A genetic algorithm has been used to perform a global sampling of the potential energy surface in the search for the lowest-energy structures of unsupported 38-atom Cu–Pt clusters. Structural details of bimetallic Cu–Pt nanoparticles are analyzed as a function of their chemical composition and the parameters of the Gupta potential, which is used to mimic the interatomic interactions. The symmetrical weighting of all parameters used in this work strongly influences the chemical ordering patterns and, consequently, cluster morphologies. The most stable structures are those corresponding to potentials weighted toward Pt characteristics, leading to Cu–Pt mixing for a weighting factor of 0.7. This reproduces density functional theory (DFT) results for Cu–Pt clusters of this size. For several weighting factor values, the Cu30Pt8 cluster exhibits slightly higher relative stability. The copper-rich Cu32Pt6 cluster was reoptimized at the DFT level to validate the reliability of the empirical approach, which predicts a Pt@Cu core-shell segregated cluster. A general increase of interatomic distances is observed in the DFT calculations, which is greater in the Pt core. After cluster relaxation, structural changes are identified through the pair distribution function. For the majority of weighting factors and compositions, the truncated octahedron geometry is energetically preferred at the Gupta potential level of theory.
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics