Self-learning Kinetic Monte-Carlo method: application to Cu(111)

TL;DR

This paper introduces a self-learning kinetic Monte Carlo method that dynamically computes or retrieves process energetics during simulations, improving efficiency and reliability in modeling atomistic surface diffusion.

Contribution

The novel method eliminates the need for pre-defined process lists by dynamically learning energetics, enabling more accurate and efficient simulations of surface phenomena.

Findings

Successfully applied to Cu adatom-cluster diffusion on Cu(111)

Achieved faster simulations with higher reliability

Provided detailed statistics of atomistic processes

Abstract

We present a novel way of performing kinetic Monte Carlo simulations which does not require an {\it a priori} list of diffusion processes and their associated energetics and reaction rates. Rather, at any time during the simulation, energetics for all possible (single or multi-atom) processes, within a specific interaction range, are either computed accurately using a saddle point search procedure, or retrieved from a database in which previously encountered processes are stored. This self-learning procedure enhances the speed of the simulations along with a substantial gain in reliability because of the inclusion of many-particle processes. Accompanying results from the application of the method to the case of two-dimensional Cu adatom-cluster diffusion and coalescence on Cu(111) with detailed statistics of involved atomistic processes and contributing diffusion coefficients attest to the suitability of the method for the purpose.