Harnessing machine learning for high-throughput screening of high thermal conductivity polyimides: A multiscale feature engineering approach

Abstract

Polyimides are known for their exceptional thermal stability and are widely utilized in high-temperature and electronic applications. To further explore and broaden their application in electronic packaging and thermal management, developing polyimide with high thermal conductivity remains a significant challenge. In this study, we present a machine learning technique with novel multiscale feature engineering approach to predict and identify high thermal conductivity polyimide efficiently. Our workflow involves digitally representing chemical structures using physical insights and refining these representations through the Statistical – Tree – Recursive feature engineering pipeline, which includes three steps--statistical selection, tree-based cascading feature selection, and recursive feature elimination. This process results in the creation of a comprehensive combinational feature set. Multiple machine learning models were trained and validated, demonstrating high predictive accuracy and generalizability. High-throughput screening identified polyimide candidates with thermal conductivity values exceeding 0.4 W/(m⋅K), and these predictions were validated using non-equilibrium molecular dynamics simulation. This workflow provides valuable insights into structure-property relationships, offering a robust framework for designing polymer materials with improved thermal properties for applications in electronics packaging, flexible sensors, and other high-performance devices.

Keywords

• Thermal conductivity
• Polyimides
• Machine learning
• Feature engineering
• Structure-properties relationship