Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics
Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics
复制标题
聚合物电介质的电热机械击穿的相场建模和机器学习
DOI：
10.1038/s41467-019-09874-8
复制
发表时间：
2019-04-23
影响因子：
16.6
通讯作者：
Shen, Yang
中科院分区：
文献类型：
--
作者：
Shen, Zhong-Hui;Wang, Jian-Jun;Shen, Yang
了解聚合物基电介质的击穿机理是实现高密度储能的关键。本文建立了一个综合相场模型来研究聚合物基电介质击穿过程中的电、热和机械效应。对填充了不同性质纳米颗粒的P(VDF-HFP)基纳米复合材料进行了高通量模拟。在高通量模拟的数据库上进行机器学习，得到击穿强度的解析表达式，并通过有针对性的实验测量进行验证，该表达式可用于半定量预测P(VDF-HFP)基纳米复合材料的击穿强度。本工作为聚合物纳米复合电介质的击穿机理提供了基本的认识，并为优化其击穿强度，从而通过筛选合适的纳米膜来最大化其储能，建立了强有力的材料设计理论框架。它有可能被扩展到优化其他类型的材料的性能，如热电材料和固体电解质。
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.