Machine learning-accelerated discovery of multi-cation entropy-stabilized NASICON solid electrolytes with 10,000 hours of stable Na plating/stripping for all-solid-state sodium batteries

Abstract

The application of medium-/high-entropy materials has revolutionized the design of solid-state electrolytes (SSEs) by stabilizing single-phase solutions from otherwise incompatible elements. However, navigating the vast compositional space of entropy-stabilized materials remains a significant challenge. To overcome this, we introduce a machine learning (ML)-accelerated approach to identify multi-cation NASICON oxide SSEs. By training a Gaussian Naive Bayes model on four key descriptors (ionic radius, electronegativity, valence state, and configurational entropy), we found four promising compositions incorporating Zr, Ti, Hf, Lu, Ga, and Sc. These compositions exhibit notable entropy-driven stabilization, demonstrated by the complete suppression of Na3PO4/ZrO2 impurity formation. Among them, the medium-entropy phase Na3.5Zr1.0Ti0.5Lu0.5Si2PO12 achieved remarkable performance, delivering an ionic conductivity of 1.3 mS cm-1 at room temperature, a critical current density of 1.9 mA cm-2, and over 10,000 hours of stable Na plating/stripping. When integrated into all-solid-state sodium batteries with a high-voltage Na3V2(PO4)2F3 cathode and a sodium anode, it further demonstrated exceptional battery performance indicators, including high-rate capability (110 mAh g-1 at 5 C) and long-term cycling stability (80% capacity retention after 700 cycles at 2 C). This work establishes entropy engineering, coupled with ML guidance, as a powerful paradigm for the rational design of next-generation SSEs.