Chemical shifts in molecular solids by machine learning

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 functi...

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Bibliographic Details
Main Authors: Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Format: article
Language:EN
Published: Nature Portfolio 2018
Subjects:
Q
Online Access:https://doaj.org/article/970a2addd1a345bea23283a88747dfe7
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