A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)

TL;DR

This paper extends a self-learning kinetic Monte Carlo model to three dimensions, enabling more accurate simulations of surface diffusion and growth phenomena on Ag(111) surfaces by using an initial database of activation energies.

Contribution

The work develops a 3D self-learning KMC model that reduces computational effort through an initial database, improving simulation accuracy for surface processes.

Findings

Successfully applied to Ag monolayer island diffusion.

Accurately modeled homoepitaxial growth of Ag on Ag(111).

Demonstrated efficiency gains with initial database setup.

Abstract

The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme. This work expands the original two-dimensional method to three dimensions. The concomitant huge increase in the number of rate calculations on the fly needed can be avoided by setting up an initial database, containing exact activation energies calculated for processes gathered from a simpler KMC model. To provide two representative examples, the model is applied to the diffusion of Ag monolayer islands on Ag(111), and the homoepitaxial growth of Ag on Ag(111) at low temperatures.