Abstract: Thiolated gold nanoclusters have attracted intense research attention due to their applications in catalysis. Calculations of reactivity descriptors are important to understand catalytic behavior, identify and predict active sites, and ultimately, design new nanocatalysts. Recent advancements in synthesis techniques have paved the way to the production of bimetallic counterparts of these systems, and the availability of a large number of possible isomers makes studies on catalytic properties very challenging.
I will discuss an approach based on density functional theory calculations to explain the catalytic activity of poly-dispersed Ag-alloyed Au25 nanoclusters for CO oxidation reaction. Results of the condensed-to-atom Fukui functions indicate that the reduction in the catalytic activity of Au25‑xAgx(SR)18 is likely related to the reduction in the electron donating capability of the outer shell sites.
I will also present a machine learning model designed to predict adsorption energies of thiolated nanoclusters. Machine learning methods provide a faster route to materials property prediction. The features of the model are based only on geometric properties of adsorbate-free, non-relaxed isomers. The predictive capability of the model was tested using three different Ag-doped clusters: Au25, Au36, and Au133. Features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. The orientation of the ligands have a significant effect on predicting the adsorption energies of Au36, which is less spherical than both Au25 and Au133.