  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.
