Current browse context:
cond-mat.soft
Change to browse by:
References & Citations
Condensed Matter > Soft Condensed Matter
Title: Learning Neural Free-Energy Functionals with Pair-Correlation Matching
(Submitted on 22 Mar 2024 (v1), last revised 15 Apr 2024 (this version, v2))
Abstract: The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory (cDFT), is at best only known approximately for 3D systems. Here we introduce a method for learning a quasi-exact neural-network 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 3D Lennard-Jones system, we demonstrate that the learned neural free-energy functional accurately predicts planar inhomogeneous density profiles under various complex external potentials obtained from simulations.
Submission history
From: Jacobus Dijkman [view email][v1] Fri, 22 Mar 2024 07:36:58 GMT (768kb,D)
[v2] Mon, 15 Apr 2024 17:05:46 GMT (1826kb,D)
Link back to: arXiv, form interface, contact.