Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells-Reference-Cited by-同舟云学术

Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells

Published:2021 Issue:51 Volume:11 Page:32126-32134
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Eslamibidgoli Mohammad J.¹^ORCID,Tipp Fabian P.²,Jitsev Jenia³^ORCID,Jankovic Jasna⁴,Eikerling Michael H.¹⁵^ORCID,Malek Kourosh¹

Affiliation:

1. Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

2. Department of Chemistry, University of Cologne, Greinstr. 4-6, 50939 Cologne, Germany

3. Julich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany

4. Department of Materials Science and Engineering, University of Connecticut, 97 North Eagleville Road, Unit 3136, Storrs, CT 06269-3136, USA

5. Chair of Theory and Computation of Energy Materials, Faculty of Georesources and Materials Engineering, RWTH Aachen University, Aachen 52062, Germany

Abstract

Deep learning enables the robust and accurate classification of the TEM images of catalyst layer inks for the polymer electrolyte fuel cells.

Funder

Bundesministerium für Bildung und Forschung

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA05324H

Reference65 articles.

1. Designing the next generation of proton-exchange membrane fuel cells