Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of traditional ML, we introduce a “topology-informed ML” paradigm—wherein some features of the network topology (rather than traditional descriptors) are used as fingerprint for ML models—and apply this method to predict the forward (stage I) dissolution rate of a series of silicate glasses. We demonstrate that relying on a topological description of the atomic network (i) increases the accuracy of the predictions, (ii) enhances the simplicity and interpretability of the predictive models, (iii) reduces the need for large training sets, and (iv) improves the ability of the models to extrapolate predictions far from their training sets. As such, topology-informed ML can overcome the limitations facing traditional ML (e.g., accuracy vs. simplicity tradeoff) and offers a promising route to predict the properties of materials in a robust fashion.

机器学习（ML）回归方法是通过从现有数据库中学习来开发预测材料性能的模型的有前途的工具。但是，尽管ML模型通常擅长插值数据，但它们通常不提供可靠的推断，并且可能违反物理定律。在这里，为了解决传统ML的局限性，我们引入了一种“拓扑通知的ML”范式-其中网络拓扑的某些功能（而不是传统的描述符）用作ML模型的指纹-并将此方法应用于预测（阶段I）一系列硅酸盐玻璃的溶解速率。我们证明，依靠原子网络的拓扑描述（i）可以提高预测的准确性，（ii）可以提高预测模型的简单性和可解释性，（iii）减少了对大型训练集的需求，并且（iv）提高了模型从其训练集外推算预测的能力。这样，拓扑通知的ML可以克服传统ML所面临的局限性（例如，准确性与简单性之间的权衡），并提供了一种有希望的途径来以健壮的方式预测材料的性能。