The determination of the structure of heterogeneous catalytic systems is a key aspect for a detailed understanding of the nature of active sites and for the rational design of efficient catalysts. However, the situation for the catalytic active sites is complicated and the determination of their structure is not straightforward. Indeed, catalysts are not static but dynamic, fluxional, metastable and they strongly evolve under reaction conditions, creating new active sites, not present for the as prepared catalysts. The best active sites are not found on the most stable structure of the catalysts, but require first its restructuring. The lecture will present two examples, based on quantum chemical calculations. We will focus on the modelling of small Pt clusters (Pt7 to Pt13) under hydrogen pressure and on their reactivity for alkane dehydrogenation [1,2]. The approach combines Density Functional Theory, high-dimensional Neural Networks and evolutionary techniques. It also includes grand canonical global optimization to study variable amount of adsorbed hydrogen and novel constrained global optimization methods to determine the structure of the cluster active site that provides the lowest barrier for the reaction. We show, with methane activation on supported Pt clusters and by an explicit sampling of cluster configurations at the transition state, that important restructuring is required to reach the most active transition state. The capability of the cluster to reconstruct, simultaneously with the C-H dissociation, is a key aspect for catalytic activity [3].

Active site restructuring on supported catalytic clusters

ACS Spring 2022

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