Data sets and trained neural networks for Cu migration barriers

Jyri Kimari; Ville Jansson; Simon Vigonski; Ekaterina Baibuz; Roberto Domingos; Vahur Zadin; Flyura Djurabekova

doi:10.1016/j.dib.2020.106094

Data sets and trained neural networks for Cu migration barriers

Data Brief. 2020 Jul 31:32:106094. doi: 10.1016/j.dib.2020.106094. eCollection 2020 Oct.

Authors

Jyri Kimari¹, Ville Jansson¹, Simon Vigonski^{1

2}, Ekaterina Baibuz¹, Roberto Domingos³, Vahur Zadin², Flyura Djurabekova¹

Affiliations

¹ Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43 (Pietari Kalmin katu 2), FI-00014, Finland.
² Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia.
³ Instituto Politécnico de Nova Friburgo - Universidade do Estado do Rio de Janeiro, Rua Sao Francisco Xavier, 524, 20550-900 Rio de Janeiro, RJ, Brazil.

Abstract

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.

Keywords: Artificial neural networks; Copper; Kinetic Monte Carlo; Machine learning; Migration barriers; Surface diffusion.