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

Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy

doi:10.1021/acs.chemmater.5b04109

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

Journal Article · Tue Feb 02 00:00:00 EST 2016 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.5b04109· OSTI ID:1255076

Kim, Chiho ^[1]; Pilania, Ghanshyam ^[2]; Ramprasad, Ramamurthy ^[1]

Univ. of Connecticut, Storrs, CT (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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. 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 dataset (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. Lastly, the models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Contributing Organization:: Univ. of Connecticut, Storrs, CT (United States)

Grant/Contract Number:: N00014-10-1-0944; N00014-15-1-2665; 20140679PRD3; AC52-06NA25396

OSTI ID:: 1255076

Report Number(s):: LA-UR-15-26967

Journal Information:: Chemistry of Materials, Vol. 28, Issue 5; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 148 works

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