Machine Learning Driven Design of Polymer Dielectrics for High Temperature Capacitive Energy Storage

Abstract

Polymer dielectric capacitors capable of stable operation under extreme conditions are essential for advancing electrification, yet conventional trial-and-error approaches are inefficient and severely hinder material discovery. Herein, we propose a machine learning driven high-throughput screening framework to accelerate the development of high performance capacitive energy storage materials for high temperature applications. Our studies demonstrate that introducing alicyclic units to modulates the formation of short-range ordered structures, which significantly suppress charge transport via electron localization. Experimental and simulation results demonstrate that the semi-aromatic polyimide film exhibits remarkable energy storage performance, with discharge energy density of 6.74 J cm -3 at 200 °C, and 4.45 J cm -3 at 250 °C while maintaining an efficiency of 90%. Furthermore, the semi-aromatic polyimide film exhibits outstanding selfcleaning capability and cycling reliability, enduring over 10 5 cycles under harsh conditions of 250 °C and 300 MV m -1 . This work illustrates a machine learning assisted strategy for developing high-temperature capacitive energy storage.