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Article Dans Une Revue Angewandte Chemie International Edition Année : 2024

Reference‐Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory

Résumé

For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation—RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer–Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling.

Domaines

Catalyse
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Origine : Publication financée par une institution
licence : CC BY NC - Paternité - Pas d'utilisation commerciale
licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04504311 , version 1 (14-03-2024)

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Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Jérôme Rey, Céline Chizallet, Dario Rocca, Tomáš Bučko, Michael M Badawi. Reference‐Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory. Angewandte Chemie International Edition, 2024, 63 (6), pp.e202312392. ⟨10.1002/anie.202312392⟩. ⟨hal-04504311⟩
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