science and all the other sciences. Computers execute instructions and programmers use flowcharts to decide how a software is put together. Classes and books in the other science and engineering majors are often devoid of flowcharts, so the use of flowcharts in this book helps the reader get into the computational mindset.

7. The list of references at the end of each chapter are primarily to review articles and articles that introduced important concepts. This provides the reader alternate sources of learning. Gone are long lists of case studies and published data.

With so many pluses, why did I give four stars instead of five? Four reasons mainly.

A. There is almost no coverage of the algorithms used to do the mathematics, whether it be diagonalizing the Hamiltonian, or an Ewald summation of interatomic potentials. For example, I do not recall reading anything about the conjugate gradient method anywhere in the book, yet this algorithm is coded into most major codes in computational chemistry like VASP, SIESTA, ADF, etc...

B. There was minimal discussion of techniques for modeling solids. There were chapters dedicated to modeling gases and liquids, but nothing on solids. This is especially disheartening considering that most of the funded chemistry (theoretical and experimental) going on today involves solids; whether it is designing new polymers, hydrogen storage for fuel cells, or examining surface catalysis.

C. A lot of the research going on today in chemistry is in the properties of surfaces and interfaces. Yet there is little mention on modeling of the concepts related to this; such as surface and interface energy, interface lattice msimatch, symmetry of slabs, etc...

D. The book emphasizes the theories behind doing a calculation, such as the Hartree-Fock method, DFT, force fields, etc.. But there is only some mention on the data that can be generated by a simulation software, and how to use them. The examples I can recall are bond orders, population analysis, radial distribution function, and charge density. Other items that should have been included include density of states (vibrational and electronic), electron localization function, and optical properties such as refractive index or dielectric constant.

Overall, it is still a great book and one worth reading.

This summer one undergraduate and I made my first research foray into computation chemistry. This book from one of the best names in the field was a useful and approachable primer to the uninitiated, but also had sufficient depth to be meaningful to a broad audience. The student took this book as a springboard and reference into the primary literature and her own research, and was able to work independently - I finally have the book back for a long enough amount of time to get to read a lot of it for myself! We're writing our first computational communication this fall, so obviously we learned what we're doing!

Chris Cramer's book on computational and theoretical chemistry is among the most sophisticated and well-written I have come across. The main strength of the book is its treatment of quantum mechanics and statistical mechanics. Cramer who is a world-renowned expert on solvation especially excels in describing important implicit and explicit solvation models. The book often puts emphasis on what we can call the philosophy of computational models. Cramer has a knack of analyzing the big picture that encompasses the specifics and elegantly describes the possibilities, pitfalls, perils and promises of models.

The one problem I have with the book is the absence of biological topics which could have been covered for illustrating the applications of the models. The case studies that have been provided in boxes are valuable but almost entirely consist of pure organic and organometallic chemical studies. For a treatment of applications to biological systems I would recommend Andrew Leach's book on molecular modeling. The other minor limitation of the book is that it might not be entirely suitable for a beginner who might find the derivations and language a little too sophisticated. However a diligent beginner's perseverance would still pay off. For the intermediate level student though, this is a very valuable if not the most comprehensive treatment of theoretical and computational chemistry models that is around, especially for understanding the philosophy of the subject.

This is a good concept-based text for computational chemistry. As others have mentioned, this book does not cover every equation and derivation. Rather, it is more conceptual, using a more pictorial approach. In my opinion, this conceptual approach is more pedagogically useful than a purely mathematical approach for beginners to the subject. Other computational texts often take the approach that a good equation is worth a thousand words. This may be true, but it may also take a thousand minutes to figure out what that equation means. Equations also do little to stir the imagination if one cannot visualize what they really mean. But make no mistake--the really important equations are presented here, but where they are included the various terms are well-defined.

I thought this book was particularly good at showing the strengths and weaknesses of a given model theory. Tables with RMS errors are here aplenty, which are helpful to get an idea of the accuracy of varying levels of theory over some test set for a given property. Coverage is particularly good for pathological cases for theory (e.g. open-shell singlet diradicals, spin contamination, root switching, etc), which is helpful for newcomers to theory.

The organization is also logical, starting with the more easily understandable classical approaches (such as MM) before getting to wavefunction/DFT based methodologies. The description of how parameters for MM are coded is particularly well-described (e.g. fourier series for torsions, Lennard-Jones potentials for van der Waals, etc).

My adviser finally broke down and bought me a copy of this book since I was always stealing his. It's worth having as a reference, and I think $60 is a very fair price for what you get.

Background: After taking a good undergraduate course in physical chemistry, one should be able to painlessly read this book from cover to cover. It essentially is a book for people new to the subject, an introduction to computational techniques without the troubling gory details.

Content: Both MM and QM methods are treated (For a list of the topics use the 'search inside' function and scroll thru the index).
The author tries to give the reader an overview of the methods used in modern computational chemistry. He does a good job in making things clear, although the level of the arguments treated is very basic (the word 'essentials' in the title is there for some reason i guess).

One thing Cramer does rather well is underlining the pros and cons of each method, thus giving even non-chemists the chance to get an intuitive grasp of what one should expect from each one of them, and of what their inherent limits are.

One thing to be pointed out is that almost all methods explained are ground state methods. There actually is a chapter on the computation of excited states but it does leave some important things out (e.g. CASSCF).
As another reviewer pointed out, nothing is said about solids and interfaces, and very little is said about dynamics.
Also there is very little math. While this may as well be a plus for some readers it may leave others disappointed. The basic equations behind the theories are given and explained, but not derived, nor is it explained how they are implemented in actual software.

:: If you are a chemist not directly involved in physical/computational chemistry, but you are still interested in having a basic grasp of what goes on in those fields without devoting too much time and effort to it, this may as well be the book for you.


If you are looking for a book that goes thru the nuts and bolts of computational techniques look elsewhere.

That said, this book is very well written and surely worth reading.

It is a good book to start with for computational chemistry. It covers the concepts and suitable for newbies. However, you need a better book if you are looking how to apply the concepts into computational software.

I made it a few chapters in before I realized that we were basically going to use this book for a reference for our class. If that's the case, then the book is helpful for reference stuff, but there needs to be a better book for introducing the material to non-computational scientists. The math gets hairy quickly and for that a better book would involve the quantum mechanics, which this book does not cover as indepthly. There is a problem with both breadth and depth in this book, that needs to be decided by the author in a subsequent edition. Do we want to explain all of it, or just highlight the "Essentials." Some things I think are essential after 10 years of calculations are not touched on until much later in the book. So, there's give and take with book.