Nature Communications (Feb 2021)

Automated discovery of a robust interatomic potential for aluminum

  • Justin S. Smith,
  • Benjamin Nebgen,
  • Nithin Mathew,
  • Jie Chen,
  • Nicholas Lubbers,
  • Leonid Burakovsky,
  • Sergei Tretiak,
  • Hai Ah Nam,
  • Timothy Germann,
  • Saryu Fensin,
  • Kipton Barros

DOI
https://doi.org/10.1038/s41467-021-21376-0
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 13

Abstract

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The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.