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We conducted a comprehensive study to investigate the performance of various machine-learning models in predicting the chemical durability of oxide glasses under different chemical conditions with glass composition as input features, by taking advantage of the large dataset (~1400 datapoints) we have collected. Two typical machine-learning tasks, weight loss regression, and surface appearance change rating classification, were conducted in the study. We successfully made Neural Networks delivered an excellent performance in predicting the weight loss, while Random Forest in classifying the surface appearance change rating. Additionally, feature importance analysis showed that SiO 2 , Na 2 O, P 2 O 5  were the most dominate features for predicting the weight loss, while SiO 2 , ZrO 2 , CaO were the topmost features for classifying the surface appearance change rating, under acid, HF, and base testing conditions, respectively. We also realized that the trained models fall short of extrapolating data far from the training dataset space even though they exhibit outstanding interpolation performance in some cases. Topology constrained theory fed by structural information from molecular dynamics simulations seems to be a promising approach to address the challenge.

Data-driven predictive models for chemical durability of oxide glass under different chemical conditions