Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion
Autor: | Vahur Zadin, Simon Vigonski, Jyri Kimari, Flyura Djurabekova, Ville Jansson, Ekaterina Baibuz, Roberto P. Domingos |
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Přispěvatelé: | Helsinki Institute of Physics, Department of Physics |
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Surface diffusion
Materials science General Computer Science Artificial neural network FOS: Physical sciences General Physics and Astronomy 02 engineering and technology General Chemistry Computational Physics (physics.comp-ph) 010402 general chemistry 021001 nanoscience & nanotechnology 113 Computer and information sciences 01 natural sciences Atomic units 114 Physical sciences 0104 chemical sciences Computational Mathematics Mechanics of Materials Lattice (order) General Materials Science Statistical physics Kinetic Monte Carlo 0210 nano-technology Physics - Computational Physics |
Popis: | 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. |
Databáze: | OpenAIRE |
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