Computer vision and machine learning are critical tools to support large-scale materials characterization and development of new materials. Quantified structure features that are extracted from the data can be leveraged in statistical and machine learning models that establish processing-structure-property-performance (PSPP) relationships to identify non-linear and unintuitive trends in the high dimensional materials development space further accelerating materials development. The aim of workshop is to bring together cross-disciplinary researchers to demonstrate recent advancements in machine learning, computer vision, and materials microscopy, and discuss open problems such as representation learning, uncertainty quantification, and explainability in materials microscopy analysis.