Transmission electron microscopy (TEM) is the characterization method of choice to observe the atomic-scale and microstructural local features within materials that play a critical role in material performance. However, a bottleneck exists between image acquisition and the extraction of relevant information that can be used in a materials design feedback loop. While image analysis of individual images can easily identify regions of interest and determine whether they contain defects, it is prohibitively time-consuming to manually perform this analysis on large numbers of images. Advances in machine learning and computer vision have made high accuracy automated image interpretation possible. Here, we present application of machine learning and other high-throughput methods to TEM images for nanoparticle identification and microstructural characterization.