Publication date: 15th December 2025
Aqueous batteries provide a safe, low-cost and sustainable option for large-scale energy storage, but their performance is often limited by the instability of the electrode–electrolyte interphase (EEI). The EEI plays a central role in regulating ion transport, nucleation, parasitic reactions and long-term cycling behaviour, yet its structure and evolution depend on a complex interplay of factors – electrolyte composition, additive chemistry, temperature, current density and cycling conditions. This creates a large, nonlinear design space that is difficult to probe through traditional, manually performed experiments, which are typically slow, path-dependent and hard to reproduce at scale. Self-driving laboratory (SDL) architectures offer a way to overcome these challenges by combining automated experimentation, standardized measurement workflows and machine-learning-guided decision making. Within the Inorganic SDL at the Acceleration Consortium, automated electrochemical testing, high-throughput characterization and active-learning experiment planners are integrated to systematically explore this landscape. The current platform uses aqueous Zn batteries as a model system, enabling closed-loop studies of electrolyte formulations and operating conditions supported by rapid measurements of Coulombic efficiency, impedance and in-situ optical microscopy of interfacial evolution. These datasets are further enriched by SEM and XPS analyses, linking additive chemistry to interfacial morphology and surface composition.
By uniting statistically robust, reproducible experimentation with adaptive machine-learning strategies, this SDL approach is positioned to provide quantitative insight into the structure–function relationships that govern EEI formation, evolution and degradation. Such mechanistic insight accelerates the development of interphases for next-generation aqueous batteries and establishes a scalable paradigm for autonomous discovery in electrochemical materials research.
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