Title: High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates,
Abstract: The optimization of composition and processing to obtain materials that
exhibit desirable characteristics has historically relied on a combination of
scientist intuition, trial and error, and luck. We propose a methodology that
can accelerate this process by fitting data-driven models to experimental data
as it is collected to suggest which experiment should be performed next. This
methodology can guide the scientist to test the most promising candidates
earlier, and can supplement scientific intuition and knowledge with data-driven
insights. A key strength of the proposed framework is that it scales to
high-dimensional parameter spaces, as are typical in materials discovery
applications. Importantly, the data-driven models incorporate uncertainty
analysis, so that new experiments are proposed based on a combination of
exploring high-uncertainty candidates and exploiting high-performing regions of
parameter space. Over four materials science test cases, our methodology led to
the optimal candidate being found with three times fewer required measurements
than random guessing on average.