Machine Learning-integrated Multiscale Simulation Framework: Bridging Scales in Associative Polymer-Colloid Suspensions

TL;DR

This paper introduces a multiscale simulation framework that integrates molecular dynamics, active learning, and population balance models to predict the rheological behavior of associative polymer-colloid suspensions, enabling efficient and accurate large-scale simulations.

Contribution

The novel framework combines explicit-chain Brownian dynamics, active learning surrogates, and population balance models to connect molecular-scale kinetics with macroscopic rheology in colloidal suspensions.

Findings

Accurately reproduces stress relaxation moduli over time and frequency.

Identifies critical chain-to-particle ratios affecting network connectivity.

Shows higher particle volume fractions lead to more persistent bonds and slower relaxation.

Abstract

Predicting the rheological behavior of associative polymers bridging colloidal particles into transient networks is fundamentally challenging because the coupled spatiotemporal scales prevent efficient molecular-fidelity modeling. We address this through a novel, unified multiscale simulation framework for telechelic polymer-colloid suspensions integrating: explicit-chain Brownian dynamics resolving polymer-particle association kinetics; active learning metamodels compressing kinetics into efficient surrogates; and Population Balance-Brownian Dynamics (Pop-BD) computing network-scale dynamics from metamodel predictions. Validated against explicit-chain Brownian dynamics, our framework accurately reproduces time-and frequency-dependent stress relaxation moduli, enabling simulations of larger systems over longer timescales. Systematic investigations reveal that network connectivity exhibits critical transitions at specific chain-to-particle ratios, with bond density and lifetime correlating to enhanced relaxation times and moduli. Higher particle volume fractions yield more persistent bonds and slower relaxation. This framework connects chain-level dynamics to macroscopic rheology, enabling computationally efficient rational design of associative colloidal materials for waterborne coatings and soft-matter applications.