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Article Dans Une Revue Journal of Chemical Information and Modeling Année : 2023

Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces

Carine Michel
Florian Göltl

Résumé

In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which, in many cases, limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C-, O-, and H-containing molecules on transition metal surfaces. We rely on generalized Bronsted–Evans–Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a mean absolute error for activation energies within our test set of 0.14 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future.

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Chimie
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Dates et versions

hal-04264829 , version 1 (30-10-2023)

Identifiants

Citer

Daniel Hutton, Kari Cordes, Carine Michel, Florian Göltl. Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces. Journal of Chemical Information and Modeling, 2023, 63 (19), pp.6006-6013. ⟨10.1021/acs.jcim.3c00740⟩. ⟨hal-04264829⟩
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