Data-Driven and Interpretable Machine-Learning for Performance-Determining Interactions Governing C-C Coupling and C₂⁺ Selectivity in Cu-Catalyzed CO₂ Electroreduction

Muhammad Asif; Luqman Hakeem; Yao Chengxi

doi:10.1039/d6cp00893c

January 1, 2026Physical Chemistry Chemical Physics

Data-Driven and Interpretable Machine-Learning for Performance-Determining Interactions Governing C-C Coupling and C₂⁺ Selectivity in Cu-Catalyzed CO₂ Electroreduction

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Key Points

The study aims to enhance the understanding of interactions affecting C-C coupling and C₂⁺ product selectivity in CO₂ electroreduction using machine learning.
Developed an interpretable machine-learning framework to analyze the interactions between catalyst properties and reaction conditions.
Evaluated the performance of the framework in predicting C-C coupling efficiency and selectivity for C₂⁺ products.
Demonstrated significant improvements in predictability of C-C coupling interactions.
Achieved enhanced selectivity for C₂⁺ products through optimized electrochemical parameters.

Implication

Interpretable machine-learning framework improves product selectivity in CO₂ conversion, suggesting enhanced efficiency.

Abstract

Efficient electrochemical conversion of CO₂ into multi-carbon C₂⁺ products remains limited by the complex interplay between catalyst morphology, electrochemical environment, and reaction conditions. Here, we present an interpretable machine-learning framework...

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Cite This Study

Asif et al. (2026) studied this question.

synapsesocial.com/papers/6a0567a8a550a87e60a1fd90 https://doi.org/10.1039/d6cp00893c

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Data-Driven and Interpretable Machine-Learning for Performance-Determining Interactions Governing C-C Coupling and C₂⁺ Selectivity in Cu-Catalyzed CO₂ Electroreduction

Key Points

Implication

Abstract

Discussion

Cite This Study

Data-Driven and Interpretable Machine-Learning for Performance-Determining Interactions Governing C-C Coupling and C₂⁺ Selectivity in Cu-Catalyzed CO₂ Electroreduction

Key Points

Implication

Abstract

Discussion

Cite This Study

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