Learning Density Functionals to Bridge Particle and Continuum Scales

TL;DR

This paper introduces a physics-informed machine learning framework that enhances classical density functional theory with neural network corrections, improving predictions of interfacial thermodynamics across scales.

Contribution

It develops a novel method to embed neural corrections into free-energy functionals, maintaining thermodynamic consistency while capturing complex correlations.

Findings

Accurately predicts density profiles, coexistence curves, and surface tensions.

Successfully generalizes to predict contact angles and droplet shapes beyond training data.

Bridges molecular simulations and continuum models with interpretable machine learning.

Abstract

Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models.