Publications by the same author
plus in the repository
plus in Google Scholar

Bibliografische Daten exportieren
 

Neural force functional for non-equilibrium many-body colloidal systems

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008156
URN to cite this document: urn:nbn:de:bvb:703-epub-8156-9

Title data

Zimmermann, Toni ; Sammüller, Florian ; Hermann, Sophie ; Schmidt, Matthias ; de las Heras, Daniel:
Neural force functional for non-equilibrium many-body colloidal systems.
In: Machine Learning: Science and Technology. Vol. 5 (2024) Issue 3 . - 035062.
ISSN 2632-2153
DOI der Verlagsversion: https://doi.org/10.1088/2632-2153/ad7191

[thumbnail of Zimmermann_2024_Mach._Learn.__Sci._Technol._5_035062.pdf]
Format: PDF
Name: Zimmermann_2024_Mach._Learn.__Sci._Technol._5_035062.pdf
Version: Published Version
Available under License Creative Commons BY 4.0: Attribution
Download (2MB)

Abstract

We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.

Further data

Item Type: Article in a journal
Keywords: neural force functional; non-equilibrium colloids; Brownian dynamics
DDC Subjects: 500 Science > 530 Physics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Theoretical Physics II > Chair Theoretical Physics II - Univ.-Prof. Dr. Matthias Schmidt
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Theoretical Physics II
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-8156-9
Date Deposited: 24 Jan 2025 09:18
Last Modified: 24 Jan 2025 09:19
URI: https://epub.uni-bayreuth.de/id/eprint/8156

Downloads

Downloads per month over past year