Distinguishing the Mixed Conduction Interphase: a Machine Learning Molecular Dynamics Study on the Na 3 PS 4 /na Battery Interface

Abstract The interphase formed between solid electrolytes and alkali metal anodes determines whether decomposition is self-passivating, solid electrolyte interphase (SEI) or continuously propagating, mixed conducting interphase (MCI). We employ machine learning interatomic potential molecular dynamics to simulate the Na 3 PS 4 /Na interface at scales of 500,000 atoms and 10 ns. Three features distinguish this system from the passivating Li 7 P 3 S 11 /Li interface: (1) P–P transport correlations enabling cooperative phosphorus migration toward the anode, (2) amorphous Na 2 S domains lacking nanocrystalline order, and (3) persistent Na–P connectivity pathways spanning the interphase from the onset of decomposition. These characteristics are consistent with mixed ionic-electronic conducting behavior and inconsistent with self-limiting SEI character. While definitive classification requires direct electronic conductivity measurements of the amorphous interphase, this work establishes atomistic mechanistic criteria for interphase classification and demonstrates that MLIP simulations can access the length and time scales necessary to resolve transport mechanisms at solid electrolyte interfaces.