  Solid state lithium- and sodium-ion batteries utilize solid ionicly
conducting compounds as electrolytes. However, the ionic conductivity of such
materials tends to be lower than their liquid counterparts, necessitating
research efforts into finding suitable alternatives. The process of electrolyte
screening is often based on a mixture of domain expertise and trial-and-error,
both of which are time and resource-intensive. Data-driven and machine learning
approaches have recently come to the fore to accelerate learnings towards
discovery. In this work, we present a simple machine-learning based approach to
predict the ionic conductivity of sodium and lithium-based SICON compounds.
Using primarily theoretical elemental feature descriptors derivable from
tabulated information on the unit cell and the atomic properties of the
components of a target compound on a limited dataset of 70 NASICON-examples, we
have designed a logistic regression-based model capable of distinguishing
between poor and good superionic conductors with a cross-validation accuracy of
over 82%. Moreover, we demonstrate how such a system is capable of cross-domain
classification on lithium-based examples at the same accuracy, despite being
introduced to zero lithium-based compounds during training. Through a
systematic permutation-based evaluation process, we reduced the number of
considered features from 47 to 7, reduction of over 83%, while simultaneously
improving model performance. The contributions of different electronic and
structural features to overall ionic conductivity is also discussed, and
contrasted with accepted theories in literature. Our results demonstrate the
utility of such a simple, yet interpretable tool provides opportunities for
initial screening of potential candidates as solid-state electrolytes through
the use of existing data.

Materials Science