Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics

Zhong Hui Shen, Jian Jun Wang, Jian Yong Jiang, Sharon X. Huang, Yuan Hua Lin, Ce Wen Nan, Long Qing Chen, Yang Shen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.

Original languageEnglish (US)
Article number1843
JournalNature communications
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2019

Fingerprint

Nanocomposites
machine learning
learning
Learning systems
Polymers
Hot Temperature
breakdown
Energy storage
polymers
Throughput
nanocomposites
Solid electrolytes
energy storage
Nanoparticles
Electrolytes
Screening
Databases
solid electrolytes
temperature effects
Machine Learning

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Cite this

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title = "Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics",
abstract = "Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.",
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Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics. / Shen, Zhong Hui; Wang, Jian Jun; Jiang, Jian Yong; Huang, Sharon X.; Lin, Yuan Hua; Nan, Ce Wen; Chen, Long Qing; Shen, Yang.

In: Nature communications, Vol. 10, No. 1, 1843, 01.12.2019.

Research output: Contribution to journalArticle

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AU - Shen, Zhong Hui

AU - Wang, Jian Jun

AU - Jiang, Jian Yong

AU - Huang, Sharon X.

AU - Lin, Yuan Hua

AU - Nan, Ce Wen

AU - Chen, Long Qing

AU - Shen, Yang

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