Accelerating Aqueous Electrolyte Design with Automated Full-Cell Battery Experimentation and Bayesian Optimization

The integration of automation and data-driven methodologies offer a promising approach to accelerating materials discovery in energy storage research. Thus far, in battery research, coin-cell assembly has advanced to become near fully-automated but remains largely disconnected from data-driven methods. To bridge the disconnect, this work presents a self-driving laboratory framework to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO4||Li4Ti5O12 organic-aqueous full-cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The study explored an organic-aqueous hybrid electrolyte system comprising four co-solvents and two lithium-conducting salts. Using this framework, cells with optimized electrolyte cycled with at least 94% Coulombic efficiency. Additionally, online electrochemical mass spectrometry revealed the optimized organic co-solvents successfully mitigated parasitic hydrogen evolution reaction. The results highlight the potential of combining Bayesian optimization with autonomous full-cell experimentation, while contributing new electrolyte design insights for next-generation aqueous batteries.