Structural optimization of Pt–Pd–Au trimetallic nanoparticles by discrete particle swarm algorithms

Trimetallic nanoparticles have received enormous attention due to their multifunctional catalytic activities. Their surface structures strongly determine their catalytic performances, therefore an investigation on their stable structures is of great importance for understanding the catalytic activity. In this article, we have employed an improved discrete particle swarm optimization algorithm to systematically explore the structural stability and segregation behavior of tetrahexahedral Pt–Pd–Au trimetallic nanoparticles. The exchange probability was introduced to decrease computational cost and to avoid falling into local optima. The simulation results reveal that Pt atoms tend to occupy the interior, while both Pd and Au atoms preferentially segregate to the surface. Furthermore, Au atoms exhibit stronger surface segregation than Pd ones, and the segregative behavior is less pronounced in larger nanoparticles. Besides, the distribution of surface atoms has been further examined by the analyses of coordination number. This study provides a fundamental perspective on structural features and segregation behavior of trimetallic nanoparticles.