Rational design of nanoscale stabilized oxide catalysts for OER with OC22

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Water electrolysis for hydrogen production is hindered by the slow oxygen evolution reaction (OER).
• Developing efficient anode catalysts is crucial for improving OER kinetics.
• The Open Catalyst 2022 (OC22) dataset offers density functional theory (DFT) calculations for OER intermediates on oxide surfaces.

Purpose of the Study:

• To create an interpolated database of total energy predictions for OER intermediates on oxide surfaces.
• To develop a flexible screening framework for identifying promising OER anode catalysts.
• To evaluate candidate catalysts based on stability, cost, overpotential, and metastability.

Main Methods:

• Interpolation of DFT total energy predictions from the OC22 dataset for 4119 oxide materials.
• Utilizing pre-trained graph neural network models for energy predictions.
• Implementing a screening framework considering Pourbaix stability, material cost, overpotential, and metastability.

Main Results:

• Generated a comprehensive database of OER surface intermediate energies for numerous oxide materials.
• Identified 122 viable candidate anode catalysts for OER in the bulk regime.
• Identified 68 viable candidate anode catalysts for OER in the nanoscale regime.

Conclusions:

• The developed database and screening framework effectively identify potential OER anode catalysts.
• This approach accelerates the discovery of efficient materials for water electrolysis.
• The study highlights promising oxide candidates for enhanced hydrogen production.