Prediction of Energy Storage Performance in Polymer Composites Using High-Throughput Stochastic Breakdown Simulation...

Area of Science:

• Materials Science
• Electrical Engineering
• Computational Physics

Background:

• Polymer dielectric capacitors are crucial for pulse power devices due to high power density.
• Pure polymers have low dielectric constants, necessitating inorganic fillers to enhance properties.
• Filler characteristics significantly influence the dielectric breakdown strength of polymer composites.

Purpose of the Study:

• To investigate the impact of filler dielectric constants, sizes, and contents on polymer composite breakdown strength.
• To develop a predictive model for breakdown strength and energy storage density using simulation and machine learning.
• To guide the design of high energy density polymer-based composites for capacitive energy storage.

Main Methods:

• High-throughput stochastic breakdown simulations were conducted on 504 datasets.
• Simulation results were used to train a machine learning model for breakdown strength prediction.
• Classical dielectric prediction formulas were combined with simulation data for energy storage density prediction.

Main Results:

• A predictive model for the breakdown strength of polymer-based composites was established.
• Energy storage density predictions were derived, correlating filler properties with performance.
• Experimental validation confirmed the accuracy of dielectric constant and breakdown strength predictions.

Conclusions:

• This research offers a computational approach to predict the performance of polymer dielectric composites.
• The study provides valuable insights for fabricating polymer-based composites with enhanced energy density for energy storage applications.
• The integration of simulation, machine learning, and experimental validation accelerates material design for advanced capacitors.