The surface tension, as a crucial property of molten slags, affects a broad range of high-temperature industrial processes. In this study, we developed a structure–thermodynamics-informed artificial neural network (STIANN) to predict the surface tension of molten slags over a broad range of composition and temperature. First, we constructed a brand-new database that included not only conventional laboratory-based variable information but also quantitative structural and thermodynamic features at different scales, including second-nearest-neighbor bonds, oxygen species, degree of depolymerization (NBO/T), oxide activities, and Gibbs free energies. Then, the four-layer feed-forward backpropagation artificial neural networks were carefully designed to build the surface tension models. Next, three models were built using the different configurations of training features. The analysis results of structural information indicate the high concentration of bridging oxygen generally contributes to the low surface tension when non-bridging oxygen and free oxygen do the opposite. Statistically, the surface tension is positively correlated with the NBO/T of system. The thermodynamic features of \({\Delta }_{\text{mix}}{G}_{\text{m}}^{\text{re}}\) and \({\Delta }_{\text{mix}}{G}_{\text{m}}^{\text{E}}\) vary in the range of 0 to − 70 and 0 to − 55 kJ/mol, respectively, and both decrease first and then increase with the increase in NBO/T. The STIANN model integrated with both structural and thermodynamic information exhibits an unprecedented and excellent predictive performance. The analysis of feature importance confirms the prominent contribution of structural and thermodynamic features to the STIANN model.

表面张力作为熔渣的重要特性，影响着广泛的高温工业过程。在这项研究中，我们开发了一种结构-热力学信息人工神经网络 (STIANN) 来预测熔渣在广泛的成分和温度范围内的表面张力。首先，我们构建了一个全新的数据库，其中不仅包括传统的基于实验室的变量信息，还包括不同尺度的定量结构和热力学特征，包括第二最近邻键、氧种类、解聚度 (NBO/T)、氧化物活动和吉布斯自由能。然后，精心设计了四层前馈反向传播人工神经网络来构建表面张力模型。下一个，使用不同的训练特征配置构建了三个模型。结构信息分析结果表明，高浓度的桥接氧通常有助于降低表面张力，而非桥接氧和游离氧则相反。统计上，表面张力与体系的NBO/T呈正相关。热力学特性\({\Delta }_{\text{mix}}{G}_{\text{m}}^{\text{re}}\)和\({\Delta }_{\text{mix}} {G}_{\text{m}}^{\text{E}}\)分别在 0 到 - 70 和 0 到 - 55 kJ/mol 的范围内变化，并且随着NBO/T 增加。与结构和热力学信息相结合的 STIANN 模型表现出前所未有的出色预测性能。特征重要性的分析证实了结构和热力学特征对 STIANN 模型的突出贡献。