Reducing false positives in the search for solid-state battery coatings

Computational searches for protective coatings stabilizing electrode-electrolyte interfaces in solid-state batteries often rely on the Li-ion migration barrier as a descriptor of ionic conductivity. While attractive for high-throughput screening, this approach can be misleading unless the equilibrium concentration of defects governing ionic transport is explicitly considered. Here, using density functional theory and machine learning, we show that accounting for dopability and dopant-defect binding, key factors controlling the concentration of mobile charge carriers, substantially reduces false positives in the selection funnel. Among the remaining candidates, we identify ones with good interfacial adhesion and low charge-transfer migration barriers – essential properties for reliable solid-solid interfaces. As a result, we predict Ge-doped Li 3 AlN 2 , a previously unreported material, as a promising interlayer for the Li-anode | Li 7 La 3 Zr 2 O 12 -electrolyte interface. This study outlines a practical approach to selecting coatings with adequate ionic conductivity for solid-state batteries.