Introduction to Computational Materials Science: Fundamentals to Applications, Richard LeSar, Cambridge U. Press, 2013. $95.00 (414 pp.). ISBN 978-0-521-84587-8
Investigating the properties of solid-state matter with computational approaches is an established practice that has received contributions from such fields as condensed-matter physics, physical chemistry, and engineering. Thanks to those many contributing sources, approaches exist to describe solids across an impressive range of length and time scales—from the quantum realm to the mesoscale and beyond. The field, my own research interest for more than 25 years, has attracted increased attention in the past few years, as evidenced by the US Materials Genome Initiative launched in 2011. Its goal is to design and deploy materials to be used for clean energy and national security, among other things. Crucial to the success of the initiative will be a judicious combination of experimental and computational approaches.
Most of the textbooks devoted to computational materials science focus on a particular computational approach, such as molecular dynamics (MD) simulations or electronic-structure calculations. However, researchers in that maturing field need a more comprehensive view that spans a wide range of approaches and their overlap. That is the path taken in Introduction to Computational Materials Science: Fundamentals to Applications. Author Richard LeSar has worked at the forefront of the field at Los Alamos National Laboratory, Ames Laboratory, and Iowa State University. As he explains in the preface, the book is intended as an accessible resource for upper-level undergraduate students and graduate students, and in large part, the book is written at that level. But it also provides an overview that is sure to be valued by practitioners seeking to branch out or wanting a comprehensive review.
The author’s introduction of each topic is clear, and the book’s overall organization makes it quite readable. For example, it covers specialized topics in concise appendices, both at the end of chapters devoted to related topics and at the end of the book. That construction allows the main narrative to flow well without getting bogged down.
The chapters focus on computational approaches that span length and time scales, from the electronic-structure scale to the atomic scale and mesoscale. Curiously, the finite-element modeling approach is not included. The pedagogical material is up to date and mentions cutting-edge approaches that are sometimes left out of other texts. For instance, the chapter on MD simulations includes accelerated MD, and the chapter on interatomic potentials includes some that were developed less than 10 years ago. Other usually omitted topics found in this book include cellular automata and phase-field methods.
Along the way, LeSar makes connections between the models and physical concepts—for example, he relates the random-walk model to diffusion coefficients. He also provides physical materials-science-based examples for topics such as coarse-grained MD, mesoscale models, and phase-field models. Fewer such examples are provided for atomic-scale and electronic-structure methods. That is probably because it is challenging to discuss the mesoscale models in a manner that is divorced from the topics for which they were developed, whereas it is more straightforward to discuss the fundamental physics associated with atomic-scale and electronic-structure methods. And although the book provides many illustrative examples, it contains no problem sets or project assignments.
To enable the inclusion of a wide range of topics, LeSar sacrifices depth in any individual topic, as he acknowledges in his preface. That is acceptable for many courses and is highly desirable for engineering undergraduate students. But graduate students and those seeking to fully understand the associated physics or appreciate the origins of methodological approximations will need to consult other sources.
Some of the material included in the appendices at the back of the book—for example, on units and energy conversions—can be readily found elsewhere. I also question the need for an appendix to introduce readers to materials science: Any readers lacking the basic knowledge summarized too briefly there will most likely struggle with many of the topics covered in the book. In contrast, the appendices that provide overviews of mathematical concepts and classical mechanics will enhance the text for nonexperts.
These criticisms don’t appreciably detract from the overall utility of the book. Introduction to Computational Materials Science offers one of the more accessible recent overviews of an increasingly popular field. I expect it will become a favorite of many students and instructors.
Susan Sinnott is the Alumni Professor of Materials Science at the University of Florida in Gainesville and director of the Cyberinfrastructure for Atomistic Materials Science, a national scientific network and forum.
