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

Na-ion batteries are considered a promising alternativeto theanalogous 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 resolvethe flammability issues associated with liquid electrolytes and increasethe energy density obtained using a particular metal anode, is rapidlygrowing. However, the ionic conductivities of these materials arelower than those of liquids. We present a novel classification approachbased on machine learning for identifying Na superionic conductor(NASICON) materials with outstanding ionic conductivities. We obtainednew features based on chemical descriptors such as Na content, elementalradii, and electronegativity. We then classified 3573 NASICON structuresby implementing the ensemble model of gradient boosting algorithms,with an average prediction accuracy of 84.2%. We further validatedthe thermodynamic stability and ionic conductivity values of the materialsclassified as superionic materials by employing density functionaltheory calculations and ab initio molecular dynamics simulations.Na3YTaSi2PO12, Na3HfZrSi2PO12, Na3LaTaSi2PO12, and Na3ScTaSi2PO12 were confirmedas promising NASICON structures that fulfill the requirements of solid-stateelectrolytes.

Automated summary

Na-ion batteries are a promising alternative to Li-ion batteries due to their low cost and abundance. Researchers are focusing on Na-ion solid electrolytes to address safety concerns and increase energy density. A machine learning approach was used to classify 3573 NASICON structures based on chemical descriptors, resulting in an average prediction accuracy of 84.2%. Promising NASICON structures, such as Na3YTaSi2PO12 and Na3HfZrSi2PO12, were identified and validated through computational simulations.