Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques

The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, eit...

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Autores: Pablo-García, Sergio, García-Muelas, Rodrigo, Sabadell-Rendón, Albert, López, Núria
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/450547
Acceso en línea:http://hdl.handle.net/2072/450547
https://doi.org/10.1002/wcms.1540
Access Level:acceso abierto
Palabra clave:54
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spelling Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniquesPablo-García, SergioGarcía-Muelas, RodrigoSabadell-Rendón, AlbertLópez, Núria54The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, either the minimum energy paths or those with the smaller span are summarized in reaction profiles. Such simplifications gather a lot of information, although further dimensionality reduction is required to obtain the most relevant descriptors of catalytic activity and generate the so-called volcano plots. The selection of descriptors has been traditionally based on simple intermediates, such as central atoms in small molecules (as C in CH4), which have good thermodynamic correlations to other fragments containing them. Yet, in emerging processes (recent studies), the number of intermediates involved increase, configurational effects and lateral interactions become significant, and complex materials with low symmetry are employed, thus the simple rules encapsulated in linear scaling relationships lose their predictive power due to error accumulation. At the same time, large datasets generated for the intermediates call for statistical analysis and thus these techniques are being leveraged to chemical systems, particularly to reduce their dimensionality.2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion1540 p.application/pdfhttp://hdl.handle.net/2072/450547https://doi.org/10.1002/wcms.1540RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésRTI2018-101394-BI00L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2072/4505472026-05-29T05:05:01Z
dc.title.none.fl_str_mv Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
title Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
spellingShingle Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
Pablo-García, Sergio
54
title_short Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
title_full Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
title_fullStr Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
title_full_unstemmed Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
title_sort Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear-scaling relationships to statistical learning techniques
dc.creator.none.fl_str_mv Pablo-García, Sergio
García-Muelas, Rodrigo
Sabadell-Rendón, Albert
López, Núria
author Pablo-García, Sergio
author_facet Pablo-García, Sergio
García-Muelas, Rodrigo
Sabadell-Rendón, Albert
López, Núria
author_role author
author2 García-Muelas, Rodrigo
Sabadell-Rendón, Albert
López, Núria
author2_role author
author
author
dc.subject.none.fl_str_mv 54
topic 54
description The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, either the minimum energy paths or those with the smaller span are summarized in reaction profiles. Such simplifications gather a lot of information, although further dimensionality reduction is required to obtain the most relevant descriptors of catalytic activity and generate the so-called volcano plots. The selection of descriptors has been traditionally based on simple intermediates, such as central atoms in small molecules (as C in CH4), which have good thermodynamic correlations to other fragments containing them. Yet, in emerging processes (recent studies), the number of intermediates involved increase, configurational effects and lateral interactions become significant, and complex materials with low symmetry are employed, thus the simple rules encapsulated in linear scaling relationships lose their predictive power due to error accumulation. At the same time, large datasets generated for the intermediates call for statistical analysis and thus these techniques are being leveraged to chemical systems, particularly to reduce their dimensionality.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/450547
https://doi.org/10.1002/wcms.1540
url http://hdl.handle.net/2072/450547
https://doi.org/10.1002/wcms.1540
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv RTI2018-101394-BI00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1540 p.
application/pdf
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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