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

Abstract

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 20 hours and finding conductivities up to 32.13 S m⁻¹. These results demonstrate how closed-loop autonomous experimentation and optimization accelerate the identification of electrolytes with the highest conductivity across a large multicomponent composition space, while minimizing experimental variability. This work lays the foundation for broader electrochemical studies using the BRINE platform.