Understanding the properties of materials requires structural characterization over large areas and different scales to link microstructure with performance. Here, we demonstrate a single-beam high-throughput scanning electron microscope allowing the collection of both secondary electron and backscattered electron signals over large areas. Combined with machine learning, a high efficiency in material research is achieved, illustrated here by a multiscale investigation of carbides in a second-generation nickel-base single-crystal superalloy. The resulting terabyte-sized panoramic atlas data, combined with conventional electron microscopy, enable a simultaneous multiscale analysis of carbide evolution during creep regarding specific type, location, composition, size, shape, and relationship with the matrix, providing sample-scale quantitative statistical data and giving a precise insight into the effect of carbides in the superalloy in a way not previously possible.