Machine Learning–Guided Disulfide Modulation of Polyimides for High‐Performance and Self‐Healable Dielectric Energy Storage

ABSTRACT Conventional PI films exhibit excellent thermal stability; however, their weak self‐healing under electrical and mechanical stress limits operational reliability. Herein, a machine‐learning‐guided screening strategy is employed to predict the E b of sulfur‐modulated PI systems, enabling the rapid identification of optimal disulfide incorporation. As a result, a series of PI films with varying disulfide contents (0–30 wt.%) are fabricated. The results reveal a non‐monotonic dependence of E b on disulfide content, with an optimal composition (PI‐15) achieving a high E b of 700 kV mm −1 and a W dis of 10.02 J cm −3 at RT, together with 670 kV mm −1 and 6.64 J cm −3 at 150°C. Notably, after self‐healing, PI‐15 retains high performance, with E b recovering to 675 and 640 kV mm −1 and W dis to 8.59 and 6.02 J cm −3 at RT and 150°C, respectively. In addition, the films exhibit excellent operational stability under temperature, frequency, and fatigue cycling at 500 kV mm −1 . Mechanistically, disulfide incorporation enables a synergistic coupling of strengthened intermolecular interactions and dynamic bond exchange, suppressing charge transport and local field concentration while facilitating structural rearrangement. This work demonstrates new working mechanisms for designing high‐performance PI, providing strong potential for high‐temperature capacitive energy storage under harsh operating conditions.