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arXiv · 2403.15007

Learning Neural Free-Energy Functionals with Pair-Correlation Matching

Jacobus Dijkman·Marjolein Dijkstra·René van Roij·Max Welling·Jan-Willem van de Meent·Bernd Ensing
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Abstract

The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a method for learning a neuralnetwork approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. For a supercritical Lennard-Jones system with planar symmetry, we demonstrate that the learned neural free-energy functional accurately predicts inhomogeneous density profiles under various complex external potentials obtained from simulations.

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Jacobus Dijkman, Marjolein Dijkstra, René van Roij, Max Welling, Jan-Willem van de Meent, Bernd Ensing. 2024-03-22. Learning Neural Free-Energy Functionals with Pair-Correlation Matching. https://doi.org/10.1103/physrevlett.134.056103

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