Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic

Abstract

A highly accurate high-dimensional neural network potential (HDNNP), trained using more than 180,000 DFT-calculated structures, is used to investigate the structure or realistic Cu−Ag bimetallic particles, as this is the dominant species during the CO2 reduction process. The structural transition of Cu and Ag nanoparticles of increasing size, ranging from hundreds of atoms to tens of thousands of atoms, has been studied. Global optimization shows that all Cu and Ag nanoparticles containing 100 to 1000 atoms have an icosahedral core. Upon increasing the number of atoms to 6000 and 10,000 for Cu and Ag, respectively, the nanoparticles’ structural transitions from icosahedral to truncated-octahedral. For even larger nanoparticles, the (100)/(111) surface ratio in truncated-octahedral structures increases, which finally leads to a transformation into the cuboctahedral shape as observed in experiments.