Heterogeneous catalytic reactions are sensitive to atomic-scale complexities arising under in-operando conditions, such as variations in surface morphology, composition, and adsorbate-adsorbate interactions. To understand the subtle interplay of these disparate phenomena, it is necessary to develop a comprehensive, atomic-scale representation of the catalyst under reaction conditions. However, the presence of many atomic-scale configurations, and the difficulty in systematically generating and analyzing these representations, makes this exercise highly non-trivial. To address this challenge, we present a screening workflow, employing graph-based enumeration and prediction strategy, that is both highly flexible and accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and variations in the substrate morphologies. We utilize this workflow to successfully sample the large number of atomic configurations to identify the most relevant surface representation stable reaction conditions. We demonstrate the utility of the workflow to determine the relevant high coverage configurations for the cases of adsorbed NO* on Pt3Sn (111) surface and that of adsorbed OH* on irregular Pt (221). The first case presents complexities because of the presence of an alloy and the strong binding nature of NO*, while the second case presents complexities arising from the presence of step defects and the ability of OH* to form inter-molecular hydrogen bonds. Using these two test cases as representative examples, we demonstrate the generalizability of our workflow to identify the most relevant atomistic configurations by utilizing only 10% of the total possible configurations. Such an approach can serve as a starting point for developing detailed atomic description of complex catalyst surfaces under in-operando conditions, ultimately leading to fundamental insights into factors that govern heterogeneous catalysis in structurally and chemically complex environments.

Graph-based machine learning framework for modeling of complex heterogeneous catalyst surfaces

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.