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

Jyri Kimari, Ville Jansson, Simon Vigonski, Ekaterina Baibuz, Roberto Domingos, Vahur Zadin, Flyura Djurabekova

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number109789
JournalComputational Materials Science
Volume183
Number of pages11
ISSN0927-0256
DOIs
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 114 Physical sciences
  • 113 Computer and information sciences

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