at Google ScholarTitle data
Stocker, Sina ; Jung, Hyunwook ; Csányi, Gábor ; Goldsmith, C. Franklin ; Reuter, Karsten ; Margraf, Johannes T.:
Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration.
In: Journal of Chemical Theory and Computation.
Vol. 19
(2023)
Issue 19
.
- pp. 6796-6804.
ISSN 1549-9626
DOI: https://doi.org/10.1021/acs.jctc.3c00541
Abstract in another language
Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Research Institutions > Central research institutes > Research Center for AI in Science and Society |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 13 Nov 2023 12:00 |
| Last Modified: | 04 Nov 2025 09:53 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87667 |