TY - JOUR
T1 - Modeling the dielectric constants of crystals using machine learning
AU - Morita, Kazuki
AU - Davies, Daniel W.
AU - Butler, Keith T.
AU - Walsh, Aron
N1 - Funding Information:
The authors thank financial support from the Yoshida Scholarship Foundation, the Japan Student Services Organization, and the Centre for Doctoral Training on Theory and Simulation of Materials at Imperial College London. This research was also supported by the Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant No. 2018M3D1A1058536). Through our membership of the UK’s HEC Materials Chemistry Consortium, which is funded by the EPSRC (Grant No. EP/L000202), this work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk).
Publisher Copyright:
© 2020 Author(s).
PY - 2020/7/14
Y1 - 2020/7/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088158134&partnerID=8YFLogxK
U2 - 10.1063/5.0013136
DO - 10.1063/5.0013136
M3 - Article
C2 - 32668921
AN - SCOPUS:85088158134
SN - 0021-9606
VL - 153
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
IS - 2
M1 - 024503
ER -