An improved machine learning strategy using structural features to predict the glass transition temperature of oxide glasses

Abstract

We present a physics-informed machine learning approach to predict the glass transition temperature (T_g) of sodium borosilicate glasses. Four models—random forest, extreme gradient boosting, support vector machines, and K-nearest neighbors—were trained using both compositional and structural features derived from statistical mechanics. Incorporating these structural descriptors significantly improved model performance. This is evident from reduction in mean absolute error (14.85 K → 13.76 K), root mean square error (21.78 → 19.12) and increase in R² (0.88 → 0.91) measured on testing the dataset for the random forest model. Similar performance improvement was seen for other models as well. Building on this, we propose a three-step predictive strategy that enhances generalization across compositions and accurately predict the T_g of unseen compositions, achieving a mean absolute error of approximately 8 K and an R² value of around 0.98. Our method demonstrates improved accuracy when benchmarked against GlassNet, which represents the current state-of-the-art in property prediction for glasses. These results highlight the importance of considering structural information in improving prediction capabilities of machine learning models for composition-specific small datasets. This approach can assist in the rapid screening and design of glass materials, reducing the reliance on time-consuming experiments and guiding future research toward targeted property optimization.