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, Tg) as screening criteria, we propose five representative polymers with desired ϵ and Tg 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.

Polymer informatics with multi-task learning

“…Data for 36 different properties of over 13,000 polymers (corresponding to over 23,000 data points) were obtained from a variety of sources. 4 , 12 , 13 , 14 , 15 , 16 , 17 , 18 Table 1 shows a synopsis of the data. All polymers are “fingerprinted”, i.e., converted to a machine-readable numerical form, using methods described elsewhere 4 , 19 , 20 (and briefly in the experimental procedures section).…”

“… 516 [1.29,2] 4 Dielectric constant 1 DFT 382 [2.6,9.1] 4 Frequency dependent electric constant b 1 Exp. 1187 [1.95, 10.4] 18 Mechanical Tensile strength MPa Exp. 672 [2.86,289] Young’s modulus Y MPa Exp.…”

Emergent Opportunities with Metallic Alloys: From Material Design to Optical Devices

“…and catalytical properties (e.g., reactant binding energy on alloy's crystalline facets) for various alloys, [139][140][141][142][143] and to predict ε for several compound materials and polymers. [144][145][146][147] The inverse design of alloys with targeted mechanical and electrical properties (e.g., tensile strength, conductivity, etc.) have also been realized using ML framework.…”

Dielectric polymers for high-temperature capacitive energy storage

“…Examples of the main-chain dipolar grafting polymers include but are not limited to aromatic PIs, [167][168][169] PAs, 170 and ArPUs. [171][172][173] The dielectric properties of the main-chain dipolar grafting polymers can be modulated by using different polar chain segments in the polymer backbone, incorporating additional permanent dipoles, varying conjugation length and controlling the degree of crystallinity. 44,174 For instance, the K values of ArPTUs are readily tuned through grafting polar chains with different lengths and flexibilities.…”