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Automation and machine learning techniques are poised to dramatically accelerate the development of new materials while simultaneously increasing our understanding of the physics and chemistry that underlie the formation of such materials. In particular, the convergence of accessible machine learning tools, the availability of high-quality data, and the advent of accessible experimental automation platforms have led to a number of closed-loop autonomous experimentation platforms or “self-driving labs”. Such platforms integrate robotic experimenters with AI-guided experiment planning to autonomously perform large numbers of experiments without human input. After briefly reviewing the state of the field and the broad classes of autonomous efforts, this perspective outlines several high-value focus areas for future ML-guided characterization efforts. Among many advantages, we expect that autonomous approaches will allow the systematic study of rare and nonequilibrium phenomena, provide dramatically greater measurement efficiency through targeting of cutting-edge, resource-intensive characterization, and enable a higher level of thinking and experimental planning for human investigators. Finally, we outline the principal barriers to realization of these advantages, including: (1) a lack of organizational structures and workforce development for the highly interdisciplinary programs needed; (2) funding and publication mechanisms that assign greater value to individual scientific results than foundational infrastructure development; and (3) a dearth of standards for open interchange of hardware, software, and data among the polymer community. We believe that we are in the early days of a once-in-a-generation shift in the way science is planned, executed, and evaluated, and we hope to provide a blueprint for the broader polymer community to take a leading role in this shift.
Beaucage et al. (Sat,) studied this question.