发表机构
Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern（工程热力学实验室，凯撒斯劳滕工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用混合机器学习模型，结合Bromley模型与矩阵补全方法，预测水电解质溶液活度。通过对478种电解质实验数据训练，得到参数矩阵，可预测9296种电解质活度，扩展Bromley模型适用性并保持高精度。
AI 中文摘要
水电解质溶液中的活度由离子活度和渗透系数描述，对工业和自然过程建模很重要。现有活度模型需针对每种电解质拟合实验数据，无法预测未研究系统。本文引入一种新的混合模型，将基于物理的Bromley模型与机器学习中的矩阵补全方法（MCM）相结合。利用电解质特定参数可排列成矩阵的特点，用MCM预测Bromley模型参数。因许多电解质缺乏实验数据，初始参数矩阵稀疏，这成为预测未研究电解质Bromley参数的矩阵补全问题。该混合模型Bromley - MCM在来自多特蒙德数据库的478种电解质水溶液在298K时的平均离子活度系数和渗透系数实验数据上进行端到端训练，得到83种阳离子和112种阴离子的Bromley参数完整矩阵，能预测9296种电解质在298K时水溶液中浓度依赖的活度，扩展了Bromley模型适用性并保持高预测精度。
英文摘要
Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature. Established activity models, such as those of Pitzer or Bromley, require fitting to experimental data for each electrolyte of interest and thus cannot predict properties for unstudied systems. While some predictive approaches exist, they are typically limited in scope and rely on additional ion-specific descriptors. In this work, we introduce a new hybrid model that combines the physics-based Bromley model with a matrix completion method (MCM) from machine learning. The MCM is employed to predict the electrolyte-specific parameters of the Bromley model, exploiting the fact that these parameters can be arranged in a matrix with cations and anions as rows and columns, respectively. Due to the lack of experimental data for many electrolytes, the initial parameter matrix is sparsely populated, making the prediction of the Bromley parameters for unstudied electrolytes a matrix completion problem. The hybrid model, Bromley-MCM, was trained end-to-end on experimental data for mean ionic activity coefficients and osmotic coefficients of aqueous solutions of 478 electrolytes at 298 K from the Dortmund Data Bank. As output, we obtain a completed matrix of Bromley parameters for 83 cations and 112 anions, enabling consistent prediction of concentration-dependent activities in aqueous solutions of 9,296 electrolytes at 298~K. This substantially extends the applicability of the Bromley model while maintaining high predictive accuracy, as demonstrated through evaluations on electrolytes excluded from model training.
