Nature Communications (Sep 2021)

Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

  • Andrij Vasylenko,
  • Jacinthe Gamon,
  • Benjamin B. Duff,
  • Vladimir V. Gusev,
  • Luke M. Daniels,
  • Marco Zanella,
  • J. Felix Shin,
  • Paul M. Sharp,
  • Alexandra Morscher,
  • Ruiyong Chen,
  • Alex R. Neale,
  • Laurence J. Hardwick,
  • John B. Claridge,
  • Frédéric Blanc,
  • Michael W. Gaultois,
  • Matthew S. Dyer,
  • Matthew J. Rosseinsky

DOI
https://doi.org/10.1038/s41467-021-25343-7
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 12

Abstract

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Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.