at Google ScholarTitle data
Xu, Wenbin ; Diesen, Elias ; He, Tianwei ; Reuter, Karsten ; Margraf, Johannes T.:
Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization.
In: Journal of the American Chemical Society.
Vol. 146
(2024)
Issue 11
.
- pp. 7698-7707.
ISSN 1520-5126
DOI: https://doi.org/10.1021/jacs.3c14486
Abstract in another language
High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.
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 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 |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 13 Jan 2025 08:09 |
| Last Modified: | 13 Jan 2025 08:09 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/91534 |