SKM 2023 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 17: Machine Learning in Dynamics and Statistical Physics I
DY 17.5: Vortrag
Dienstag, 28. März 2023, 11:00–11:15, ZEU 160
A machine learned classical density functional for orientational correlations in the Kern-Frenkel model for patchy particles — •Alessandro Simon1,2 and Martin Oettel1 — 1Institute for Applied Physics, University of Tübingen, Germany — 2Max Planck Institute for Intelligent Systems, Tübingen, Germany
Models of patchy particles in a generic form (hard spheres decorated with a fixed number of attraction sites), posses an interesting phase behaviour, despite their apparent simplicity. This includes gel-formation and a vanishing fluid density at the gas-liquid coexistance, as the number of attractive patches and temperature is decreased. Using simulations of a symmetric four-patch model, we examine the orientational order of the particles and the effects of their tetrahedral symmetry on the expansion of density profiles and pair correlations in rotational invariants. Building on an existing classical density functional model which is formulated on the basis of Wertheim's theory for associating liquids and does not resolve orientational correlations [Stopper et al. J. Chem. Phys. 149, 224503 (2018)], we construct an improved density functional using machine learning and show that it yields the correct orientation distribution in slit-like geometries.