Self-learning kinetic Monte Carlo Simulations of Self-diffusion of small Ag clusters on Ag (111) surface

TL;DR

This study employs self-learning kinetic Monte Carlo simulations to investigate the self-diffusion of small Ag clusters on Ag(111), revealing key diffusion mechanisms, size-dependent behaviors, and comparing activation barriers with DFT calculations.

Contribution

It introduces a self-learning kinetic Monte Carlo approach to automatically identify diffusion processes for small Ag clusters on Ag(111).

Findings

Diffusion coefficients vary with cluster size.

Multiple diffusion mechanisms, including concerted and multi-atom processes, are identified.

Activation barriers from simulations are compared with DFT results.

Abstract

The self-diffusion of two-dimensional small Ag islands (containing up to $10$ atoms) on Ag(111) surface has been studied using and self-learning kinetic Monte Carlo [J. Phys.: Condens. Matter 24, 354004 (2012)] simulations. A variety of concerted, multi-atom and single-atom processes were automatically revealed in these simulations. The size dependence of the diffusion coefficients, effective energy barriers as well as key diffusion processes responsible for island diffusion are reported. In addition, we have compared activation barriers for concerted diffusion processes with those obtained from Density Functional Theory (DFT) calculations.