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Self-learning kinetic Monte Carlo model for arbitrary surface orientations

  • Andreas Latz (a1), Lothar Brendel (a1) and Dietrich E. Wolf (a1)

Abstract

While the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.

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Keywords

Self-learning kinetic Monte Carlo model for arbitrary surface orientations

  • Andreas Latz (a1), Lothar Brendel (a1) and Dietrich E. Wolf (a1)

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