Absorbent material composition prediction based on multi-objective regression with value stacking and selection - He, Shi; Chen, Jiaying; Huang, Kai; Mao, Jian; Li, Kexun; Liu, Taikang (Frontiers in Materials, 2025)
IntroductionElectromagnetic wave absorption materials reduce incoming wave energy, with machine learning focusing on data-driven design methods. Traditional multi-objective regression methods often fail to provide accurate component predictions, limiting their performance.MethodWe propose a multi-objective predictive model for absorbent compositions. Using single-variable predictions as cumulative features in a regression chain improves feature representation. Performance metrics identify the optimal predictor variables for material composition, aiding in the classification of carbon nanotubes based on required performance and predicted values.Result and discussionExperimental results indicate that the model achieves better R2 and mean squared error for carbon nanotubes, carbon black, and carbon fiber than other methods, with optimal Accuracy and Matthews Correlation Coefficient in classifying carbon nanotubes, validating the method for material composition design.