Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*

Area of Science:

• Materials Science
• Electrochemistry
• Computational Materials Science

Background:

• Developing active, selective, and stable catalysts is crucial for sustainable energy conversion technologies.
• High-entropy alloys (HEAs) present a vast compositional landscape for designing advanced catalysts.
• Exploring this vast compositional space efficiently requires advanced computational tools.

Purpose of the Study:

• To employ Bayesian optimization coupled with density functional theory (DFT) to predict optimal HEA compositions for the oxygen reduction reaction (ORR).
• To minimize the number of compositions sampled for identifying highly active HEAs.
• To validate computationally predicted optimal compositions through experimental testing.

Main Methods:

• Utilized Bayesian optimization integrated with a DFT-based model to predict catalytic activity.
• Focused on two HEA systems: Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru.
• Performed DFT scrutiny and experimental validation of the predicted optimal compositions.

Main Results:

• Identified highly active HEA compositions for the ORR using a significantly reduced number of simulations.
• Experimental validation confirmed optimal catalytic activities for specific binary alloys, including Ag-Pd, Ir-Pt, and Pd-Ru.
• Determined that approximately 50 experiments are sufficient for optimizing the compositional space of these multimetallic alloys for ORR.

Conclusions:

• Bayesian optimization is an effective tool for navigating the vast compositional space of HEAs for catalyst discovery.
• This approach drastically reduces the experimental effort required for optimizing multimetallic alloy catalysts.
• The findings provide valuable insights into accelerating the development of efficient electrocatalysts for energy conversion.