Data science-centric design, discovery, and evaluation of novel synthetically accessible polyimides with desired dielectric constants

Abstract

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), a model sufficient to predict ε at multiple frequencies is developed with an 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, using which the 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.