  Li-Ion Solid-State Electrolytes (Li-SSEs) are a promising solution that
resolves the critical issues of conventional Li-Ion Batteries (LIBs) such as
poor ionic conductivity, interfacial instability, and dendrites growth. In this
study, a platform consisting of a high-throughput screening and a
machine-learning surrogate model for discovering superionic Li-SSEs among
20,237 Li-containing materials is developed. For the training database, the
ionic conductivity of Na SuperIonic CONductor (NASICON) and Li SuperIonic
CONductor (LISICON) type SSEs are obtained from the previous literature. Then,
the chemical descriptor (CD) and additional structural properties are used as
machine-readable features. Li-SSE candidates are selected through the screening
criteria, and the prediction on the ionic conductivity of those is followed.
Then, to reduce uncertainty in the surrogate model, the ensemble method by
considering the best-performing two models is employed, whose mean prediction
accuracy is 0.843 and 0.829, respectively. Furthermore, first-principles
calculations are conducted for confirming the ionic conductivity of the strong
candidates. Finally, six potential superionic Li-SSEs that have not previously
been investigated are proposed. We believe that the constructed platform can
accelerate the search for Li-SSEs with high ionic conductivity at minimum cost.
