| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Baibuz, Ekaterina
University of Helsinki
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2020Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusioncitations
- 2020Tungsten migration energy barriers for surface diffusioncitations
- 2019Au nanowire junction breakup through surface atom diffusioncitations
- 2018Migration barriers for surface diffusion on a rigid lattice : Challenges and solutionscitations
- 2018Migration barriers for surface diffusion on a rigid latticecitations
- 2018Au nanowire junction breakup through surface atom diffusioncitations
- 2016Long-term stability of Cu surface nanotipscitations
Places of action
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article
Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion
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. ; Peer reviewed