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-vapour 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 there can be no wetting transition in this symmetrical mixture.

8 pages, 4 figures, previous title was "Learning the bulk and interfacial physics of liquid-liquid phase separation"

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• Gauge invariance and hyperforce correlation theory for equilibrium fluid mixtures
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