The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, in particular the catalyst layers. Essential structural building blocks of catalyst layers are formed during 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 employment of data augmentation that works on multiple scales in the training of robust and accurate classification pipelines.