Multi-property prediction and high-throughput screening of polyimides: An application case for interpretable machine learning

Abstract

Polyimide (PI), as a high-performance polymer widely used in aerospace, optoelectronics, microelectronics, etc., the properties it focused on in different areas of application were diverse. However, most of the past machine learning studies in polyimide property prediction were focused only on the prediction of a single property. This study focused on four major categories of properties of polyimide, including thermal (Tg, T_d5, T_d10, and CTE), mechanical (Ts and TM), dielectric (ε), and optical (λ_cutoff, T400, n_av, and Δn), totaling eleven properties. PI data synthesized from previous studies were collected. MorGan fingerprints, improved MorGan fingerprints, RDkit and Mordred descriptors were selected as feature representations. Four ML models such as DNN, RF, XGBoost and BT were also built. Collectively, 176 machine learning models were trained for 11 predictions of properties. The performance and generalization ability of the models were confirmed by experimental validation, external validation and leave-one-out cross-validation. SHAP analysis was used to explain the optimal model for each property prediction from a physicochemical point of view and structural aspects, and three PIs with different structures were designed accordingly. Finally, a high-throughput virtual screening of nearly 7.6 million polyimides was performed based on the trained model. SA_scores was used to evaluate the ease of synthesis of each PI, and finally high-performance PIs with potential and easy to synthesize were selected for each field. This study could be expected to provide a guideline and a design framework for the application of PIs in various fields in the future.