Machine learning of a density functional for anisotropic patchy particles
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.
| Author(s): | Simon, Alessandro and Weimar, Jens and Martius, Georg and Oettel, Martin |
| Journal: | Journal of Chemical Theory and Computation |
| Year: | 2024 |
| Bibtex Type: | Article (article) |
| DOI: | https://doi.org/10.1021/acs.jctc.3c01238 |
| State: | Accepted |
| URL: | https://pubs.acs.org/doi/full/10.1021/acs.jctc.3c01238 |
| Electronic Archiving: | grant_archive |
BibTex
@article{jctc_mlpatchy,
title = {Machine learning of a density functional for anisotropic patchy particles},
journal = {Journal of Chemical Theory and Computation},
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. },
year = {2024},
slug = {jctc_mlpatchy},
author = {Simon, Alessandro and Weimar, Jens and Martius, Georg and Oettel, Martin},
url = {https://pubs.acs.org/doi/full/10.1021/acs.jctc.3c01238}
}