Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes

Na-ion batteries are considered a promising alternative to the analogous Li-ion batteries because of their low manufacturing cost, large abundance, and similar chemical/electrochemical properties. In particular, research on Na-ion solid electrolytes, which resolve the flammability issues associated with liquid electrolytes and increase the energy density obtained using a particular metal anode, is rapidly growing. However, the ionic conductivities of these materials are lower than those of liquids. We present a novel classification approach based on machine learning for identifying Na superionic conductor (NASICON) materials with outstanding ionic conductivities. We obtained new features based on chemical descriptors such as Na content, elemental radii, and electronegativity. We then classified 3573 NASICON structures by implementing the ensemble model of gradient boosting algorithms, with an average prediction accuracy of 84.2%. We further validated the thermodynamic stability and ionic conductivity values of the materials classified as superionic materials by employing density functional theory calculations and ab initio molecular dynamics simulations. Na 3 YTaSi 2 PO 12, Na 3 HfZrSi 2 PO 12, Na 3 LaTaSi 2 PO 12, and Na 3 ScTaSi 2 PO 12 were confirmed as promising NASICON structures that fulfill the requirements of solid-state electrolytes.