Accurate prediction of infinite dilution activity coefficient (γ^∞) for phase equilibria and process design is crucial. In this work, an experimental γ^∞ dataset containing 295 solutes and 407 solvents (21,048 points) is obtained through data integrating, cleaning, and filtering. The dataset is arranged as a sparse matrix with solutes and solvents as columns and rows, respectively. Neural collaborative filtering (NCF), a modern matrix completion technique based on deep learning, is proposed to fully fill in the γ^∞ matrix. Ten-fold cross-validation is performed on the collected dataset to test the effectiveness of the proposed NCF, proving that NCF outperforms the state-of-the-art physical model and previous machine learning model. The completed γ^∞ matrix makes solvent screening and extension of UNIFAC parameters possible. Taking two typical hard-to-separate systems (benzene/cyclohexane and methyl cyclopentane/n-hexane mixtures) as examples, the NCF-developed database provides high-throughput screening for separation systems in terms of solvent selectivity and capacity.

中文翻译：

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用神经协同过滤预测无限稀释活动系数

准确预测相平衡和工艺设计的无限稀释^{活度系数 (γ∞} )至关重要。在这项工作中，通过数据整合、清洗和过滤，获得了一个包含 295 个溶质和 407 个溶剂（21,048 个点）的实验性γ ^{∞数据集。}数据集排列为稀疏矩阵，溶质和溶剂分别作为列和行。提出了一种基于深度学习的现代矩阵补全技术神经协同过滤（NCF）来完全^填充γ∞矩阵。对收集到的数据集进行十倍交叉验证，以测试所提出的 NCF 的有效性，证明 NCF 优于最先进的物理模型和以前的机器学习模型。完整的γ ^∞矩阵使 UNIFAC 参数的溶剂筛选和扩展成为可能。NCF 开发的数据库以两种典型的难以分离的系统（苯/环己烷和甲基环戊烷/正己烷混合物）为例，为分离系统提供了溶剂选择性和容量方面的高通量筛选。