Accelerating the Discovery of Efficient High-Entropy Alloy Electrocatalysts: High-Throughput Experimentation and...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Materials Science

Background:

• High-entropy alloys (HEAs) show promise as electrocatalysts but face challenges in discovery due to complex compositions.
• Efficient electrocatalysts are crucial for the hydrogen evolution reaction (HER), a key process in clean energy technologies.

Purpose of the Study:

• To accelerate the discovery of efficient high-entropy alloy (HEA) electrocatalysts for the hydrogen evolution reaction (HER).
• To establish a procedural approach integrating high-throughput experimentation and data-driven strategies for catalyst development.

Main Methods:

• Rapid preparation of HEA arrays with diverse element combinations and compositions using a model system.
• High-throughput screening of intrinsic activity using scanning electrochemical cell microscopy (SECCM).
• Development of an ensemble machine learning (EML) model to predict catalyst activity based on composition-activity data.

Main Results:

• Generated precise composition-activity datasets for HEA systems.
• EML model successfully predicted activity across the composition subspace.
• Identified and validated two promising groups of HEA electrocatalysts based on model predictions.

Conclusions:

• The integrated approach of high-throughput experimentation and data-driven strategies significantly accelerates HEA electrocatalyst discovery.
• This methodology provides a novel pathway for identifying efficient HEA catalysts for the hydrogen evolution reaction (HER).