Structural studies of Au–Pd bimetallic nanoparticles by a genetic algorithm method

Abstract

Metallic nanoparticles have attracted particular interests due to their excellent electronic, catalytic and optical properties over the past decades. Atomic-level understanding of structural characteristics of metallic nanoparticles is of great importance for their syntheses and applications because the structural characteristics strongly determine their chemical and physical properties. In this article, we systematically investigated the structural stability and structural features of Au–Pd nanoparticles by using the genetic algorithm with the quantum correction Sutton–Chen potentials. Layered coordinate ranking method and an effective fitness function have been introduced into the genetic algorithm to enhance its searching ability of low-energy configurations. Here were addressed eight representative nanoshapes including single-crystalline and multiple-twinned structures. The results reveal that the developed genetic algorithm exhibits superior searching ability. In all polyhedra, the truncated octahedron possessed the best stability, while the icosahedron did the worst. Moreover, segregation of Au to the surface and that of Pd to the core were disclosed in these polyhedral Au–Pd nanoparticles. Particularly, for Au composition of 50%, the optimized structures of Au–Pd nanoparticles were predicted to exhibit core–shell structures.