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Autonomous experimentation (AE) combines machine learning and researchhardware automation in a closed loop, guiding subsequent experiments towarduser goals. As applied to materials research, AE can accelerate materialsexploration, reducing time and cost compared to traditional Edisonian studies.Additionally, integrating knowledge from diverse sources including theory,simulations, literature, and domain experts can boost AE performance. Domainexperts may provide unique knowledge addressing tasks that are difficult toautomate. Here, we present a set of methods for integrating human input into anautonomous materials exploration campaign for composition-structure phasemapping. The methods are demonstrated on x-ray diffraction data collected froma thin film ternary combinatorial library. At any point during the campaign,the user can choose to provide input by indicating regions-of-interest, likelyphase regions, and likely phase boundaries based on their prior knowledge(e.g., knowledge of the phase map of a similar material system), along withquantifying their certainty. The human input is integrated by defining a set ofprobabilistic priors over the phase map. Algorithm output is a probabilisticdistribution over potential phase maps, given the data, model, and human input.We demonstrate a significant improvement in phase mapping performance givenappropriate human input.

Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping