Journal
DATA IN BRIEF
Volume 32, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.dib.2020.106094
Keywords
Copper; Kinetic Monte Carlo; Artificial neural networks; Machine learning; Surface diffusion; Migration barriers
Categories
Funding
- CERN K-contract
- Academy of Finland [269696, 313867, 285382]
- MEPhI Academic Excellence Project [02.a03.21.0005]
- doctoral program MATRENA of the University of Helsinki
- Estonian Research Council grant [PUT 1372]
- Academy of Finland (AKA) [313867, 285382, 313867, 285382] Funding Source: Academy of Finland (AKA)
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Kinetic Monte Carlo (KMC) is an efficient method for studying diffusion. A limiting factor to the accuracy of KMC is the number of different migration events allowed in the simulation. Each event requires its own migration energy barrier. The calculation of these barriers may be unfeasibly expensive. In this article we present a data set of migration barriers on for nearest-neighbour jumps on the Cu surfaces, calculated with the nudged elastic band (NEB) method and the tethering force approach. We used the data to train artificial neural networks (ANN) in order to predict the migration barriers for arbitrary nearest-neighbour Cu jumps. The trained ANNs are also included in the article. The data is hosted by the CSC IDA storage service. (C) 2020 Published by Elsevier Inc.
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