at Google ScholarTitle data
Margraf, Johannes T. ; Ulissi, Zachary W. ; Jung, Yousung ; Reuter, Karsten:
Heterogeneous Catalysis in Grammar School.
In: The Journal of Physical Chemistry C.
Vol. 126
(2022)
Issue 6
.
- pp. 2931-2936.
ISSN 1932-7455
DOI: https://doi.org/10.1021/acs.jpcc.1c10285
Abstract in another language
The discovery of new catalytically active materials is one of the holy grails of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources and reduce the energy consumption of chemical industry. Indeed, heterogeneous catalysis is essential for the production of synthetic fuels and many commodity chemicals. Consequently, novel solid catalysts with higher activity and selectivity, increased sustainability and longevity, or improved prospects for rejuvenation and cyclability are needed for a diverse range of processes. Unfortunately, computational catalyst discovery is a daunting task, among other reasons because it is often unclear whether a proposed material is stable or synthesizable. This perspective proposes a new approach to this challenge, namely the use of generative grammars. We outline how grammars can guide the search for stable catalysts in a large chemical space and sketch out several research directions that would make this technology applicable to real materials.
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 13:49 |
| Last Modified: | 13 Nov 2023 13:49 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87656 |