The efficiency of H2 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) framework 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 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. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction conditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime respectively.
@article{arxiv.2311.00784,
title = {Rational design of nanoscale stabilized oxide catalysts for OER with OC22},
author = {Richard Tran and Liqiang Huang and Yuan Zi and Shengguang Wang and Benjain M. Comer and Xuqing Wu and Stefan J. Raaijman and Nishant K. Sinha and Sajanikumari Sadasivan and Shibin Thundiyil and Kuldeep B. Mamtani and Ganesh Iyer and Lars C. Grabow and Ligang Lu and Jiefu Chen},
journal= {arXiv preprint arXiv:2311.00784},
year = {2024}
}