Researchers are exploring the use of machine learning in scanning transmission electron microscopy to automate experimentation. Various studies have been published on the application of different machine learning techniques, including convolutional variational autoencoders, graph neural networks, and nonnegative matrix factorization, to improve analysis and processing of electron microscopy data. These advancements aim to enhance the capabilities of electron microscopy, potentially leading to new possibilities in the field.