at Google ScholarTitle data
Stocker, Sina ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
Machine learning in chemical reaction space.
In: Nature Communications.
Vol. 11
(2020)
.
- 5505.
ISSN 2041-1723
DOI: https://doi.org/10.1038/s41467-020-19267-x
Abstract in another language
Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 13 Nov 2023 11:51 |
| Last Modified: | 13 Nov 2023 11:51 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87678 |