Novel high voltage polymer insulators using computational and data-driven techniques
Abstract
One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode–insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 04, 2021
- Source ID
- 10.1063/5.0044306
Entities
People
- Chiho Kim
- Deepak Kamal
- Lihua Chen
- Rampi Ramprasad
- Tran Doan Huan
- V. Roshan Joseph
- Yang Cao
- Yifei Wang
Organizations
- Georgia Tech
- Toyota Research Institute
- University of Connecticut