Computer vision approaches have the potential to accelerate the characterization process of novel alloys that are in demand for applications ranging from solar to structural materials . Innovating new alloys is challenging, in part because the composition space is vast, requiring numerous cycles of synthesis and characterization . Fur-thermore, numerous cycles of learning are time-and costprohibitive to perform manually. These pain points in the domain of material synthesis can be targeted via laboratory automation. Siemenn et al. and MacLeod et al. recently demonstrated autonomous hardware systems capable of significantly accelerating the cycles of learning for material synthesis. However, to realistically accelerate the process of novel material discovery, one has to match the high-throughput synthesis loop with equally as fast characterization. The scalability of the pipeline is not only a matter of accelerating the characterization of individual samples but depends also on the breadth of the pipeline, e.g., the number of samples required to be characterized. Computer vision has the capacity to scale to arbitrarily many samples without significantly increasing the duration of each characterization step .