Data Sets and Trained Neural Networks for Cu Migration Barriers.

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

Data in Brief（2020）

Univ Helsinki | Univ Estado Rio de Janeiro | Univ Tartu

Cited 1|Views15

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.

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Key words

Copper,Kinetic Monte Carlo,Artificial neural networks,Machine learning,Surface diffusion,Migration barriers

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