Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals

Assessing catalytic mechanisms using DFT calculations greatly aids catalyst design, but is impractical for large molecules. Here the authors develop a statistical learning-based thermochemical model for estimating adsorption of organics onto metals, retaining DFT accuracy while reducing the number o...

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Autores principales:	Rodrigo García-Muelas, Núria López
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/bcfedd75f53c4af9ad17dc158fdf3102
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spelling	oai:doaj.org-article:bcfedd75f53c4af9ad17dc158fdf31022021-12-02T14:38:43ZStatistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals10.1038/s41467-019-12709-12041-1723https://doaj.org/article/bcfedd75f53c4af9ad17dc158fdf31022019-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12709-1https://doaj.org/toc/2041-1723Assessing catalytic mechanisms using DFT calculations greatly aids catalyst design, but is impractical for large molecules. Here the authors develop a statistical learning-based thermochemical model for estimating adsorption of organics onto metals, retaining DFT accuracy while reducing the number of calculations by a factor of 20.Rodrigo García-MuelasNúria LópezNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-7 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Rodrigo García-Muelas Núria López Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
description	Assessing catalytic mechanisms using DFT calculations greatly aids catalyst design, but is impractical for large molecules. Here the authors develop a statistical learning-based thermochemical model for estimating adsorption of organics onto metals, retaining DFT accuracy while reducing the number of calculations by a factor of 20.
format	article
author	Rodrigo García-Muelas Núria López
author_facet	Rodrigo García-Muelas Núria López
author_sort	Rodrigo García-Muelas
title	Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
title_short	Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
title_full	Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
title_fullStr	Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
title_full_unstemmed	Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
title_sort	statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/bcfedd75f53c4af9ad17dc158fdf3102
work_keys_str_mv	AT rodrigogarciamuelas statisticallearninggoesbeyondthedbandmodelprovidingthethermochemistryofadsorbatesontransitionmetals AT nurialopez statisticallearninggoesbeyondthedbandmodelprovidingthethermochemistryofadsorbatesontransitionmetals
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Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals

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