  Recently, machine-learning approaches have accelerated computational
materials design and the search for advanced solid electrolytes. However, the
predictors are currently limited to static structural parameters, which may not
fully account for the dynamic nature of ionic transport. In this study, we
meticulously curated features considering dynamic properties and developed
machine-learning models to predict the ionic conductivity of solid
electrolytes. We compiled 14 phonon-related descriptors from first-principles
phonon calculations along with 16 descriptors related to structure and
electronic properties. Our logistic regression classifiers exhibit an accuracy
of 93 %, while the random forest regression model yields a root mean square
error of 1.179 S/cm and $R^2$ of 0.710. Notably, phonon-related features are
essential for estimating the ionic conductivity in both models. Furthermore, we
applied our prediction model to screen 264 Li-containing materials and
identified 11 promising candidates as potential superionic conductors.
