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.
Documenting the many advances made possible by improved computing power and new developments in approaches such as machine learning, this new edition provides an introduction to, and description of, the up-to-date techniques for first-principles-based modelling of catalysts.
Computational Catalyst Screening
First-principles Thermodynamic Models in Heterogeneous Catalysis
Kinetic Monte Carlo Simulations to Study Reactions over Nanoparticles
Solvation Effects in First-principles Calculations for Catalysis
Density Functional Theory Methods for Electrocatalysis
Practical Application of Machine Learning in Catalysis
A ReaxFF Reactive Force-field for Proton Transfer Reactions in Bulk Water and Its Applications to Heterogeneous Catalysis
Addressing Challenges in Modeling Complex Structures in Heterogeneous Catalysis