Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design

Abstract

Scaling relationships are theoretical constructs that relate the binding energies of a wide variety of catalytic intermediates across a range of catalyst surfaces. Such relationships are ultimately derived from bond order conservation principles that were first introduced several decades ago. Through the growing power of computational surface science and catalysis, these concepts and their applications have recently begun to have a major impact in studies of catalytic reactivity and heterogeneous catalyst design. In this review, the detailed theory behind scaling relationships is discussed, and the existence of these relationships for catalytic materials ranging from pure metal to oxide surfaces, for numerous classes of molecules, and for a variety of catalytic surface structures is described. The use of the relationships to understand and elucidate reactivity trends across wide classes of catalytic surfaces and, in some cases, to predict optimal catalysts for certain chemical reactions, is explored. Finally, the observation that, in spite of the tremendous power of scaling relationships, their very existence places limits on the maximum rates that may be obtained for the catalyst classes in question is discussed, and promising strategies are explored to overcome these limitations to usher in a new era of theory-driven catalyst design.

Rational Design Rules for Molecular Water Oxidation Catalysts based on Scaling Relationships

“…However, because these different intermediates bind to the catalyst in a similar way, their binding energies cannot be optimized individually. These energetic relationships between catalytic intermediates are known as scaling relationships . Due to the scaling relationships, the best catalyst has a non‐zero thermodynamic overpotential, and is therefore sub‐optimal.…”

Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

“…So far, the focus was put on predicting the synthesis process and only over the past few years, research has focused on developing descriptorbased approaches and scaling relationships for catalytic applications so far, [15][16][17][18][19][20] methods that have been applied successfully to predict the activity of compounds in heterogeneous surface catalysis. 7 However, previous work in the field has focused on a very narrow class of possible metal sites and an accurate, general approach has not been achieved.…”

Machine Learning for Computational Heterogeneous Catalysis

“…The analysis of linear regression coefficients has brought insights about the role of the bond order between adsorbate and surface in scaling relations using the to C scaling as an example (Figure ). This observation has enabled a fundamental understanding of the parameters that define catalytic activity . Linear regression has furthermore been successfully used to predict the d ‐band center within a mean absolute error of about 0.26 eV from a set of experimental elementary descriptors .…”