This screening study demonstrates how the combination of experimental activity data, crystallographic information from structural databases, and first-principles computation of binding energies is used to identify potential new catalysts.
Over the last century, the search for novel catalysts was mostly based on extensive experiments guided by chemical intuition and experience.
In the last two decades, computational approaches have been increasingly applied in the design of catalysts, but the high complexity of this endeavor is daunting.
The present approach demonstrated in this note exploits the concept of Sabatier, namely that an optimal catalyst binds the reactants and products neither too strongly nor too weakly. Rather than trying to simulate the details of molecules interacting with the catalyst surface (which is difficult to characterize), the present approach focuses on the essential interaction, namely the bonding of a metal to neighboring sulfur atoms. Furthermore, it exploits beautifully the richness of information that exists in crystallographic databases by targeted calculations of the key bonding energies in these systems.