(205c) Electronic Structure Engineering in Heterogeneous Catalysis: From Simple Analytical to Machine Learnt Models of Chemisorption on Alloys

While one metal might be chemically inert for a particular catalytic chemical transformation, its first neighbor in the periodic table might be overly reactive and equally inefficient. The main reason of this is that neighboring metals in the periodic table exhibit very large differences in binding energies of various adsorbates (as large as ~1 eV (~100 kJ/mol)). As a consequence, the periodic table does not provide enough flexibility in identifying optimal catalysts. One avenue to address this problem is to fine-tune the chemical reactivity of surface atoms in metals by changing their local coordination by for example creating metal alloys. The phase space of alloys is immense, and it is almost impossible to screen through even a small fraction of this potential alloys using experimental studies or even quantum chemical calculation on model systems.

I will discuss our work on developing simple predictive models of chemical reactivity for alloys. More specifically, we analyzed how a change in the local coordination of metal surface atoms due to alloying with another metal changes the local chemical reactivity of the metal site. The central finding of our studies is that it is possible to reliably predict the change in the local electronic structure of an active site induced by the formation of an alloy, and the change in the local chemical reactivity, based on very simple analytical models which employ only easily accessible physical characteristics of the elements that form the alloy (mainly their electronegativity, distance between neighboring atoms, and the spatial extend of metal orbitals). We have also extended these simple models and improve their accuracy by using advanced machine learning tools. We will compare out findings obtained using these different approaches and shed light on the parameters that play the critical role in chemisorption on metal surface.

We show how these predictive structure-reactivity relationships can be employed to rapidly screen through large libraries of alloy compositions and structures to identify optimal alloy catalysts. We describe our findings by focusing on an example of the design of optimal Pt alloy electrocatalysts for the oxygen reduction reaction (ORR) in fuel cell cathodes.

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