Novel high voltage polymer insulators using computational and data-driven techniques

Deepak Kamal; Huan Tran; Chiho Kim; Yifei Wang; Lihua Chen; Yang Cao; V Roshan Joseph; Rampi Ramprasad

doi:10.1063/5.0044306

Novel high voltage polymer insulators using computational and data-driven techniques

J Chem Phys. 2021 May 7;154(17):174906. doi: 10.1063/5.0044306.

Authors

Deepak Kamal¹, Huan Tran¹, Chiho Kim¹, Yifei Wang², Lihua Chen¹, Yang Cao², V Roshan Joseph³, Rampi Ramprasad¹

Affiliations

¹ School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, USA.
² Department of Materials Science and Engineering, University of Connecticut, Storrs, Connecticut 06269, USA.
³ H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, Georgia 30332, USA.

PMID: 34241081
DOI: 10.1063/5.0044306

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 (E_gap) of the polymer and electron injection barrier (Φ_e) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φ_e and E_gap. 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 E_gap and Φ_e 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.