Communications Materials (Aug 2022)

Why big data and compute are not necessarily the path to big materials science

  • Naohiro Fujinuma,
  • Brian DeCost,
  • Jason Hattrick-Simpers,
  • Samuel E. Lofland

DOI
https://doi.org/10.1038/s43246-022-00283-x
Journal volume & issue
Vol. 3, no. 1
pp. 1 – 9

Abstract

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Machine learning is an increasingly important tool for materials science. Here, the authors suggest that its contextual use, including careful assessment of resources and bias, judicious model selection, and an understanding of its limitations, will help researchers to expedite scientific discovery.