Rapidly advancing computer technology has demonstrated great potential in recent years to assist in the generation and discovery of promising molecular structures. Herein, we present a data science-centric “Design-Discovery-Evaluation” scheme for exploring novel polyimides (PIs) with desired dielectric constants (ε). A virtual library of over 100,000 synthetically accessible PIs is created by extending existing PIs. Within the framework of quantitative structure-property relationship (QSPR), one model sufficient to predict ε at multi-frequencies is developed with R2 of 0.9768, allowing further high-throughput screening of the prior structures with desired ε. Furthermore, the structural feature representation method of atomic adjacent group (AAG) is introduced, upon which reliability of high-throughput screening results is evaluated. This workflow identifies 9 novel PIs (ε > 5 at 103 Hz and glass transition temperatures between 250°C and 350°C) with potential applications in high-temperature capacitive energy storage, and confirms these promising findings by high-fidelity molecular dynamics (MD) simulations.

This article is Open Access

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