Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts-Reference-Cited by-同舟云学术

Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts

Published:2024 Issue:5 Volume:12 Page:3072-3083
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Jenewein Ken J.¹²^ORCID,Torresi Luca³^ORCID,Haghmoradi Navid⁴^ORCID,Kormányos Attila¹⁵^ORCID,Friederich Pascal³⁴^ORCID,Cherevko Serhiy¹^ORCID

Affiliation:

1. Helmholtz-Institute Erlangen-Nürnberg for Renewable Energy (IEK-11), Forschungszentrum Jülich, Cauerstrasse 1, 91058 Erlangen, Germany

2. Department of Chemical and Biological Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Egerlandstrasse 3, 91058 Erlangen, Germany

3. Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany

4. Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

5. Department of Physical Chemistry and Materials Science, Interdisciplinary Excellence Centre, University of Szeged, Aradi Square 1, Szeged, H-6720 Hungary

Abstract

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.

Funder

Bundesministerium für Bildung und Forschung

Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06651G

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