Title:
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Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
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Author:
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García-Muelas, Rodrigo; López, Núria
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Abstract:
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The rational design of heterogeneous catalysts relies on the efficient survey of mechanisms
by density functional theory (DFT). However, massive reaction networks cannot be sampled
effectively as they grow exponentially with the size of reactants. Here we present a statistical
principal component analysis and regression applied to the DFT thermochemical data of 71
C1–C2 species on 12 close-packed metal surfaces. Adsorption is controlled by covalent
(d-band center) and ionic terms (reduction potential), modulated by conjugation and conformational
contributions. All formation energies can be reproduced from only three key
intermediates (predictors) calculated with DFT. The results agree with accurate experimental
measurements having error bars comparable to those of DFT. The procedure can be
extended to single-atom and near-surface alloys reducing the number of explicit DFT calculation
needed by a factor of 20, thus paving the way for a rapid and accurate survey of
whole reaction networks on multimetallic surfaces. |
Publication date:
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2019-10-15 |
Subject(s):
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54 |
Rights:
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L'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/ |
Pages:
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4687 p. |
Document type:
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Article Article - Accepted version |
DOI:
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https://doi.org/10.1038/s41467-019-12709-1
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