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

• Published in: Nature communications Vol. 10; no. 1; p. 1843
• Main Authors: Shen, Zhong-Hui, Wang, Jian-Jun, Jiang, Jian-Yong, Huang, Sharon X., Lin, Yuan-Hua, Nan, Ce-Wen, Chen, Long-Qing, Shen, Yang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-04-2019; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1037; 639/4077/4079; 639/766/1130; Computer simulation; Density; Design optimization; Dielectric breakdown; Dielectric strength; Energy storage; Humanities and Social Sciences; Learning algorithms; Machine learning; Molten salt electrolytes; multidisciplinary; Nanocomposites; Nanoparticles; Polymers; Science; Science (multidisciplinary); Solid electrolytes
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-09874-8

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.
Polymer dielectrics are promising for high-density energy storage but dielectric breakdown is poorly understood. Here, a phase-field model is developed to investigate electric, thermal, and mechanical effects in the breakdown process for a range of polymer dielectrics, and analytical expression for breakdown strength is provided by machine learning.