| Autre titre | Development of Fukui function based descriptors for a machine learning study of CO2 reduction |
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| Téléchargement | - Voir la version de l’auteur : Development of Fukui function based descriptors for a machine learning study of CO₂ reduction (PDF, 1.6 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1021/acs.jpcc.0c03101 |
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| Auteur | Rechercher : Gusarov, Sergey1Identifiant ORCID : https://orcid.org/0000-0003-2033-705X; Rechercher : Stoyanov, Stanislav R.Identifiant ORCID : https://orcid.org/0000-0002-1878-4216; Rechercher : Siahrostami, SamiraIdentifiant ORCID : https://orcid.org/0000-0002-1192-4634 |
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| Affiliation | - Conseil national de recherches du Canada. Nanotechnologie
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| Format | Texte, Article |
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| Sujet | catalysts; reactivity; thermodynamic properties; adsorption; molecules |
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| Résumé | Developing novel methods that capture chemical properties quickly and with reasonable accuracy has emerged as an attractive way to replace time-consuming density functional theory (DFT) calculations. In this study, we propose a new type of machine learning (ML) enhanced descriptors based on the Fukui function (FF) projected onto the Connolly surface. The FF contains information about the local system’s response to the perturbation and could be used as a descriptor of the chemical properties of a surfaces. We show that the FF, augmented by a general characteristic of the electronic structure of the surface, such as a work function, is well correlated to the mapped adsorption energy of CO. Therefore, this combination might replace the computationally expensive mapping of the adsorption energy of small molecules as an indicator of catalytic activity. Potential extensions of the proposed methodology are briefly discussed. |
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| Date de publication | 2020-04-13 |
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| Maison d’édition | American Chemical Society |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro du CNRC | NRC-NANO-055 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | b5967c34-1063-4b7e-9304-ca8c5dcf88d0 |
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| Enregistrement créé | 2020-06-30 |
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| Enregistrement modifié | 2021-01-26 |
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