Frequency-dependent dielectric constant prediction of polymers using machine learning

Abstract The dielectric constant (ϵ) is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors, microelectronic devices, and high-voltage insulations. However, agile discovery of polymer dielectrics with desirable ϵ remains a challenge, especially for high-energy, high-temperature applications. To aid accelerated polymer dielectrics discovery, we have developed a machine-learning (ML)-based model to instantly and accurately predict the frequency-dependent ϵ of polymers with the frequency range spanning 15 orders of magnitude. Our model is trained using a dataset of 1210 experimentally measured ϵ values at different frequencies, an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm. The developed ML model is utilized to predict the ϵ of synthesizable 11,000 candidate polymers across the frequency range 60–10¹⁵ Hz, with the correct inverse ϵ vs. frequency trend recovered throughout. Furthermore, using ϵ and another previously studied key design property (glass transition temperature, T_g) as screening criteria, we propose five representative polymers with desired ϵ and T_g for capacitors and microelectronic applications. This work demonstrates the use of surrogate ML models to successfully and rapidly discover polymers satisfying single or multiple property requirements for specific applications.