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We have developed a first-of-its-kind Autonomous Research System, ARES, capable of designing, executing, and analyzing its own experiments autonomously using artificial intelligence (AI) and Machine Learning (ML). The closed loop, iterative method enables ARES to design new experiments based on prior results dynamically, after each experiment; a first for materials research.

We are applying this method to understand and control the synthesis of single wall carbon nanotubes, in this case optimizing growth rate in (7) – dimensional parameter space. We use automated in situ Raman spectroscopy characterization of growth rate for CVD synthesis of carbon nanotubes as a metric for a target objective used by our AI planner. We use a random forest learning approach which models experimental results, and a genetic algorithm planner to propose new experiments expected to achieve the targeted growth rate.

We expect ARES to be a disruptive advance in the near future, combining advances in robotics, AI, data sciences and operando methods to enable us to attack high dimensional research problems that were previously intractable by current research processes. We are applying the ARES method to multiple problems, including Additive Manufacturing and defect engineering in graphene. Human-robot research teams have to potential to redefine the research process and lead to a Moore’s Law for the speed of research.

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