Chemical shifts in molecular solids by machine learning

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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Main Authors: Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Format: Article
Language:English
Published: Nature Publishing Group 2018-10-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-06972-x
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spelling doaj-970a2addd1a345bea23283a88747dfe72021-05-11T09:33:27ZengNature Publishing GroupNature Communications2041-17232018-10-019111010.1038/s41467-018-06972-xChemical shifts in molecular solids by machine learningFederico M. Paruzzo0Albert Hofstetter1Félix Musil2Sandip De3Michele Ceriotti4Lyndon Emsley5Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL)Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL)Institut des Sciences et Génie Matériaux, Ecole Polytechnique Fédérale de Lausanne (EPFL)Institut des Sciences et Génie Matériaux, Ecole Polytechnique Fédérale de Lausanne (EPFL)Institut des Sciences et Génie Matériaux, Ecole Polytechnique Fédérale de Lausanne (EPFL)Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL)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.https://doi.org/10.1038/s41467-018-06972-x
collection DOAJ
language English
format Article
sources DOAJ
author Federico M. Paruzzo
Albert Hofstetter
Félix Musil
Sandip De
Michele Ceriotti
Lyndon Emsley
spellingShingle Federico M. Paruzzo
Albert Hofstetter
Félix Musil
Sandip De
Michele Ceriotti
Lyndon Emsley
Chemical shifts in molecular solids by machine learning
Nature Communications
author_facet Federico M. Paruzzo
Albert Hofstetter
Félix Musil
Sandip De
Michele Ceriotti
Lyndon Emsley
author_sort Federico M. Paruzzo
title Chemical shifts in molecular solids by machine learning
title_short Chemical shifts in molecular solids by machine learning
title_full Chemical shifts in molecular solids by machine learning
title_fullStr Chemical shifts in molecular solids by machine learning
title_full_unstemmed Chemical shifts in molecular solids by machine learning
title_sort chemical shifts in molecular solids by machine learning
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2018-10-01
description 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.
url https://doi.org/10.1038/s41467-018-06972-x
work_keys_str_mv AT federicomparuzzo chemicalshiftsinmolecularsolidsbymachinelearning
AT alberthofstetter chemicalshiftsinmolecularsolidsbymachinelearning
AT felixmusil chemicalshiftsinmolecularsolidsbymachinelearning
AT sandipde chemicalshiftsinmolecularsolidsbymachinelearning
AT micheleceriotti chemicalshiftsinmolecularsolidsbymachinelearning
AT lyndonemsley chemicalshiftsinmolecularsolidsbymachinelearning
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