Browsing by Subject "energy barrier"

Surface diffusion in metals can be simulated with the atomistic kinetic Monte Carlo (KMC) method, where the evolution of a system is modeled by successive atomic jumps. The parametrisation of the method requires calculating the energy barriers of the different jumps that can occur in the system, which poses a limitation to its use. A promising solution to this are machine learning methods, such as artificial neural networks, which can be trained to predict barriers based on a set of pre-calculated ones. In this work, an existing neural network based parametrisation scheme is enhanced by expanding the atomic environment of the jump to include more atoms. A set of surface diffusion jumps was selected and their barriers were calculated with the nudged elastic band method. Artificial neural networks were then trained on the calculated barriers. Finally, KMC simulations of nanotip flattening were run using barriers which were predicted by the neural networks. The simulations were compared to the KMC results obtained with the existing scheme. The additional atoms in the jump environment caused significant changes to the barriers, which cannot be described by the existing model. The trained networks also showed a good prediction accuracy. However, the KMC results were in some cases more realistic or as realistic as the previous results, but often worse. The quality of the results also depended strongly on the selection of training barriers. We suggest that, for example, active learning methods can be used in the future to select the training data optimally.