From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
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. 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.
- Authors:
-
- Univ. of Connecticut, Storrs, CT (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- Contributing Org.:
- Univ. of Connecticut, Storrs, CT (United States)
- OSTI Identifier:
- 1255076
- Report Number(s):
- LA-UR-15-26967
Journal ID: ISSN 0897-4756
- Grant/Contract Number:
- N00014-10-1-0944; N00014-15-1-2665; 20140679PRD3; AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Chemistry of Materials
- Additional Journal Information:
- Journal Volume: 28; Journal Issue: 5; Journal ID: ISSN 0897-4756
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Kim, Chiho, Pilania, Ghanshyam, and Ramprasad, Ramamurthy. From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown. United States: N. p., 2016.
Web. doi:10.1021/acs.chemmater.5b04109.
Kim, Chiho, Pilania, Ghanshyam, & Ramprasad, Ramamurthy. From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown. United States. https://doi.org/10.1021/acs.chemmater.5b04109
Kim, Chiho, Pilania, Ghanshyam, and Ramprasad, Ramamurthy. Tue .
"From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown". United States. https://doi.org/10.1021/acs.chemmater.5b04109. https://www.osti.gov/servlets/purl/1255076.
@article{osti_1255076,
title = {From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown},
author = {Kim, Chiho and Pilania, Ghanshyam and Ramprasad, Ramamurthy},
abstractNote = {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.},
doi = {10.1021/acs.chemmater.5b04109},
journal = {Chemistry of Materials},
number = 5,
volume = 28,
place = {United States},
year = {Tue Feb 02 00:00:00 EST 2016},
month = {Tue Feb 02 00:00:00 EST 2016}
}
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