Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently needed for a spectrum of electrical and electronic applications. Given the complexity of the dielectric breakdown mechanism and the vast chemical space of polymers, the discovery of suitable candidates is nontrivial. We have laid the foundation for a systematic search of the polymer chemical space, which starts with "gold-standard" experimental measurements and data on the temperature-dependent breakdown strength (Ebd) for a benchmark set of commercial dielectric polymer films. Phenomenological guidelines are derived from this data set on easily accessible properties (or "proxies") that are correlated with Ebd. Screening criteria based on these proxy properties (e.g., band gap, charge injection barrier, and cohesive energy density) and other necessary characteristics (e.g., a high glass transition temperature to maintain the thermal stability and a high dielectric constant for high energy density) were then setup. These criteria, along with machine learning models of these properties, were used to screen polymers candidates from a candidate list of more than 13 000 previously synthesized polymers, followed by experimental validation of some of the screened candidates. These efforts have led to the creation of a consistent and high-quality data set of temperature-dependent Ebd, and the identification of screening criteria, chemical design rules, and a list of optimal polymer candidates for high-temperature and high-energy-density capacitor applications, thus demonstrating the power of an integrated and informatics-based philosophy for rational materials design.
Keywords: breakdown; dielectric polymers; energy storage; informatics; machine learning.