Machine learning of a density functional for anisotropic patchy particles
Abstract
Anisotropic patchy particles have become an archetypical statistical model system for associating fluids. Here we formulate an approach to the Kern-Frenkel model via classical density functional theory to describe the positionally and orientationally resolved equilibrium density distributions in flat wall geometries. The density functional is split into a reference part for the orientationally averaged density and an orientational part in mean-field approximation. To bring the orientational part into a kernel form suitable for machine learning techniques, an expansion into orientational invariants and the proper incorporation of single-particle symmetries is formulated. The mean-field kernel is constructed via machine learning on the basis of hard wall simulation data. Results are compared to the well-known random-phase approximation which strongly underestimates the orientational correlations close to the wall. Successes and shortcomings of the mean-field treatment of the orientational part are highlighted and perspectives are given for attaining a full density functional via machine learning.
Cite
@article{arxiv.2311.04358,
title = {Machine learning of a density functional for anisotropic patchy particles},
author = {Alessandro Simon and Jens Weimar and Georg Martius and Martin Oettel},
journal= {arXiv preprint arXiv:2311.04358},
year = {2024}
}