In this paper, we explore the catalytic CO₂ reduction process on 13-atom bimetallic nanoclusters with icosahedron geometry. As copper and nickel atoms may be positioned in different locations and either separated into groups or uniformly distributed, the possible permutations lead to many unnecessary simulations. Thus, we have developed a machine learning model aimed at predicting the energy of a specific group of bimetallic (CuNi) clusters and their interactions with CO₂ reduction intermediates. The training data for the algorithm have been provided from DFT simulations and consist only of the coordinates and types of atoms, together with the related potential energy of the system. While the algorithm is not able to predict the exact energy of the given complex, it is able to select the candidates for further optimization with reasonably good certainty. We have also found that the stability of the complex depends on the type of central atom in the nanoparticle, despite it not directly interacting with the intermediates.

We present a DFT analysis of the role of the Cu-Ni synergistic effect for the CO₂ reduction to C₂H₄, in comparison to the pure Cu catalyst. The analysis is focused on the thermodynamic stability of reactive intermediates along the proposed pathway of C₂ species formation. We have observed that the potential needed for the reaction decreases with the addition of Ni in the investigated model. In addition, we have observed the differences in the preferred pathway based on the significant differences in stability of the reactive intermediates depending on th Cu:Ni ratio. The results suggest that despite the fact the Cu surface is always exposed, and it is the only one that is able to directly interact with the intermediates, the presence of the Ni in the underlying sections of the crystal is significant enough to change the mechanism of the reaction.