  Na-ion solid-state electrolytes (Na-SSE) exhibit high potential for
electrical energy storage owing to their high energy densities and low
manufacturing cost. However, their mechanical properties critical to maintain
structural stability at the interface are still insufficiently understood. In
this study, a machine learning based regression model was developed for
predicting the mechanical properties of Na-SSEs. As a training set, 12,361
materials were obtained from a well-known materials database (Materials
Project) and were represented with their respective chemical and structural
descriptors. The developed surrogate model exhibited a remarkable accuracy (R2
score) of 0.72 and 0.87, with a mean absolute error of 11.8 GPa and 15.3 GPa
for the shear and bulk modulus, respectively. This model was then applied to
predict the mechanical properties of 2,432 Na-SSEs, the properties of which
have been validated with first principles calculations. Finally, the
optimization process was performed to develop an ideal materials screening
platform by adding the new minimized dataset, wherein the prediction
uncertainty is reduced. We believe that the platform proposed in this study can
accelerate the search for Na-SSEs with ideal mechanical properties at minimum
cost.
