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Bayesian active machine learning for Cluster expansion construction

Publication ,  Journal Article
Chen, H; Samanta, S; Zhu, S; Eckert, H; Schroers, J; Curtarolo, S; van de Walle, A
Published in: Computational Materials Science
January 5, 2024

The Cluster expansion (CE) is a powerful method for representing the energetics of alloys from a fit to first principles energies. However, many common fitting methods are computationally demanding and do not provide the guarantee that the system's ground states are preserved. This paper demonstrates the use of an efficient implementation of a Bayesian algorithm for cluster expansion construction that ensures all the input structural energies are fitted perfectly while reducing computational cost. The method incorporates an active learning scheme that searches for new optimal structures to include in the fit. As performance tests, we calculate the phase diagram of the Fe–Ir system and study the short range order in an equimolar MoNbTaVW system. The new method has been integrated into the Alloy Theoretic Automated Toolkit (ATAT).

Duke Scholars

Published In

Computational Materials Science

DOI

ISSN

0927-0256

Publication Date

January 5, 2024

Volume

231

Related Subject Headings

  • Materials
  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 0912 Materials Engineering
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics
 

Citation

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Chen, H., Samanta, S., Zhu, S., Eckert, H., Schroers, J., Curtarolo, S., & van de Walle, A. (2024). Bayesian active machine learning for Cluster expansion construction. Computational Materials Science, 231. https://doi.org/10.1016/j.commatsci.2023.112571
Chen, H., S. Samanta, S. Zhu, H. Eckert, J. Schroers, S. Curtarolo, and A. van de Walle. “Bayesian active machine learning for Cluster expansion construction.” Computational Materials Science 231 (January 5, 2024). https://doi.org/10.1016/j.commatsci.2023.112571.
Chen H, Samanta S, Zhu S, Eckert H, Schroers J, Curtarolo S, et al. Bayesian active machine learning for Cluster expansion construction. Computational Materials Science. 2024 Jan 5;231.
Chen, H., et al. “Bayesian active machine learning for Cluster expansion construction.” Computational Materials Science, vol. 231, Jan. 2024. Scopus, doi:10.1016/j.commatsci.2023.112571.
Chen H, Samanta S, Zhu S, Eckert H, Schroers J, Curtarolo S, van de Walle A. Bayesian active machine learning for Cluster expansion construction. Computational Materials Science. 2024 Jan 5;231.
Journal cover image

Published In

Computational Materials Science

DOI

ISSN

0927-0256

Publication Date

January 5, 2024

Volume

231

Related Subject Headings

  • Materials
  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 0912 Materials Engineering
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics