Modeling the dielectric constants of crystals using machine learning

Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


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 behavior. Analytical models are often limited to a particular type of compound, 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 1364 dielectric constants. Analysis of Shapley additive explanations 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.

Original languageEnglish
Article number024503
JournalJournal of Chemical Physics
Issue number2
Publication statusPublished - 2020 Jul 14

Bibliographical note

Publisher Copyright:
© 2020 Author(s).

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry


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