Publication date: 15th December 2025
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, by navigating a multi-component electrolyte design space comprising four organic co-solvents and two lithium salts, the framework rapidly identified formulations achieving ≥ 95% Coulombic efficiency for an LiFePO4 || Li4Ti5O12 model chemistry. Coupling the autonomous workflow with online electrochemical mass spectrometry further enabled mechanistic insight, revealing that optimized cosolvent 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 ZnCl2, ZnSO4, and three functional additives. The data-efficient optimization revealed nuanced additive–salt interactions. Electrochemical quartz crystal microbalance experiments provided a quantitative assessment of the solid-electrolyte interphase formed from the surveyed electrolyte formulations. 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.
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