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The optimization of composition and processing to obtain materials thatexhibit desirable characteristics has historically relied on a combination ofscientist intuition, trial and error, and luck. We propose a methodology thatcan accelerate this process by fitting data-driven models to experimental dataas it is collected to suggest which experiment should be performed next. Thismethodology can guide the scientist to test the most promising candidatesearlier, and can supplement scientific intuition and knowledge with data-driveninsights. A key strength of the proposed framework is that it scales tohigh-dimensional parameter spaces, as are typical in materials discoveryapplications. Importantly, the data-driven models incorporate uncertaintyanalysis, so that new experiments are proposed based on a combination ofexploring high-uncertainty candidates and exploiting high-performing regions ofparameter space. Over four materials science test cases, our methodology led tothe optimal candidate being found with three times fewer required measurementsthan random guessing on average.

High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates