Learning Universal Fundamental Relations for Predicting the Phase Behavior of Liquid Mixtures

Predicting properties of liquid mixtures is central to chemical engineering, yet accurate predictions from unimolecular properties have proven elusive. Activity models are a popular class of mixture models due to their parsimony yet strong correlative ability. The latest evolution in activity models is data-driven machine learning models using neural networks (NN). However, NN models require a substantial amount of training data to make their predictions, and many lack an accessible free energy function, hindering extrapolation. This work presents a joint data- and theory-driven model learning a set of universal fundamental relations (UFR) governing small molecule liquid mixtures. UFR models accurately correlate experimental infinite dilution activity coefficient data (IDAC). Trained only on IDAC data, UFR models can predict T–x–y and P–x–y vapor–liquid equilibrium curves of mixtures outside the training set. Several UFR models were also able to simultaneously predict binary liquid–liquid phase separations.