Rational design of nanoscale stabilized oxide catalysts for OER with OC22

TL;DR

This study leverages a large DFT dataset and machine learning models to identify promising oxide catalysts for oxygen evolution reaction, optimizing for stability, cost, and efficiency at nanoscale and bulk levels.

Contribution

It introduces a comprehensive screening framework using pre-trained models on OC22 data to discover stable, cost-effective oxide catalysts for OER at nanoscale and bulk.

Findings

Identified 122 bulk oxide candidates for OER.

Found 68 nanoscale oxide candidates suitable for OER.

Demonstrated the utility of machine learning in catalyst screening.

Abstract

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) 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.