Deciphering the structure-activity-selectivity relationship of high-entropy alloys for CO₂ reduction via...

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• High-entropy alloys (HEAs) offer diverse active sites for catalysis.
• Understanding structure-performance relationships in HEAs is challenging.
• The CO2 reduction reaction (CO2RR) is crucial for sustainable energy.

Purpose of the Study:

• To systematically explore the structure-activity-selectivity relationship of HEAs for CO2RR.
• To develop a machine learning framework combined with DFT for HEA catalyst design.
• To identify promising HEA candidates for efficient and selective CO2RR.

Main Methods:

• Density functional theory (DFT) computations to calculate binding energies.
• Machine learning (ML) framework for statistical analysis and descriptor construction.
• High-throughput screening of a large HEA compositional space.

Main Results:

• HEAs can deviate from traditional scaling relationships found in pure metal catalysts.
• An activity-selectivity tradeoff exists in HEAs, influenced by unpaired d electron numbers.
• Developed descriptors accurately predict HEA performance and identified 10 promising candidates.

Conclusions:

• The study provides quantitative criteria for rational HEA catalyst design for CO2RR.
• A systematic approach was established to unravel structure-performance relationships in complex alloy systems.
• This work accelerates the discovery of advanced catalysts for CO2 conversion.