A Flexible Theory for Catalysis: Learning Alkaline Oxygen Reduction on Complex Solid Solutions within the Ag-Pd-Pt-Ru...

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• High-entropy alloys (HEAs) and oxides present a vast chemical space for catalyst discovery.
• Experimental screening of numerous compositions is challenging due to the complexity of identifying optimal catalyst materials.
• Understanding the descriptor-activity relationship is crucial for efficient catalyst design.

Purpose of the Study:

• To establish a link between inferred adsorption energy distributions and experimentally observed electrocatalytic performance.
• To develop a predictive model for the catalytic activity of complex solid solution surfaces.
• To accelerate the exploration and application of high-entropy materials in catalysis.

Main Methods:

• Inferred adsorption energy distributions of *OH and *O on Ag-Pd-Pt-Ru alloy surfaces were calculated.
• A theory-derived model with two adjustable parameters was applied to predict catalytic activity.
• 1582 alloy compositions were screened, and their catalytic activity was predicted.

Main Results:

• A strong coupling was observed between inferred adsorption energy distributions and experimental electrocatalytic performance for the oxygen reduction reaction.
• The predictive model achieved a cross-validated mean absolute error of 0.042 mA/cm².
• Discrepancies between predicted and measured values provided insights into surface compositions during reaction conditions.

Conclusions:

• The study successfully bridges the gap between computational modeling and experimental observation in catalysis.
• The findings offer insights into the fundamental theory of catalysis using high-entropy materials.
• This approach advances the exploration and utilization of high-entropy alloys for catalytic applications.