The efficiency of $H_2$ production via water electrolysis is typically limited to the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. 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 of 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 materialas 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 full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts under a bulk and nanoscale regime for OER by assessing the price, thermodynamic stability, and resistance to corrosion, surface stability, and overpotential. Finally we verified the overpotentials and reaction energies of the final candidate catalysts using DFT. From our assessment, we were able to identify 48 and 69 viable candidates for OER under the bulk and nanoscale regime respectively.
