at Google ScholarTitle data
Margraf, Johannes T. ; Jung, Hyunwook ; Scheurer, Christoph ; Reuter, Karsten:
Exploring catalytic reaction networks with machine learning.
In: Nature Catalysis.
Vol. 6
(2023)
.
- pp. 112-121.
ISSN 2520-1158
DOI: https://doi.org/10.1038/s41929-022-00896-y
Abstract in another language
Chemical reaction networks form the heart of microkinetic models, which are one of the key tools available for gaining detailed mechanistic insight into heterogeneous catalytic processes. The exploration of complex chemical reaction networks is therefore a central task in current catalysis research. Unfortunately, microscopic experimental information about which elementary reaction steps are relevant to a given process is almost always sparse, making the inference of networks from experiments alone almost impossible. While computational approaches provide important complementary insights to this end, their predictions also come with substantial uncertainties related to the underlying approximations and, crucially, the use of idealized structure models. In this Perspective, we aim to shine a light on recent applications of machine learning in the context of catalytic reaction networks, aiding both the inference of effective kinetic rate laws from experiment and the computational exploration of chemical reaction networks.
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 Research Institutions Research Institutions > Central research institutes |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 13 Nov 2023 12:46 |
| Last Modified: | 04 Nov 2025 09:52 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87663 |