Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes

Na-ion solid-state electrolytes (Na-SSEs)
exhibit high potential
for electrical energy storage owing to their high energy densities
and low manufacturing cost. However, their mechanical properties that
are critical to maintaining 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 remarkable accuracies (R2 score) of 0.72 and 0.87, with mean absolute errors of
11.8 and 15.3 GPa for the shear and bulk modulus, respectively. This
model was then applied to predict the mechanical properties of 2432
Na-SSEs, which have been validated with first-principles calculations.
Finally, the optimization process was performed to develop an ideal
materials screening platform by adding the minimized data set, 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.