Prediction of pair interactions in mixtures by matrix completion.

Molecular simulations enable the prediction of physicochemical properties of mixtures based on pair-interaction models of the pure components and combining rules to describe the unlike interactions. However, if no adjustment to experimental data is made, the existing combining rules often do not yield sufficiently accurate predictions of mixture data. To address this problem, adjustable binary parameters describing the pair interactions in mixtures ( + ) are used. In this work, we present the first method for predicting for unstudied mixtures based on a matrix completion method (MCM) from machine learning (ML). Considering molecular simulations of Henry's law constants as an example, we demonstrate that for unstudied mixtures can be predicted with high accuracy. Using the predicted significantly increases the accuracy of the Henry's law constant predictions compared to using the default = 1. Our approach is generic and can be transferred to molecular simulations of other mixture properties and even to combining rules in equations of state, granting predictive access to the description of unlike intermolecular interactions.