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

36 Scopus citations

Abstract

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
Volume153
Issue number2
DOIs
StatePublished - 14 Jul 2020

Bibliographical note

Publisher Copyright:
© 2020 Author(s).

Fingerprint

Dive into the research topics of 'Modeling the dielectric constants of crystals using machine learning'. Together they form a unique fingerprint.

Cite this