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

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.