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Machine Learning of a Density Functional for Anisotropic Patchy Particles.

Alessandro Simon^1,2, Jens Weimar¹, Georg Martius²

¹Institute for Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany.

Journal of Chemical Theory and Computation

|January 17, 2024

Summary

This summary is machine-generated.

This study models associating fluids using anisotropic patchy particles and classical density functional theory. Machine learning constructs an orientational kernel, improving upon approximations for density distributions near walls.

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Area of Science:

Statistical mechanics
Soft matter physics
Computational chemistry

Background:

Anisotropic patchy particles are key models for associating fluids.
Classical density functional theory (DFT) is used to study fluid behavior.
Understanding density distributions near walls is crucial for material properties.

Purpose of the Study:

To develop a machine learning (ML)-based approach for the Kern-Frenkel model.
To describe equilibrium density distributions of anisotropic particles near flat walls.
To improve upon existing approximations for orientational correlations.

Main Methods:

Formulation of a DFT approach for anisotropic patchy particles.
Splitting the density functional into reference and orientational parts.
Developing a mean-field kernel for the orientational part using ML and simulation data.

Main Results:

The ML-based kernel accurately describes positionally and orientationally resolved densities.
The approach captures orientational correlations near walls better than the random-phase approximation.
Successes and limitations of the mean-field treatment are identified.

Conclusions:

The ML-based DFT approach provides a powerful tool for studying associating fluids.
This method offers improved accuracy for orientational correlations compared to traditional approximations.
Future work aims to develop a full ML-based density functional.