Identifying the nature
of an active site and understanding the
reaction mechanism at the atomic level are of critical importance
in the design of efficient catalysts for targeted applications. While
extended metal surfaces have been studied extensively for various
catalytic processes and relative activities of different metals were
predicted using volcano relationships, much less is known with regard
to catalysis at metal/oxide interface sites. This Perspective focuses
on recent computational studies that were aimed at understanding catalysis
at such metal/oxide interface sites. The water–gas shift (WGS)
reaction catalyzed by supported Pt catalysts has been chosen as a
model system for such an analysis, since extensive computational studies
with varying sizes of Pt clusters and supports are available and,
thus, by comparison to experimental data, a deeper understanding of
the active sites can be attained. Pt catalysts with different sizes
and shapes stabilized on various supports were found to be active
for the WGS. However, the identification of the exact nature and function
of the active site in these catalysts is still a matter of debate.
Here, we analyzed the computational studies performed using different
active site models, namely, Pt surface models, reducible oxide supported
Pt cluster models, and supported single Pt site models. The focus
of this Perspective is not to summarize computational or experimental
studies of the WGS but to highlight the importance of choosing appropriate
active site models and methods in the computational studies such that
the experimental behavior of these catalysts and the specific roles
of the metal and support can be understood. Furthermore, results obtained
for the WGS mechanism catalyzed by Na-stabilized single Pt cations
supported on TiO2 are discussed and the promotional role
of alkali cations are identified. Finally, we touch upon the importance
of uncertainty quantification to account for the inexact nature of
DFT in the correlation of computational predictions with experimental
observations.