Methods for predicting Henry's law constants H_ij are important as experimental data are scarce. We introduce a new machine learning approach for such predictions: matrix completion methods (MCMs) and demonstrate its applicability using a data base that contains experimental H_ij values for 101 solutes i and 247 solvents j at 298 K. Data on H_ij are only available for 2661 systems i + j. These H_ij are stored in a 101 × 247 matrix; the task of the MCM is to predict the missing entries. First, an entirely data-driven MCM is presented. Its predictive performance, evaluated using leave-one-out analysis, is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), which, however, cannot be applied to all studied systems. Furthermore, a hybrid of MCM and PSRK-EoS is developed in a Bayesian framework, which yields an unprecedented performance for the prediction of H_ij of the studied data set.

中文翻译：

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通过矩阵完成预测亨利定律常数

由于实验数据稀缺，预测亨利定律常数H _{ij的方法很重要。}我们为此类预测引入了一种新的机器学习方法：矩阵补全方法 (MCM)，并使用包含298 K 时101 种溶质i和 247 种溶剂j的实验H _ij值的数据库证明其适用性。仅提供有关H _ij的数据对于 2661 系统i  +  j。这些H _ij存储在 101 × 247 矩阵中；MCM 的任务是预测丢失的条目。首先，提出了一个完全由数据驱动的 MCM。其使用留一法分析评估的预测性能类似于预测性 Soave-Redlich-Kwong 状态方程 (PSRK-EoS)，但是，它不能应用于所有研究的系统。此外，在贝叶斯框架中开发了 MCM 和 PSRK-EoS 的混合体，这为所研究数据集的H _ij预测产生了前所未有的性能。