BRINE: a cost-effective electrochemical self-driving laboratory for accelerated discovery of high-performance electrolytes

The discovery of next-generation battery electrolytes increasingly involves complex, multicomponent formulations that demand high-throughput, systematic exploration. We present the Bayesian Robotic Investigator of Novel Electrolytes (BRINE), a cost-effective, self-driving laboratory (SDL) that autonomously prepares and tests mixed electrolyte solutions. BRINE combines an open-source liquid-handling robot with a potentiostat and custom-made electrodes to mix reagents and perform electrochemical measurements without human intervention. A Bayesian optimization routine navigates multidimensional composition spaces, allowing the platform to rapidly identify promising formulations. As a proof of concept, BRINE mapped ionic conductivity in two aqueous electrolyte spaces (i) aqueous mixtures of NaCl, KCl, MgCl2, and CaCl2, and (ii) battery-oriented mixtures containing ZnCl2, KCl, NH4Cl, NaCl, and EMIMCl, testing ~230 unique compositions in under 18 hours and finding conductivities up to 32.13 S/m. These results demonstrate how closed-loop robotics and optimization accelerate electrolyte discovery while minimizing experimental variability, laying the foundation for broader electrochemical studies with the BRINE platform.