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

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.

References in corpus (7)

• Direct observation of size scaling and elastic interaction between nano-scale defects in collision cascades
• Au nanowire junction breakup through surface atom diffusion
• Long-term stability of Cu surface nanotips
• Migration barriers for surface diffusion on a rigid lattice: challenges and solutions
• Growth mechanism for nanotips in high electric fields
• A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)
• Tungsten migration energy barriers for surface diffusion: a parameterization for KMC simulations