Machine Learning‐Based Prediction of Polypropylene Molecular Weight Distribution for Film Capacitor Applications

Abstract

This study proposes a machine learning‐based model for predicting the molecular weight distribution (MWD) of polypropylene, aiming to optimize the performance of dielectric materials for film capacitors. A database was established by collecting 233 sets of experimental data from published literature, with 10 influencing factors as input variables and molecular weight distribution as the output variable. Six machine learning algorithms—Gradient Boosting Regression (GBR), AdaBoost Regression (ABR), Bagging Regression (BAGR), Support Vector Regression (SVR), Random Forest Regression (RFR), and K Neighbors Regressor (KNR)—were employed to predict and analyze polypropylene molecular weight distribution. Results demonstrate that the constructed machine learning models effectively learn from complex data and accurately predict molecular weight distribution. Among these, the Gradient Boosting Regression model exhibited the highest predictive performance, achieving a coefficient of determination ( R 2 ) of 0.9380 and a mean absolute error (MAE) of only 0.1545. This research provides an efficient tool for optimizing the molecular weight distribution of polypropylene and lays a theoretical foundation for enhancing the performance of film capacitors.