Computational insights into the physico-chemical properties of pure and single-atom copper-indium sub-nanometre clusters: a DFT-genetic algorithm approach.

Catalysis involving Cu-In nanoparticles represents an exciting area of technological advancement. However, our fundamental grasp of the mechanisms governing mixing within Cu-In clusters at the sub-nanometer scale and their gas-phase physicochemical properties remains inadequate. We have determined the global minima for gas-phase Cu-In clusters containing 3 to 10 atoms using the Mexican Enhanced Genetic Algorithm in conjunction with density functional theory. Simulations were also conducted for Cu and In atoms and their dimers at the same theoretical level. Comparative analyses were performed between mixed Cu-In systems and their pure counterparts, with pure Cu and In clusters being modeled up to 13 atoms. The findings indicate a 2D-3D transition for pure Cu clusters at 7 atoms, while for pure In clusters, this transition occurs at 5 atoms. For Cu-In nanoalloys, both cluster size and doping have been found to significantly and non-linearly impact cluster structures. Stability assessments, including binding energies, second differences in energy, and mixing energies, were used to evaluate the energetics, structures, and segregation tendencies of sub-nanometer Cu-In clusters. The most stable composition, as indicated by mixing energies, is achieved when the Cu to In ratio is equal or nearly equal. The HSE06 spin-projected band structure reveals that In