Data for the paper "Unlocking a shortcut: Acceleration of non-aqueous electrolyte development by active machine learning for sodium-based batteries"

Experimental and numerical data collected and analyzed within the paper "Unlocking a shortcut: Acceleration of non-aqueous electrolyte development by active machine learning for sodium-based batteries". The data include experimental ionic conductivities for Na-based and Li-based non-aqueous electrolytes, galvanostatic cycling performance for optimized Na-based electrolyte formulations and synthetic data to compare different active learning algorithms.