High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO₂ Activation

CO₂ activation is an integral component of thermocatalytic and electrocatalytic CO₂ conversion to liquid fuels and value-added chemicals. However, the thermodynamic stability of CO₂ and the high kinetic barriers to activating CO₂ are significant bottlenecks. In this work, we propose that dual atom alloys (DAAs), homo- and heterodimer islands in a Cu matrix, can offer stronger covalent CO₂ binding than pristine Cu. The active site is designed to mimic the Ni-Fe anaerobic carbon monoxide dehydrogenase CO₂ activation environment in a heterogeneous catalyst. We find that combinations of early transition metals (TMs) and late TMs embedded in Cu are thermodynamically stable and can offer stronger covalent CO₂ binding than Cu. Additionally, we identify DAAs that have CO binding energies similar to Cu, both to avoid surface poisoning and to ensure attainable CO diffusion to Cu sites so that the C-C bond formation ability of Cu can be retained in conjunction with facile CO₂ activation at the DAA sites. Machine learning feature selection reveals that the more electropositive dopants are primarily responsible for attaining the strong CO₂ binding. We propose seven Cu-based DAAs and two single atom alloys (SAAs) with early TM late TM combinations, (Sc, Ag), (Y, Ag), (Y, Fe), (Y, Ru), (Y, Cd), (Y, Au), (V, Ag), (Sc), and (Y), for facile CO₂ activation.