Regensburg 2013 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

MM: Fachverband Metall- und Materialphysik

MM 26: Computational Materials Modelling - Diffusion & Kinetics I

MM 26.3: Talk

Tuesday, March 12, 2013, 15:30–15:45, H24

Three-dimensional self-learning kinetic Monte Carlo — •Andreas Latz, Lothar Brendel, and Dietrich E. Wolf — Department of Physics and Center for Nanointegration Duisburg-Essen (CeNIDE), University of Duisburg-Essen, Duisburg, Germany

The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme.

We expanded the original two-dimensional method to three dimensions (Latz et al 2012 J. Phys.: Condens. Matter 24 485005). Excessive on-the-fly calculations of rates can be avoided by setting up an initial database, which can be done perfectely in parallel.

The performance is illustrated by applying the method to homoepitaxial growth of Ag on Ag(111) at low temperatures.