PNNL's new AI model for materials science can identify patterns in electron microscope images without human intervention. It also removes a barrier for autonomous experimentation on electron microscopes. Typically, to train an AI model to understand a phenomenon like radiation damage, researchers would painstakingly produce a hand-labeled dataset, manually tracing the radiation-damaged regions. Labeling datasets by hand is not ideal.