Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries <i>via</i> Data-Driven Approaches

Li-ion solid-state electrolytes (SSEs)
have great potential, but
their commercialization is limited due to interfacial contact stability
issues and the formation and growth of dendrites. In this study, a
machine learning regression algorithm was implemented to screen for
mechanically superior SSEs among 17,619 candidates. Elasticity information
(14,238 structures) was imported from an available database, and their
machine learning descriptors were constructed using physiochemical
and structural properties. A surrogate model for predicting the shear
and bulk moduli exhibited R2 values of
0.819 and 0.863, respectively. The constructed model was applied to
predict the elastic properties of potential SSEs, and first-principles
calculations were conducted for validation. Furthermore, the application
of an active learning process, which reduced the prediction uncertainty,
was clearly demonstrated to improve the R2 score from approximately 0.6–0.8 by adding only 32–63%
of new data sets depending on the type of modulus. We believe that
the current model and additional data sets can accelerate the process
of finding optimal SSEs to satisfy the mechanical conditions being
sought.