(223b) Toward the Design of Catalysts

A chemical kinetic model, although an essential ingredient of the
understanding of catalytic behavior, does not contain sufficient knowledge to
predict improved catalysts.  The inherent chemistry of families of catalysts
governs relationships between the various rate and equilibrium constants and
thus provides the key to the optimal catalyst formulation.  We call our
strategy at Purdue for identifying and utilizing this information Discovery
Informatics.  This approach to catalyst design envisions building a
quantitative forward model that links descriptors of the catalyst
chemistry, through a microkinetic model, to catalyst performance.  While such a
model has intrinsic value, that value will be powerfully leveraged if the model
can be used to predict performance of new materials chosen in a guided
stochastic search of the descriptor space.  In principle, comparison of the
measured performance to that predicted for the ?fittest? materials chosen in
that inverse search then provides direction for model improvement.
Iteration of the prediction, measurement, correction cycle would produce
evolution toward an optimal catalyst.  In this process, the forward model
becomes the repository for catalytic knowledge extracted from the experiments.

Progress in developing this
approach will be illustrated with two examples: homogeneous single site olefin
polymerization catalysis and heterogeneous water gas shift (WGS) catalysis.  In
the first case, aryloxide and Cp(Cp^*) ligated Ti and Zr catalysts
present well defined catalytic sites and a team of researchers has combined DFT
computation with kinetic experiments to produce a detailed model that
correlates the 1-hexene propagation rate with ion pair separation energies and
the cone angles available for monomer docking.  Extensions to other systems
have included a population balance kinetic model that can use molecular weight
distribution data to fit additional kinetic parameters.  Heterogeneous catalyst
systems add to the complexity of the design problem because of the many potential
catalytic sites often available.  Recent literature has shown remarkable
success using scaling laws and Brønsted, Evans, Polyani relations to identify
optimal catalysts for reactions that are relatively insensitive to catalyst
structure. Our work with supported Pt, Pd and Au water gas shift catalysts
shows effects of support, alloying and particle size that on the one hand show
the critical importance of good kinetic data in narrowing the focus for
theoretical analysis and illustrate the need for ?scalable? theories to account
for structural details.