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 language | English (US) |
|---|---|
| Article number | 1843 |
| Journal | Nature communications |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2019 |
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All Science Journal Classification (ASJC) codes
- Chemistry(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Physics and Astronomy(all)
Cite this
}
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 journal › Article
TY - JOUR
T1 - Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics
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
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85064888610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064888610&partnerID=8YFLogxK
U2 - 10.1038/s41467-019-09874-8
DO - 10.1038/s41467-019-09874-8
M3 - Article
C2 - 31015446
AN - SCOPUS:85064888610
VL - 10
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 1843
ER -