Sodium-ion batteries are a potential low-cost rechargeable battery alternative with safer chemistries and less susceptible supply risks over contemporarily popular lithium-ion batteries [1]. However, the chemical differences between sodium and lithium complicate the substitution of sodium-ions into developed lithium-ion technologies. Currently, swapping sodium into lithium-based battery technologies results in sodium-ion batteries with short cycle lives, low coulombic efficiencies, and unstable solid-electrolyte interphases due to the increased redox-ion size and chemical incompatibilities [2]. Multicomponent electrolyte mixtures have been utilized to overcome these limitations in redox cycling by beneficially modulating ion solvation structures [3], although this approach has not yet been broadly investigated in sodium-ion batteries.
In this work, we combine batch Bayesian optimization (BO) and high-throughput cycling experiments to discover complex electrolyte mixtures with up to 10 components for sodium-ion batteries. We show that the BO approach effectively explores the high-dimensional electrolyte design space and quickly identifies mixtures with >99.9% coulombic efficiency, while testing <1% of the possible mixtures. By visualizing the Gaussian Process (GP) model predictions over the experimental design space [4], we identify electrolyte composition trends that advance our understanding of sodium-ion solvation, coulombic efficiency, and solid-electrolyte interphase stability. Overall, the combined automated experimental platform provides a new avenue to efficiently optimize electrolyte mixtures while learning chemical insights into material-property relationships.
Nayak, P.K., et al., From Lithium-Ion to Sodium-Ion Batteries: Advantages, Challenges, and Surprises. Angewandte Chemie International Edition, 2018. 57(1): p. 102-120.
Hu, Z., et al., Current Progress of Anode-Free Rechargeable Sodium Metal Batteries: Origin, Challenges, Strategies, and Perspectives. Advanced Functional Materials, 2024. 34(22): p. 2313823.
Kim, S.C., et al., High-entropy electrolytes for practical lithium metal batteries. Nature Energy, 2023. 8(8): p. 814-826.
Vela, B., et al., Visualizing high entropy alloy spaces: methods and best practices. Digital Discovery, 2025. 4(1): p. 181-194.
