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.
Na3YTaSi2PO12, Na3HfZrSi2PO12, Na3LaTaSi2PO12, and Na3ScTaSi2PO12 were confirmed
as promising NASICON structures that fulfill the requirements of solid-state
electrolytes.