Data-Driven Electrolyte Discovery Through Autonomous Battery Assembly, Testing, and Bayesian Optimization for Sustainable Aqueous Batteries
The rapid development of next-generation energy storage technologies increasingly depends on the ability to efficiently explore high-dimensional materials and formulation spaces. Automation combined with data-driven optimization provides a powerful pathway to accelerate discovery while reducing experimental cost and human bias. This presentation highlights our recent advances in self-driving laboratory frameworks for battery electrolyte engineering, with a focus on Bayesian optimization (BO) integrated with an automated coin-cell assembly and electrochemical testing platform (ODACell 2) across both lithium- and zinc-based aqueous battery chemistries. In lithium-based systems, we demonstrate a closed-loop experimental workflow that integrates automated coin-cell assembly, galvanostatic cycling of LiFePO 4 || Li 4 Ti 5 O 12 organic–aqueous full cells, and BO-driven experiment selection. This approach addresses a longstanding disconnect between highly automated cell assembly and data-driven decision-making in battery research. By navigating a multi-component electrolyte design space comprising four organic co-solvents and two lithium salts, the framework rapidly identified formulations achieving ≥ 94% Coulombic efficiency. Coupling the autonomous workflow with online electrochemical mass spectrometry further enabled mechanistic insight, revealing that optimized co-solvent combinations effectively suppress parasitic hydrogen evolution. Complementary advances are presented for aqueous zinc batteries. Coulombic efficiency was optimized in Cu || Zn cells across a five-dimensional electrolyte space comprising ZnCl 2 , ZnSO 4 , and multiple functional additives. The data-efficient optimization revealed strong salt-dependent behavior, including pronounced sensitivity of ZnCl 2 electrolytes to concentration, stabilizing effects of ZnSO 4 -based systems, and nuanced additive–salt interactions. While BO proved highly effective for rapid screening and performance mapping, limitations in model interpretability near design-space boundaries underscore the need for careful experimental design and validation strategies. Together, these studies establish Bayesian optimization as a practical, scalable tool for electrolyte discovery when tightly integrated with laboratory automation. By spanning distinct battery chemistries and experimental objectives, this work illustrates both the opportunities and challenges of deploying data-driven methods in real-world electrochemical systems. The presented frameworks provide a foundation for extending autonomous, data-efficient optimization toward predictive electrolyte design and broader adoption in advanced energy storage research.