The increasing demand for innovative designs often outpaces the rate at which new designs are developed, in part, due to the time needed to search within a vast set of alternatives, especially when evaluating alternatives requires a time-consuming experiment. For example, although novel configurationally complex materials have the potential to impact a diverse set of applications including neuromorphic memories and correlated electron materials, searching their design parameter space is currently prohibitively difficult using existing methods. Helping to accelerate the search for designs, modern sequential experimental design (SED) methods iteratively use machine learning to predict the best points to sample. This project seeks to develop new machine learning methods for optimizing configurationally complex materials, accounting for the complexity associated with conducting physical experiments.

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