Abstract Scope |
Density is one of the most commonly measured/estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science and sustainable cements. Here, two types of machine learning models (i.e., random forest (RF) and artificial neural network (ANN)) have been developed to predict the room-temperature density of glasses in the compositional space of CaO-MgO-Al2O3-SiO2-TiO2-FeO-Fe2O3-Na2O-K2O-MnO, based on ~2100 data points mined from literature. The results show that both RF and ANN models exhibit accurate density predictions with R2 value of ~0.96-0.98 and MAPE of ~0.59-0.79% for the 15% testing set, better than empirical density models based on ionic packing ratio (R2 values and MAPE of ~0.28-0.91 and ~1.40-4.61%, respectively). Analysis of the predicted density-composition relationships from these models suggests that the ANN model exhibits a certain level of transferability and captures known features, including the mixed alkaline earth effects for (CaO-MgO)0.5-(Al2O3-SiO2)0.5 glasses. |