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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/970a2addd1a345bea23283a88747dfe7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:970a2addd1a345bea23283a88747dfe7
record_format	dspace
spelling	oai:doaj.org-article:970a2addd1a345bea23283a88747dfe72021-12-02T14:40:44ZChemical shifts in molecular solids by machine learning10.1038/s41467-018-06972-x2041-1723https://doaj.org/article/970a2addd1a345bea23283a88747dfe72018-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-06972-xhttps://doaj.org/toc/2041-1723Solid-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.Federico M. ParuzzoAlbert HofstetterFélix MusilSandip DeMichele CeriottiLyndon EmsleyNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Federico M. Paruzzo Albert Hofstetter Félix Musil Sandip De Michele Ceriotti Lyndon Emsley Chemical shifts in molecular solids by machine learning
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.
format	article
author	Federico M. Paruzzo Albert Hofstetter Félix Musil Sandip De Michele Ceriotti Lyndon Emsley
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 Portfolio
publishDate	2018
url	https://doaj.org/article/970a2addd1a345bea23283a88747dfe7
work_keys_str_mv	AT federicomparuzzo chemicalshiftsinmolecularsolidsbymachinelearning AT alberthofstetter chemicalshiftsinmolecularsolidsbymachinelearning AT felixmusil chemicalshiftsinmolecularsolidsbymachinelearning AT sandipde chemicalshiftsinmolecularsolidsbymachinelearning AT micheleceriotti chemicalshiftsinmolecularsolidsbymachinelearning AT lyndonemsley chemicalshiftsinmolecularsolidsbymachinelearning
_version_	1718390188201213952

Chemical shifts in molecular solids by machine learning

Ejemplares similares