The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. This process is traditionally driven by subject expertise and the creativity of individual researchers, who “close the loop” by updating their hypotheses and models in light of new data or knowledge acquired from the community. Since the early 2000s, there has been notable progress in the automation of each step of the scientific process. With recent advances in using machine learning for hypothesis generation and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process has emerged as an exciting research frontier. The future of fully autonomous research systems for materials science no longer feels far-fetched. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This paper provides a brief overview of closed-loop research systems of today, and our related work at the Toyota Research Institute applied across different materials challenges and identifies both limitations and future opportunities.
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