Multifunctional polyimide performance prediction based on explainable machine learning.

ABSTRACT: Polyimides (PIs) are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures. In flexible substrate applications, designing the molecular structure to balance thermodynamic and optical properties is the most critical part of the PI design process. To accelerate the discovery of high-performance PIs, we established predictive models for glass transition temperature (T_g), cut-off wavelength (CW), and coefficient of thermal expansion (CTE) using various machine learning algorithms. The optimal predictive models for the three properties demonstrated high accuracy and stability in both test set predictions and cross-validation results. Additionally, the interpretability of the three optimal mod

SUBMITTER: Wang S 

PROVIDER: S-EPMC12755225 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature