Machine learned features from density of states for accurate adsorption energy prediction

Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as par...

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Autores principales:	Victor Fung, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/db4b29d6fa904989b00edcedcb18059e
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spelling	oai:doaj.org-article:db4b29d6fa904989b00edcedcb18059e2021-12-02T15:16:22ZMachine learned features from density of states for accurate adsorption energy prediction10.1038/s41467-020-20342-62041-1723https://doaj.org/article/db4b29d6fa904989b00edcedcb18059e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20342-6https://doaj.org/toc/2041-1723Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.Victor FungGuoxiang HuP. GaneshBobby G. SumpterNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Victor Fung Guoxiang Hu P. Ganesh Bobby G. Sumpter Machine learned features from density of states for accurate adsorption energy prediction
description	Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.
format	article
author	Victor Fung Guoxiang Hu P. Ganesh Bobby G. Sumpter
author_facet	Victor Fung Guoxiang Hu P. Ganesh Bobby G. Sumpter
author_sort	Victor Fung
title	Machine learned features from density of states for accurate adsorption energy prediction
title_short	Machine learned features from density of states for accurate adsorption energy prediction
title_full	Machine learned features from density of states for accurate adsorption energy prediction
title_fullStr	Machine learned features from density of states for accurate adsorption energy prediction
title_full_unstemmed	Machine learned features from density of states for accurate adsorption energy prediction
title_sort	machine learned features from density of states for accurate adsorption energy prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/db4b29d6fa904989b00edcedcb18059e
work_keys_str_mv	AT victorfung machinelearnedfeaturesfromdensityofstatesforaccurateadsorptionenergyprediction AT guoxianghu machinelearnedfeaturesfromdensityofstatesforaccurateadsorptionenergyprediction AT pganesh machinelearnedfeaturesfromdensityofstatesforaccurateadsorptionenergyprediction AT bobbygsumpter machinelearnedfeaturesfromdensityofstatesforaccurateadsorptionenergyprediction
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Machine learned features from density of states for accurate adsorption energy prediction

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