Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–subst...

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Autores principales:	Siwen Wang, Hemanth Somarajan Pillai, Hongliang Xin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b19c00b76505438c92ecff26dba7d5e6
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id	oai:doaj.org-article:b19c00b76505438c92ecff26dba7d5e6
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spelling	oai:doaj.org-article:b19c00b76505438c92ecff26dba7d5e62021-12-02T14:42:47ZBayesian learning of chemisorption for bridging the complexity of electronic descriptors10.1038/s41467-020-19524-z2041-1723https://doaj.org/article/b19c00b76505438c92ecff26dba7d5e62020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19524-zhttps://doaj.org/toc/2041-1723Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.Siwen WangHemanth Somarajan PillaiHongliang XinNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Siwen Wang Hemanth Somarajan Pillai Hongliang Xin Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
description	Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.
format	article
author	Siwen Wang Hemanth Somarajan Pillai Hongliang Xin
author_facet	Siwen Wang Hemanth Somarajan Pillai Hongliang Xin
author_sort	Siwen Wang
title	Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
title_short	Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
title_full	Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
title_fullStr	Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
title_full_unstemmed	Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
title_sort	bayesian learning of chemisorption for bridging the complexity of electronic descriptors
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b19c00b76505438c92ecff26dba7d5e6
work_keys_str_mv	AT siwenwang bayesianlearningofchemisorptionforbridgingthecomplexityofelectronicdescriptors AT hemanthsomarajanpillai bayesianlearningofchemisorptionforbridgingthecomplexityofelectronicdescriptors AT hongliangxin bayesianlearningofchemisorptionforbridgingthecomplexityofelectronicdescriptors
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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

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