Neural network – assisted search for active site ensembles in dilute bimetallic nanoparticle catalysts

The activity of dilute bimetallic nanoparticle catalysts depend on the gas and temperature treatment of the catalyst prior to reaction. The pretreatment drives the redistribution of the reactive dilute species between the surface, subsurface, and bulk, resulting in significant changes to the distribution of atoms. As a result these catalysts enable many reaction pathways. Current methods of reaction modeling require the assumption of an active configuration a priori, but tools for the verification of such assumptions are presently lacking. Here we present a method that combines the in situ measurements of X-ray absorption fine structure spectroscopy (XAFS) and catalytic activity, by way of neural network modeling, to create starting configurations for theoretical reaction modeling. The focus of this method is to facilitate the search for catalytically active structures that provide agreement between experimentally measured and theoretically calculated activities. We demonstrate its utility by using a series of theoretically simulated structures and their corresponding reaction pathways for hydrogen dissociation over dilute Pd-Au catalysts.