High-Performance Iridium-Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Optimization...

Area of Science:

• Materials Science
• Electrochemistry
• Catalysis

Background:

• Iridium (Ir) oxides are crucial but expensive catalysts for the oxygen evolution reaction (OER) in acidic media.
• Developing cost-effective alternatives or improving Ir utilization is essential for efficient OER catalysis.

Purpose of the Study:

• To explore the vast phase space of Ir-based mixed oxides for improved OER performance.
• To identify promising dopants that enhance OER activity and stability while reducing Ir content.

Main Methods:

• Utilized a workflow combining machine learning-aided Bayesian optimization and density functional theory (DFT) for materials screening.
• Synthesized and characterized Ir-Mo mixed oxides as thin-film electrocatalysts.

Main Results:

• Identified Molybdenum (Mo) as a promising dopant for acid-tolerant Ir-based OER catalysts.
• Ir-Mo mixed oxides showed ~30 mV lower overpotentials compared to pure Ir.
• Achieved 24% lower Ir dissolution rates in Ir-Mo oxides than in pure Ir.

Conclusions:

• Molybdenum is a highly effective dopant for enhancing Ir-based OER catalysts.
• Machine learning accelerates the discovery and optimization of novel catalytic materials.
• The developed workflow demonstrates the potential of AI in guiding materials science research.