# A Machine Learning‐Guided Search of Cu‐Based Bimetallic Alloys That Favor CO Dimerization in CORR

## TL;DR

This paper uses machine learning to find copper-based alloys that improve CO2 electroreduction into useful multicarbon products by enhancing CO dimerization.

## Contribution

A novel ML framework with interpretable features screens thousands of CuM alloy configurations to identify promising catalysts for CO dimerization.

## Key findings

- CuAg, CuAl, CuAu, CuZn, CuIn, and CuGa are promising for CO dimerization.
- CuGa shows lower activation barriers for CO dimerization compared to pure Cu.
- The ML models were validated using DFT calculations for accuracy.

## Abstract

The rational design of Cu‐based bimetallic catalysts for the electrochemical reduction of CO2 into multicarbon (C2/C2+) products critically depends on tuning the CO adsorption strength, which governs C–C coupling selectivity. However, systematically exploring the vast configurational space of CuM alloys through density functional theory (DFT) is computationally prohibitive. We developed a machine learning (ML) framework to predict CO adsorption behavior on Cu‐based bimetallic surfaces using a physically interpretable feature set, including geometric descriptors (nearest‐neighbor distance, coordination number) and elemental properties (electronegativity, ionization energy), which collectively determine the local electronic environment of the CO binding sites. A two‐step ML protocol, combining a Gradient Boosting Classifier to identify stable adsorption sites and a Gradient Boosting Regressor to predict their adsorption energies, was trained on DFT data for 15 CuM(111) and CuM(100) systems, resulting in a total of 1,515 structures. The models were then applied to screen 29 CuM alloys, encompassing about 91,000 adsorption sites across multiple surface concentrations and configurations of the alloying atoms. The screening identified CuAg, CuAl, CuAu, CuZn, CuIn, and CuGa as promising candidates for promoting CO–CO coupling, the rate‐determining step toward C2 product formation. Among these, CuGa was selected for further validation as a representative system, having received far less attention in the CO2RR literature than other well‐studied alloys (CuAg, CuAl, and CuAu). Constant‐potential DFT calculations confirmed the ML predictions, revealing that CO dimerization on CuGa(100) proceeds with a more favorable reaction energy and an activation barrier about 0.2 eV lower than on pure Cu(100).

A machine learning–driven screening identifies Cu‐based bimetallic alloys that enhance CO–CO coupling in CO2 electroreduction. Using interpretable geometric and elemental features, the framework predicts CO adsorption behavior across thousands of sites, highlighting CuGa, CuAg, CuAl, CuAu, CuZn, and CuIn as promising catalysts for selective C2 formation.© 2026 WILEY‐VCH GmbH