Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

Knowing compositional motifs of nanoparticle catalysts in operando conditions is crucial towards understanding their catalytic behavior. Here, the authors develop a physics-driven machine learning approach to predict adsorption sites for a CO molecule over platinum nanoparticles in a multitude of co...

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Autores principales:	Joshua L. Lansford, Dionisios G. Vlachos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6fd23d16dcac44afbf726b5db3d22040
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spelling	oai:doaj.org-article:6fd23d16dcac44afbf726b5db3d220402021-12-02T14:40:42ZInfrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials10.1038/s41467-020-15340-72041-1723https://doaj.org/article/6fd23d16dcac44afbf726b5db3d220402020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15340-7https://doaj.org/toc/2041-1723Knowing compositional motifs of nanoparticle catalysts in operando conditions is crucial towards understanding their catalytic behavior. Here, the authors develop a physics-driven machine learning approach to predict adsorption sites for a CO molecule over platinum nanoparticles in a multitude of coordination environments.Joshua L. LansfordDionisios G. VlachosNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Joshua L. Lansford Dionisios G. Vlachos Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
description	Knowing compositional motifs of nanoparticle catalysts in operando conditions is crucial towards understanding their catalytic behavior. Here, the authors develop a physics-driven machine learning approach to predict adsorption sites for a CO molecule over platinum nanoparticles in a multitude of coordination environments.
format	article
author	Joshua L. Lansford Dionisios G. Vlachos
author_facet	Joshua L. Lansford Dionisios G. Vlachos
author_sort	Joshua L. Lansford
title	Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_short	Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_full	Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_fullStr	Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_full_unstemmed	Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_sort	infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/6fd23d16dcac44afbf726b5db3d22040
work_keys_str_mv	AT joshuallansford infraredspectroscopydataandphysicsdrivenmachinelearningforcharacterizingsurfacemicrostructureofcomplexmaterials AT dionisiosgvlachos infraredspectroscopydataandphysicsdrivenmachinelearningforcharacterizingsurfacemicrostructureofcomplexmaterials
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Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

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