First-principles-based modelling of catalysts is a growing field and the past decade has seen the range of applications for it increase. Improvements in computing power and developments in the areas of machine learning have made many exciting advances possible. The new edition of Computational Catalysis provides an update on the contents of the previous edition whilst introducing new chapters on kinetic Monte Carlo, modelling solvent effects, machine learning for catalyst modelling and design, and modelling complex heterogeneous structures. Written to be accessible to anyone with a familiarity with quantum mechanical methods, this book is a valuable resource for both early career researchers and graduate students.

The field of computational catalysis has existed in one form or another for at least 30 years. Its ultimate goal - the design of a novel catalyst entirely from the computer. While this goal has not been reached yet, the 21st Century has already seen key advances in capturing the myriad complex phenomena that are critical to catalyst behaviour under reaction conditions. This book presents a comprehensive review of the methods and approaches being adopted to push forward the boundaries of computational catalysis. Each method is supported with applied examples selected by the author, proving to be a more substantial resource than the existing literature. Both existing a possible future high-impact techniques are presented. An essential reference to anyone working in the field, the book's editors share more than two decade's of experience in computational catalysis and have brought together an impressive array of contributors. The book is written to ensure postgraduates and professionals will benefit from this one-stop resource on the cutting-edge of the field.