Publication date: 21st July 2025
The transition to sustainable energy systems requires the development of efficient and robust catalysts capable of converting renewable electricity into chemical energy. Among these, bimetallic catalysts have garnered significant attention for their ability to enhance catalytic performance through synergies between metal constituents.1,2 However, the complex nature of these catalysts—characterized by site-occupancy disorder—poses challenges for accurately modeling adsorption behaviors and identifying active sites. Traditional computational approaches, which typically rely on a single configuration per alloy composition, overlook the inherent structural diversity of real bimetallic catalysts under reaction conditions.
In this communication, we present a framework for addressing the statistical nature of adsorption on binary alloy surfaces, with a focus on Cu-based bimetallic alloys for C2+ product formation from CO2 electrolysis. By introducing the concept of the "effective cutoff radius", we establish a method to efficiently sample the configurational ensemble that accurately captures the diversity of binding energies present in a Cu-based bimetallic system. This approach overcomes the limitations of traditional models that assume a uniform surface and provides a more accurate and cost-effective means of mapping adsorption chemical space on alloys. Our findings not only allow the rationalization of chemical trends in the field of CO2 electroreduction but are envisioned to accelerate the rational design of bimetallic catalysts, offering a robust foundation for efficient computational screenings of complex materials in the future and their optimization in CO2 electrolysis and related energy conversion applications.
We acknowledge the support from the European Union's Horizon 2021 programme under the Marie Skłodowska-Curie Doctoral Networks (MSCA-DN) grant agreement No 101072830 (ECOMATES). The Research IT Unit of Trinity College Dublin is also acknowledged for the generous provision of computational facilities and support.