Nature Communications (Oct 2018)

Chemical shifts in molecular solids by machine learning

  • Federico M. Paruzzo,
  • Albert Hofstetter,
  • Félix Musil,
  • Sandip De,
  • Michele Ceriotti,
  • Lyndon Emsley

DOI
https://doi.org/10.1038/s41467-018-06972-x
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 10

Abstract

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Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.