Designing a material is fundamentally a complex function that takes material formulation as input and outputs performance. However, new battery designs can require extensive manual testing for material optimization, which can take years. High-performance batteries are crucial to the electrification of transportation and aviation 1, 2.

All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite ∣ ∣LiNi 0.5Mn 0.3Co 0.2O 2 pouch cell configuration. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named 'Clio”) to machine-learning (a Bayesian optimization-based experiment planner named 'Dragonfly”). In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. However, battery innovations can take years to deliver. Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation.

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