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.
@article{arxiv.1912.06077,
title = {Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation},
author = {James P. Horwath and Dmitri N. Zakharov and Remi Megret and Eric A. Stach},
journal= {arXiv preprint arXiv:1912.06077},
year = {2019}
}