Unveiling Electrolyte Design Principles for Sodium-Ion Batteries Using Combinatorial Electrochemistry and Machine Learning-Assisted Analysis

To accelerate the development of high-performance electrolytes for sodium-ion batteries (SIBs), we systematically investigated the effects of three key parameters, NaFSI concentration, DMC/EMC ratio, and FEC content, on the performance of SIB using NaNi1/3Fe1/3Mn1/3O2 and hard carbon as the positive and negative electrodes. A total of 132 electrolyte formulations were prepared using automated liquid handling, and their electrochemical performance was evaluated using multichannel full-cell measurements. Data-driven analysis employing machine learning revealed that NaFSI concentration plays the most critical role in enabling highly reversible charge–discharge behavior. Long-term cycling tests and interfacial composition analyses were further conducted to clarify the influence of electrolyte components on stability. Detailed studies focusing on high-NaFSI, FEC-containing electrolytes showed that EMC-rich formulations outperformed DMC-rich counterparts, maintaining Coulombic efficiencies over 99.6% even after 300 cycles. X-ray photoelectron spectroscopy confirmed that these stable systems promote the formation of NaF-rich interphases on both electrodes. These findings provide valuable insights into electrolyte design strategies for durable and efficient SIBs and highlight the utility of high-throughput experimentation coupled with machine learning for electrolyte discovery.