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

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.