A Self-Driving Laboratory for Accelerated Electrolyte Discovery

Abstract The development of electrochemical materials is constrained by extensive formulation spaces and the low throughput of manual screening. Here, we report a self-driving electrochemical laboratory integrating liquid-handling robotics, online electroanalysis, and Bayesian optimization to accelerate electrolyte discovery. Operating in a 96-well microtiter format, the platform automatically formulates candidate electrolytes, performs temperature-controlled electrochemical measurements, and iteratively updates surrogate models to explore high-dimensional chemical spaces. This framework was applied to the development of high-entropy aqueous electrolytes for low-temperature zinc metal batteries. The system screened hundreds of formulations, and multiobjective optimization yielded an electrolyte with a conductivity of 134.1 mS cm–1 and an electrochemical stability window of 2.50 V, with ∼2019 h of cycling stability in symmetric cells at –20 °C. This approach significantly reduces experimental labor while accelerating the development of electrochemical materials.