Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning

The preparation methods and thermal conductivity (TC) of the reported thermal conductive polymers vary significantly. A method to clarify the relationship between TC and influencing factors and to reach consistent conclusions is needed. In this study, we compiled 403 sets of data from the literature. Six typical features and three machine learning (ML) algorithms were selected and optimized. XGBoost algorithm achieved the best prediction of TC of thermal conductive polymer (correlation coefficient with 0.855). To further investigate the influence of the 6 features on the TC of thermal conductive polymer, we conducted the SHapley Additive exPlanations (SHAP) analysis. Based on the above results, pyrrhotite tailings were determined as the filler and the corresponding process parameters were also determined. However, the above model built based on literature was still unsatisfactory. We further optimized XGBoost and built XGBoost-Exp through data from the real experiment. Finally, a small percentage (23%) of real experimental data can significantly improve the prediction power of XGBoost-Exp for unseen data (correlation coefficient with 0.815). To summarize, XGBoost-Exp exhibits exceptional predictive performance for the TC of the unseen data, offering valuable insights into the influence of various features. Meanwhile, this method provides a new perspective for the utilization of hazardous sulfide minerals. [Display omitted] •The optimized XGBoost predicts the thermal conductivity of composite materialwell.•The influence of 6 features on thermal conductivity was visualized by SHAP.•Real thermal conductive polymer preparation experiments were guided by SHAP.•XGBoost-Exp based on the real experiment can save the trial-and-error cost well.