Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion

TL;DR

This paper explores using artificial neural networks to efficiently predict migration barriers in kinetic Monte Carlo simulations of copper surface diffusion, enabling accurate modeling with reduced computational complexity.

Contribution

It introduces a machine learning approach to predict atomic-scale barriers, improving the efficiency of KMC simulations for complex materials.

Findings

Neural network accurately predicts migration barriers for Cu surfaces.

KMC simulations with the neural network reproduce thermodynamic stability.

Method reduces computational effort in surface diffusion modeling.

Abstract

Kinetic Monte Carlo (KMC) is a powerful method for simulation of diffusion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe diffusion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface diffusion. We train an artificial neural network on a subset of the large set of $2^{26}$ barriers needed to correctly describe the surface diffusion in Cu. Our KMC simulations using the obtained barrier predictor show sufficient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces.