Navigating the unknown with AI multiobjective Bayesian optimization of non-noble acidic OER catalysts /
This study highlighted the effectiveness of AI-driven multiobjective Bayesian optimization for electrocatalysis, accelerating the search for active and stable compositions for the acidic oxygen evolution reaction by 17x.
Elmentve itt :
| Szerzők: | |
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2024
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| Sorozat: | JOURNAL OF MATERIALS CHEMISTRY A
12 No. 5 |
| Tárgyszavak: | |
| doi: | 10.1039/D3TA06651G |
| mtmt: | 34559426 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/35016 |
| Tartalmi kivonat: | This study highlighted the effectiveness of AI-driven multiobjective Bayesian optimization for electrocatalysis, accelerating the search for active and stable compositions for the acidic oxygen evolution reaction by 17x. |
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| Terjedelem/Fizikai jellemzők: | 3072-3083 |
| ISSN: | 2050-7488 |