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

Jingnan Su¹, Zhiheng Ji², Dan Jiang³

¹Research Center for Quantum Physics and Technologies, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China.

The Journal of Physical Chemistry Letters

|November 24, 2025

Summary

This summary is machine-generated.

We developed a DFT-ML framework to discover advanced copper catalysts for CO2 reduction. This accelerates finding efficient catalysts, significantly improving CO2RR performance over existing benchmarks.

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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.