The confirmation of atomic theory in the early 1900s, and the creation of quantum mechanics as a way to explain subsequent experimental results at the atomic level, have led to a merging of chemistry and physics at that level. Chemists view a chemical reaction as the combination of substances to form new substances, while physical chemists calculate how the properties of the atomic components of the substances lead to the results of the reaction. Quantum mechanics describes those properties and how subatomic components of atoms behave when brought together. For example, a reaction under study involves two substances in the presence of a catalyst, the last greatly increasing the speed of the reaction. One goal of the computation is to calculate the energy released when the reaction occurs. Accurate predictions via computation for known situations permit the computational system to be used to make predictions of situations that have not been tested. The value gained is that computation is generally less costly than laboratory experiments. This is important when investigating, say, finding a new drug. If the experimenter can do computer simulations to narrow down the number of lab trials needed, effort and cost are lowered.

If one deals with systems of a few atoms, quantum theory can be applied to all components, resulting in an accurate description of the chemistry. A problem arises when calculating in the biological realm where the system to be examined is comprised of thousands of atoms. In this realm, the quantum mechanical equations become unmanageable and complex; that is, computations would involve treating every single component, electrons and nuclei, of every atom, requiring computing resources far above what can be brought to bear with current technology. Although there will be ongoing benefits from Moore’s law in providing ever more powerful computers, with current computing resources, calculations involving about 2,000 atoms are at the forefront of today’s research. Considering that a typical human cell contains on the order of 100 trillion atoms (estimates vary), one can see there is a long way to go in this endeavor. The goal is thus finding approximations that reduce the computational complexity but provide accurate results.

Given that background, we have this book, a PhD thesis describing the author’s research into ways to improve the accuracy in calculating the energies of reactions involving large numbers of atoms relative to previous work, beginning with a test case that has been studied in the lab and whose experimental results are well known. This provides a basis for measuring the effectiveness of new approaches to computation. The author’s contribution to the area is his research showing that careful analysis of the materials under investigation and appropriate choice and combination of methods of calculation, comprised of a mix of classical and quantum techniques, are required to achieve accurate results and reasonable computational efficiency. He demonstrates the use of density functional theory (DFT). DFT is an approach that uses quantum theory to examine charge densities at points in space rather than requiring treatment of the behavior of individual particles, which is much more computationally expensive. This simplification permits the use of algorithms that scale linearly in the number of atoms being studied. Using DFT can permit using quantum mechanical calculations over a higher percentage of the sample when analyzing a system, resulting in increased accuracy. Calculations on a sample size of over 2,000 atoms can be effectively done using these techniques.

The dissertation is beautifully written in clear, precise language. It reads, in fact, almost as a textbook, providing in successive chapters the history, theory, and computational methods as background, then proceeding to discussing a validation computation followed by a detailed analysis of the importance of analyzing boundary conditions, then concluding with an analysis based on total use of DFT, and final thoughts. Anyone interested in this area can learn a great deal from this work.