Towards Self-Driving Labs for Better Batteries: Accelerating Electrolyte Discovery with Automation 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, which have been primarily developed for computational or multi-fidelity datasets. To bridge the disconnect, this work presents a self-driving laboratory framework designed to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO4||Li4Ti5O12 full cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The integration of Bayesian optimization highlights machine-intelligent experimental decision-making enabling closed-loop experimentation-analysis workflow for self-driving laboratories. The study focuses on an organic-aqueous hybrid electrolyte system comprising four co-solvents dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water—and two salts, lithium perchlorate and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Using this framework, high- performing electrolyte formulations were identified, with water content emerging as a key performance factor. Online electrochemical mass spectrometry provided insights into hydrogen evolution as a significant side reaction, while revealing limited effects due to co-solvent interactions. The results highlight the potential of combining Bayesian optimization with autonomous experimentation for improving efficiency in materials research, while contributing new insights into electrolyte behavior for next-generation aqueous lithium-ion batteries.