论文标题
数据驱动的基于硅酸盐玻璃的室温密度的预测
Data-driven prediction of room temperature density for multicomponent silicate-based glasses
论文作者
论文摘要
密度是最常见或估计的材料特性之一，尤其是对于许多田地引起的玻璃和融化，包括冶金，地质，材料科学和可持续水泥。 Here, two types of machine learning (ML) 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 (CMASTFNKM), based on ~2100 data points mined from ~140 literature studies.结果表明，RF和ANN模型具有R2值，RMSE和MAPE的准确预测〜0.96-0.98，〜0.02-0.03 g/cm3和〜0.59-0.79％，分别为15％的测试集，与基于仿效密度的封装相比，这是更准确的（与iOnirial ionic ionic of Meping cortio and 2）。 〜0.28-0.91，〜0.05-0.15 g/cm3和〜1.40-4.61％）。此外，玻璃密度被证明是一系列CAO-AL2O3-SIO2（CAS）和火山玻璃的可靠反应性指标，这是由于其与平均金属氧离子分离能（R2值高于0.90）的强相关性（R2值高于0.90）。分析来自这些模型的预测密度组合关系（对于选定的组成子空间）表明，ANN模型表现出一定水平的可传递性（即，在数据库中不涵盖的不涵盖的组成空间（或更少）的能力），并捕获已知的特征，包括（CAO-MGO）（CAO-MGO）0.5-（CAO-MGO）0.5-（AL2O3-a2o3-sio2）0.5玻璃。
Density is one of the most commonly measured or 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 (ML) 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 (CMASTFNKM), based on ~2100 data points mined from ~140 literature studies. The results show that the RF and ANN models give accurate predictions of glass density with R2 values, RMSE, and MAPE of ~0.96-0.98, ~0.02-0.03 g/cm3 and ~0.59-0.79%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ~0.28-0.91, ~0.05-0.15 g/cm3, and ~1.40-4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO-Al2O3-SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ~0.90) with the average metal-oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density-composition relationships from these models (for selected compositional subspaces) suggests that the ANN model exhibits a certain level of transferability (i.e., ability to extrapolate to compositional space not (or less) covered in the database) and captures known features including the mixed alkaline earth effects for (CaO-MgO)0.5-(Al2O3-SiO2)0.5 glasses.
