From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

TLDR

Understanding the behavior and failure of dielectric insulators under extreme electric fields is critical for current and emerging electrical devices, yet a predictive theory remains elusive due to its complex multiscale nature. The study aims to determine the intrinsic dielectric breakdown field of insulators, defined by their chemical composition, atomic structure, and bonding. Using a benchmark dataset of intrinsic breakdown fields from first‑principles calculations, the authors apply advanced statistical and machine learning techniques to derive simple phenomenological models that link breakdown field to accessible material properties. The resulting models are generalizable and can guide the systematic screening and identification of materials tolerant to high electric fields.

Abstract

Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. Despite its importance, the development of a predictive theory of dielectric breakdown has remained a challenge, owing to the complex multiscale nature of this process. Here, we focus on the intrinsic dielectric breakdown field of insulators—the theoretical limit of breakdown determined purely by the chemistry of the material, i.e., the elements the material is composed of, the atomic-level structure, and the bonding. Starting from a benchmark data set (generated from laborious first-principles computations) of the intrinsic dielectric breakdown field of a variety of model insulators, simple predictive phenomenological models of dielectric breakdown are distilled using advanced statistical or machine learning schemes, revealing key correlations and analytical relationships between the breakdown field and easily accessible material properties. The models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.