Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
This work addresses the difficulty of using deep learning for physical parameter learning in materials science, such as catalyst degradation analysis, but is incremental in nature.
The paper tackled the challenge of applying deep learning for image segmentation in materials science, specifically for coarsening dynamics in nanoparticles, by systematically studying dataset preparation, architecture, and evaluation to improve model generalizability and accuracy.
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical parameters. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding e.g. the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required.