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

RSC Advances, vol. 11, pp. 32126–32134

Abstract

The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, particularly the catalyst layers. The structural building blocks of catalyst layers are formed during the processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and data augmentation that works on multiple scales in training robust and accurate classification pipelines.

Authors 6

1. Mohammad J. Eslamibidgoli corresponding

   Forschungszentrum Jülich

   Affiliation as printed

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

2. Fabian P. Tipp

   University of Cologne

   Affiliation as printed

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

3. Jenia Jitsev

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Julich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany

4. Jasna Janković

   University of Connecticut

   Affiliation as printed

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

5. Michael Hermann Eikerling Aachen Chair of Theory and Computation of Energy Materials Faculty of Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

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

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

6. Kourosh Malek corresponding

   Forschungszentrum Jülich

   Affiliation as printed

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