Preface to the Second Edition

It has been over 10 years since Computational Catalysis was published in 2013 as part of the RSC Catalysis Book Series. The book was the first in the RSC Catalysis Book Series solely focused on computational modeling of heterogenous catalysis. A key goal of the book was to focus on a pedagogical presentation of key modeling methods used in the field. However, the field of computational catalysis is fast-moving and the past decade has seen several key advancements that justify the need for an updated edition of Computational Catalysis.

In this second edition, we have chosen to retain some key chapters from the first edition, while adding chapters on what we felt where key topics not addressed. As in the first edition, all these methods extend the application of ab initio methods, in particular density functional theory (DFT), to tackle challenges in catalyst modeling. The first two chapters on microkinetic modeling (Lars Grabow, University of Houston) and ab initio thermodynamics (Jason Bray and Bill Schneider, University of Notre Dame) are retained from the first edition. Both chapters have been popular and are among the most downloaded chapters in the first edition. These methods are essential for extending DFT studies of surface reactions to study catalysts under reaction conditions.

The third chapter, written by Mikkel Jørgensen and Henrik Grönbeck from Chalmers University, new in this edition, extends the topic of realistic modeling of catalysts by presenting the powerful kinetic Monte Carlo (kMC) technique. Unlike microkinetic modeling, kMC does not assume a mean-field approximation but instead directly follows each surface process explicitly. Such an approach is especially important when the surface is heterogenous and diffusion between different types of sites plays an important role. The kMC method applied to surface reactions is presented using some simple examples before CO oxidation on Pt(111) and Pt nanoparticles are discussed.

The interest in catalysis at solid–liquid interfaces has grown substantially over recent decades, in part motivated by growing interest in bio-derived species conversion and electrocatalysis. Techniques for the inclusion of solvation within DFT models have grown along with this interest. In Chapter 4, Alex Maldonando (University of Pittsburgh), John Keith (University of Pittsburgh), Kathleen Schwarz (NIST), and Ravishankar Sundararaman (Rensselaer Polytechnic Institute) present the main approaches and challenges in incorporating solvation in first-principles calculations in catalysis.

Chapter 5 written by co-editor Mike Janik (Penn State University) and his students focuses on DFT-based methods to model electrocatalytic surface reactions. This chapter is an updated version of Chapter 3 in the first edition (written by Kuan-Yu Yeh and Mike Janik) with updates on calculation of activation barriers and further details on modeling cyclic voltammetry experiments.

One of the major changes in the past decade has been the explosion in applying machine learning (ML) to a range of applications, and heterogenous catalysis has been no exception. In Chapter 6, Zachary Ulissi and John Kitchin with their students from Carnegie Mellon University present an overview of the practical application of ML in catalysis. The chapter covers practical tips and pitfalls in applying ML to catalysis, with an emphasis on best practices for approaching problems with ML.

The chapter on reactive force-field (ReaxFF) potentials in catalysis written by Adri van Duin and co-workers is retained as Chapter 7. ReaxFF has been applied to a wide range of materials science problems and continues to draw interest in the field of catalysis for the ability to simulate large complex catalyst systems directly.

The final chapter in the book, written by Peng Wang and Thomas Senftle from Rice University, focuses on modeling complex structures that are found in real-world catalysts. Practical approaches to building up models from simple (single crystal surface) to complex (supported clusters) are discussed along with the challenge of metastable structures that can play an important role in catalysts.

Our hope is that the new edition of Computational Catalysis will provide young researchers a useful transition to key techniques in this rapidly growing field. We thank the authors for taking on the challenge of writing these chapters in a manner that is suitable for researchers looking for an accessible entry point to computational catalysis.

Aravind Asthagiri and Michael Janik