The presentation will describe different types of heterogeneous catalyst models, different calculation types and their integration with experimental results.
Molecular modeling plays a valuable role in developing better heterogeneous catalysts and optimizing reaction conditions. Its impact is greatest when DFT calculations are tightly integrated with experimental catalyst testing and characterization. This presentation will outline common heterogeneous catalyst models—such as clusters and periodic surfaces—and describe how quantum‑chemical and hybrid QM/MD approaches can be combined with experimental data.
Our work shows that experiments designed specifically to support and validate DFT models are essential. Once validated, these models help interpret experimental observations under realistic reaction conditions. Techniques such as infrared and Raman spectroscopy provide information on stable surface species, while DFT calculations identify their structures, energetics, and vibrational signatures. More importantly, DFT enables the prediction of reaction pathways and short‑lived intermediates that are rarely detected experimentally.
This integrated experimental–computational strategy has been successfully applied to multiple catalytic systems. Several case studies—ranging from selective hydrocarbon oxidation to biomass hydrodeoxygenation and natural‑gas‑to‑aromatics conversion—will illustrate how this approach provides deeper insight into catalytic mechanisms and guides the design of improved catalysts.
