Data sets and trained neural networks for Cu migration barriers

TL;DR

This paper presents a dataset of Cu surface migration barriers calculated with NEB and trains neural networks to predict these barriers, enabling more efficient KMC simulations of diffusion.

Contribution

It introduces a new dataset of migration barriers and trained neural networks for predicting Cu surface diffusion barriers, improving KMC simulation accuracy and efficiency.

Findings

Neural networks accurately predict migration barriers

Dataset enables faster KMC simulations

Trained models are publicly available

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 artifcial 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.