Learning the bulk and interfacial physics of liquid–liquid phase separation with neural density functionals

Abstract

We use simulation-based supervised machine learning and classical density functional theory to investigate bulk and interfacial phenomena associated with phase coexistence in binary mixtures. For a prototypical symmetrical Lennard-Jones mixture, our trained neural density functional yields accurate liquid–liquid and liquid–vapor binodals together with predictions for the variation of the associated interfacial tensions across the entire fluid phase diagram. From the latter, we determine the contact angles at fluid–fluid interfaces along the line of triple-phase coexistence and confirm that there can be no wetting transition in this symmetrical mixture.