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Photoinduced desorption dynamics of CO from Pd(111): a neural network approach
dc.contributor.author | Serrano Jiménez, Alfredo | |
dc.contributor.author | Sánchez Muzas, Alberto Pablo | |
dc.contributor.author | Zhang, Yaolong | |
dc.contributor.author | Ovcar, Juraj | |
dc.contributor.author | Jiang, Bin | |
dc.contributor.author | Loncaric, Ivor | |
dc.contributor.author | Juaristi Oliden, Joseba Iñaki | |
dc.contributor.author | Alducín Ochoa, Maite | |
dc.date.accessioned | 2021-09-02T10:16:51Z | |
dc.date.available | 2021-09-02T10:16:51Z | |
dc.date.issued | 2021-07-19 | |
dc.identifier.citation | Journal of Chemical Theory and Computation 17 : 4648-4659 (2021) | es_ES |
dc.identifier.issn | 1549-9618 | |
dc.identifier.issn | 1549-9626 | |
dc.identifier.uri | http://hdl.handle.net/10810/52906 | |
dc.description.abstract | [EN] Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (Te,Tl)-AIMDEF [Alducin, M.;et al. Phys. Rev. Lett. 2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y.;et al. J. Phys. Chem. Lett. 2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (Te,Tl)-AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90-1000 K); a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms; and the varying CO coverage caused by the abundant desorption events. | es_ES |
dc.description.sponsorship | The authors acknowledge financial support by the Gobierno Vasco-UPV/EHU Project no. IT1246-19 and the Spanish Ministerio de Ciencia e Innovación [Grant no. PID2019- 107396GB-I00/AEI/10.13039/501100011033]. This work has been supported in part by the Croatian Science Foundation under project UIP-2020-02-5675. 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. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | American Chemical Society | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/PID2019-107396GB-I00/AEI/10.13039/501100011033 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | Photoinduced desorption dynamics of CO from Pd(111): a neural network approach | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | ©2021 The Authors. Published by American Chemical Society. Attribution 4.0 International (CC BY 4.0) | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://pubs.acs.org/doi/10.1021/acs.jctc.1c00347 | es_ES |
dc.identifier.doi | 10.1021/acs.jctc.1c00347 | |
dc.departamentoes | Polímeros y Materiales Avanzados: Física, Química y Tecnología | es_ES |
dc.departamentoeu | Polimero eta Material Aurreratuak: Fisika, Kimika eta Teknologia | es_ES |