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Data sets and trained neural networks for Cu migration barriers.

Jyri Kimari1, Ville Jansson1, Simon Vigonski1,2

  • 1Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43 (Pietari Kalmin katu 2), FI-00014, Finland.

Data in Brief
|September 9, 2020
PubMed
Summary

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This study introduces a dataset of copper (Cu) migration barriers for surface diffusion simulations. Artificial neural networks (ANNs) were trained using this data to predict barriers, improving the accuracy of Kinetic Monte Carlo (KMC) simulations.

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Surface Science

Background:

  • Kinetic Monte Carlo (KMC) simulations are vital for studying diffusion processes.
  • The accuracy of KMC is limited by the number of migration events considered.
  • Calculating migration energy barriers for each event can be computationally expensive.

Purpose of the Study:

  • To create a dataset of migration energy barriers for nearest-neighbor jumps on copper (Cu) surfaces.
  • To train artificial neural networks (ANNs) for predicting these migration barriers.
  • To enhance the efficiency and accuracy of KMC simulations for Cu surface diffusion.

Main Methods:

  • Utilized the nudged elastic band (NEB) method and the tethering force approach to calculate migration barriers.
Keywords:
Artificial neural networksCopperKinetic Monte CarloMachine learningMigration barriersSurface diffusion

Related Experiment Videos

  • Developed and trained artificial neural networks (ANNs) using the calculated dataset.
  • The dataset and trained ANNs are made available.

    • Main Results:

    • A comprehensive dataset of migration barriers for nearest-neighbor jumps on Cu surfaces was generated.
    • Trained ANNs demonstrated the ability to predict migration barriers for arbitrary Cu jumps.
    • The developed approach facilitates more accurate and efficient KMC simulations.

    Conclusions:

    • The presented dataset and ANNs offer a valuable resource for researchers studying Cu surface diffusion.
    • This work significantly reduces the computational cost associated with KMC simulations.
    • The findings pave the way for more advanced modeling of surface processes.