As presented in Figure , the dataset extracted from the NEMD simulations includes three features: the shear rates (  ), the temperatures ( T ) and the nanoparticles content (  ). We used this dataset to train different ML models to predict the value of  under different feature settings. In the current study, we applied four ML models, including linear regression (LR) , support vector machine (SVM) , decision tree (DT) and extreme gradient boosting (XGBoost) . These four models can be classified as linear (LR) and non-linear models (SVM, DT and XGBoost) and have been actively used in the study of property predictions in polymer materials. LR fits a linear model that aims to minimize the residual sum of squares between actual and predicted values. DT and XGBoost are both tree-based models. DT uses binary tree graph to split and assign samples. XGBoost is a boosting algorithm with regularization to prevent over-fitting. SVM can perform non-linear regression through kernel functions that transform data samples to a high-dimensional space.