Machine Learning-Enabled Parallel Prediction and Co-Design of Polyimides for Tailored Glass Transition, Dielectric, and Bandgap Properties.

ABSTRACT: Polyimide (PI) is widely used in aerospace, nuclear industries, microelectronics, and flexible devices due to its exceptional thermal stability, electrical insulation, mechanical strength, and low dielectric loss. However, developing high-performance PIs traditionally requires decades of experimental effort. To accelerate discovery, we integrated machine learning (ML) with molecular dynamics (MD) and quantum mechanics (QM) simulations, demonstrating the efficacy of data-driven approaches for novel PI development. We collected 1499 PI structures from relevant literature, with experimentally measured glass transition temperature (T _g), dielectric constant (DC), and bandgap width (EG). Subsequently, substructure information was extracted from their SMILES encoding as feature

SUBMITTER: Liang B 

PROVIDER: S-EPMC12529134 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature