Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

Abstract

Experiments and simulations can reveal energetic barriers during atomic-scale growth but are time-consuming. Here, machine learning is applied to single images from kinetic Monte Carlo simulations of sub-monolayer film growth, allowing diffusion barriers and binding energies to be accurately determined.