Data sets and trained neural networks for Cu migration barriers

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...

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Bibliographic Details
Published in:Data in brief 2020-10, Vol.32, p.106094-106094, Article 106094
Main Authors: Kimari, Jyri, Jansson, Ville, Vigonski, Simon, Baibuz, Ekaterina, Domingos, Roberto, Zadin, Vahur, Djurabekova, Flyura
Format: Article
Language:English
Subjects:
Artificial neural networks
Copper
Kinetic Monte Carlo
Machine learning
Materials Science
Migration barriers
Surface diffusion
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Summary: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.
ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2020.106094