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

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http://hdl.handle.net/10138/318844

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Kimari , J , Jansson , V , Vigonski , S , Baibuz , E , Domingos , R , Zadin , V & Djurabekova , F 2020 , ' Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion ' , Computational Materials Science , vol. 183 , 109789 . https://doi.org/10.1016/j.commatsci.2020.109789

Title: Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion
Author: Kimari, Jyri; Jansson, Ville; Vigonski, Simon; Baibuz, Ekaterina; Domingos, Roberto; Zadin, Vahur; Djurabekova, Flyura
Contributor: University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Department of Physics
University of Helsinki, Helsinki Institute of Physics
University of Helsinki, Department of Physics
Date: 2020-10
Language: eng
Number of pages: 11
Belongs to series: Computational Materials Science
ISSN: 0927-0256
URI: http://hdl.handle.net/10138/318844
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.
Subject: 114 Physical sciences
113 Computer and information sciences
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