Design of Polymers for Energy Storage Capacitors Using Machine Learning and Evolutionary Algorithms

Many applications, such as electric vehicles and switched-mode power supplies, require capacitors that have high energy density, operating temperature, dielectric breakdown strength, and failure tolerance. Modern polymer film capacitors are useful due to their high failure tolerance; however, they suffer from low energy density per volume and low thermal stability. By utilizing a genetic algorithm approach, we have designed hypothetical polymers with bandgaps above 5 eV, glass transition temperatures above 500 K, and dielectric constants above 4 at 100 Hz. These are useful properties, as a high bandgap can be used as a proxy for dielectric breakdown field strength, a high glass transition temperature indicates the polymer can function uniformly from low to high temperatures, and a high dielectric constant improves energy density per volume. Over 10,000 hypothetical polymers were designed, which have been further down selected (and recommended for synthesis) based on synthesis feasibility considerations.