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

TL;DR

This paper introduces a machine learning approach combined with power functional theory to accurately predict non-equilibrium dynamics of colloidal particles, enabling analysis of superadiabatic forces and transport properties in large systems.

Contribution

The authors develop a neural network-based functional mapping for non-equilibrium forces, allowing scalable and accurate predictions of colloidal system dynamics from steady-state data.

Findings

Neural network accurately predicts non-equilibrium force fields.

Method scales to larger systems beyond original simulations.

Predictions align well with direct simulation results.

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.