  The relative permittivity of a crystal is a fundamental property that links
microscopic chemical bonding to macroscopic electromagnetic response. Multiple
models, including analytical, numerical and statistical descriptions, have been
made to understand and predict dielectric behaviour. Analytical models are
often limited to a particular type of compounds, whereas machine learning (ML)
models often lack interpretability. Here, we combine supervised ML, density
functional perturbation theory, and analysis based on game theory to predict
and explain the physical trends in optical dielectric constants of crystals.
Two ML models, support vector regression and deep neural networks, were trained
on a dataset of 1,364 dielectric constants. Shapley additive explanations
(SHAP) analysis of the ML models reveals that they recover correlations
described by textbook Clausius-Mossotti and Penn models, which gives confidence
in their ability to describe physical behavior, while providing superior
predictive power.
