Prediction of glass transition temperature of oxide glasses based on interpretable machine learning and sparse data sets

Abstract

The glass transition temperature (Tg) is a crucial characteristic of oxide glasses, exerting significant influence on their properties and applications. In this study, we utilized a sparse dataset and machine learning techniques to establish a predictive model for the relationship between the composition of oxide glasses and Tg. Among four machine learning algorithms compared, the Extreme Tree Regressor (ETR) algorithm demonstrated superior performance, exhibiting robust generalization capabilities on the validation set. We performed feature selection on the original features using Pearson Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC), resulting in a subset of 38 dimensions. Subsequently, we employed Sparse Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) for dimensionality reduction, yielding a final subset of 12 dimensions. Introducing SHapley Additive exPlanations (SHAP) values for interpretability analysis of the predictive model, we obtained important feature rankings and analyzed how variations in features affect the target variable. Finally, using the CaO-SiO2-Al2O3 system as an example, we demonstrated how the model predicts Tg based on composition, facilitating rational design of oxide glass compositions.