Prediction of Henry's law constants by matrix completion

Abstract

Abstract 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.