Self-learning kinetic Monte Carlo model for arbitrary surface orientations

TL;DR

This paper extends the self-learning kinetic Monte Carlo method to simulate surface phenomena on arbitrarily oriented surfaces by detecting local orientations, enabling more accurate modeling of complex surface shapes.

Contribution

The work introduces a local orientation detection technique to generalize SLKMC for arbitrary surface geometries, surpassing previous limitations to specific surface orientations.

Findings

Successfully simulated diffusion of Ag monolayer islands and voids on different surfaces.

Modeled relaxation of a 3D spherical particle.

Demonstrated improved accuracy for complex surface shapes.

Abstract

While the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.