Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides.

ABSTRACT: The rational design of polyimides (PIs) with targeted glass transition temperature (T_g) is crucial for advanced microelectronics applications. While data-driven approaches offer promise, there is a pressing need for models that are not only predictive but also physically interpretable, especially with limited datasets. Herein, we present a highly interpretable Quantitative Structure-Property Relationship (QSPR) model for accurate T_g prediction of PIs. Employing a Genetic Algorithm combined with Multiple Linear Regression (GA-MLR), we identified an optimal set of seven molecular descriptors from a curated dataset. The model demonstrates robust predictive performance and strong generalization ability, validated through rigorous statistical tests. Crucially

SUBMITTER: Cui T 

PROVIDER: S-EPMC12735220 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature