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 SiO₂, Na₂O, P₂O₅ were the most dominate features for predicting the weight loss, while SiO₂, ZrO₂, 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.

我们利用收集到的大量数据集（〜1400个数据点），进行了一项综合研究，以研究各种机器学习模型在以玻璃成分为输入特征的不同化学条件下预测氧化物玻璃的化学耐久性时的性能。在这项研究中，进行了两个典型的机器学习任务：失重回归和表面外观变化等级分类。我们成功地使神经网络在预测重量损失方面表现出出色的性能，而Random Forest在对表面外观变化等级进行分类的过程中表现出色。另外，特征重要性分析表明，SiO ₂，Na ₂ O，P ₂ O ₅SiO ₂，ZrO ₂，CaO是预测重量损失的最主要特征，分别在酸性，HF和碱性测试条件下对表面外观变化等级进行分类。我们还认识到，即使在某些情况下，训练后的模型表现出出色的插值性能，也无法从训练数据集空间外推算数据。由分子动力学模拟的结构信息提供的拓扑约束理论似乎是解决挑战的有前途的方法。