Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface

The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such c...

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Autores: Žugec, Ivan, Tetenoire, Auguste, Muzas, Alberto S., Zhang, Yaolong, Jiang, Bin, Alducin Ochoa, Maite, Juaristi Oliden, Joseba Iñaki
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/365513
Acceso en línea:http://hdl.handle.net/10261/365513
Access Level:acceso abierto
Palabra clave:Neural networks
Femtochemistry
CO oxidation and desorption
Ru(0001)
Potential energy surface
Laser-induced dynamics
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spelling Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surfaceŽugec, IvanTetenoire, AugusteMuzas, Alberto S.Zhang, YaolongJiang, BinAlducin Ochoa, MaiteJuaristi Oliden, Joseba IñakiNeural networksFemtochemistryCO oxidation and desorptionRu(0001)Potential energy surfaceLaser-induced dynamicsThe study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons.The authors acknowledge financial support by the Spanish MCIN/AEI/10.13039/501100011033/and FEDER “Una manera de hacer Europa” [Grant no. PID2022-140163NB-I00], Gobierno Vasco-UPV/EHU [Project no. IT1569-22] and the Basque Government Education Department IKUR program cofunded by the European NextGenerationEU action through the Spanish PRTR. This research was conducted in the scope of the Transnational Common Laboratory (LTC) “QuantumChemPhys – Theoretical Chemistry and Physics at the Quantum Scale”. Computational resources were provided by the DIPC computing center.Peer reviewedAmerican Chemical SocietyMinisterio de Ciencia, Innovación y Universidades (España)Ministerio de Ciencia e Innovación (España)European CommissionAgencia Estatal de Investigación (España)Eusko JaurlaritzaDonostia International Physics CenterUniversidad del País VascoConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/365513reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-140163NB-I00Žugec, Ivan; Tetenoire, Auguste; Muzas, Alberto S.; Zhang, Yaolong; Jiang, Bin; Alducin Ochoa, Maite; Juaristi Oliden, Joseba Iñaki; 2024; Supporting Information: Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface [Dataset]; American Chemical Society; https://doi.org/10.1021/jacsau.4c00197https://doi.org/10.1021/jacsau.4c00197Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3655132026-05-22T06:33:51Z
dc.title.none.fl_str_mv Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
title Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
spellingShingle Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
Žugec, Ivan
Neural networks
Femtochemistry
CO oxidation and desorption
Ru(0001)
Potential energy surface
Laser-induced dynamics
title_short Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
title_full Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
title_fullStr Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
title_full_unstemmed Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
title_sort Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
dc.creator.none.fl_str_mv Žugec, Ivan
Tetenoire, Auguste
Muzas, Alberto S.
Zhang, Yaolong
Jiang, Bin
Alducin Ochoa, Maite
Juaristi Oliden, Joseba Iñaki
author Žugec, Ivan
author_facet Žugec, Ivan
Tetenoire, Auguste
Muzas, Alberto S.
Zhang, Yaolong
Jiang, Bin
Alducin Ochoa, Maite
Juaristi Oliden, Joseba Iñaki
author_role author
author2 Tetenoire, Auguste
Muzas, Alberto S.
Zhang, Yaolong
Jiang, Bin
Alducin Ochoa, Maite
Juaristi Oliden, Joseba Iñaki
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Ministerio de Ciencia e Innovación (España)
European Commission
Agencia Estatal de Investigación (España)
Eusko Jaurlaritza
Donostia International Physics Center
Universidad del País Vasco
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Neural networks
Femtochemistry
CO oxidation and desorption
Ru(0001)
Potential energy surface
Laser-induced dynamics
topic Neural networks
Femtochemistry
CO oxidation and desorption
Ru(0001)
Potential energy surface
Laser-induced dynamics
description The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/365513
url http://hdl.handle.net/10261/365513
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-140163NB-I00
Žugec, Ivan; Tetenoire, Auguste; Muzas, Alberto S.; Zhang, Yaolong; Jiang, Bin; Alducin Ochoa, Maite; Juaristi Oliden, Joseba Iñaki; 2024; Supporting Information: Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface [Dataset]; American Chemical Society; https://doi.org/10.1021/jacsau.4c00197
https://doi.org/10.1021/jacsau.4c00197
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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