(86d) Rational Design of Nanoscale Stabilized Oxide Catalysts for the Oxygen Evolution Reaction with OC22

The efficiency of H₂ production via water electrolysis is typically limited by the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through anode catalyst design. More recently, Open Catalyst 2022 (OC22) has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides.¹ When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4,119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1 with adsorption configurations for O* and OH*. To demonstrate the utility of this database, we address one of the major pitfalls in electrocatalyst design: Pourbaix or aqueous stability under harsh reaction conditions. Optimal OER activity generally necessitates reaction conditions such as moderately high temperatures, applied potentials and acidity, all of which disqualifies a large pool of candidate anode catalysts due to aqueous instability. Herein, we propose nanoscale stabilization as a possible solution to expand the pool of existing candidates by improving Pourbaix stability under the prerequisite reaction conditions. To do so, we design a screening framework that can filter materials based on overpotential, surface stability, and stability at the nanoscale using the predicted total energies in our database. From our assessment, we were able to identify 48 and 69 viable candidates for OER under the bulk and nanoscale regime respectively.

¹ Tran et al., ACS Catal. 2023, 13, 5, 3066 - 3084