Coordination-Engineered Double-Atom Catalysts with Inverse Sandwich Structures for CO₂ Reduction: A Combined DFT and...

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• Electrocatalysts are crucial for CO2 reduction reaction (CO2RR).
• Coordination engineering offers a pathway to enhance catalyst performance.
• Discovering efficient catalysts requires advanced computational methods.

Purpose of the Study:

• To accelerate the discovery of highly active and selective copper-based double-atom catalysts (DACs) for CO2RR.
• To establish a robust density functional theory (DFT)-machine learning (ML) framework for catalyst screening.
• To identify promising DACs with inverse sandwich structures through a multi-step protocol.

Main Methods:

• Utilized a DFT-ML framework combining density functional theory calculations and machine learning models.
• Implemented a four-step screening protocol: stability, CO2 adsorption, selectivity, and activity.
• Developed an interpretable XGBoost model based on five key descriptors for predicting catalytic activity.

Main Results:

• Identified 18 promising Cu-based DAC candidates from 162 structures, outperforming Cu(111) and Cu-N4.
• Screened Ag-based and Cu-based DACs with mixed C/N/B coordination, yielding 9 and 153 candidates, respectively.
• DFT validation confirmed the reliability and predictive power of the developed ML model.

Conclusions:

• Coordination-engineered DACs show significant potential for efficient CO2RR.
• The developed DFT-ML strategy is a robust and transferable approach for accelerating catalyst discovery.
• This work paves the way for designing next-generation electrocatalysts for CO2 conversion.