Unraveling Nonequilibrium and Cooperative Dynamics in Soft Materials through Advanced XPCS, Rheology, and AI-Driven Analysis

Understanding how microscopic dynamics govern macroscopic behavior in soft materials remains a central challenge across physics, chemistry, and materials science. Recent advances in coherent X-ray scattering, high-frame-rate detectors, and data-driven analysis are now enabling a new generation of experiments that probe relaxation, yielding, and intermittency with unprecedented temporal and spatial resolution. In this work, we present an integrated framework built on three complementary advances that collectively redefine how nonequilibrium dynamics are measured, interpreted, and modeled.

First, we introduce a new theoretical approach that directly connects Langevin dynamics to X-ray photon correlation spectroscopy (XPCS) without the conventional data-averaging assumptions. This method retrieves microscopic transport coefficients from single experimental trajectories, resolving relaxation pathways and avalanche-like events that remain hidden in ensemble-averaged analyses. Second, using combined rheology, coherent X-ray scattering, and simulations, we reveal how interparticle interactions dictate yielding behavior in charged colloidal suspensions. Repulsive systems display homogeneous yielding, whereas attractive systems exhibit shear banding, delayed flow, resolidification, and boundary-driven dynamics that directly couple nanoscale particle rearrangements to macroscopic rheology. Third, we present an AI-powered XPCS analysis framework that interprets intermittent two-time correlations as spatiotemporal cooperative rearranging regions (CRRs). Deep learning–based detection and tracking uncover the hierarchical, cascading nature of these events, bridging previously disconnected views of intermittency, relaxation, and dynamical heterogeneity.

Together, these advances establish a unified platform for probing nonequilibrium and cooperative dynamics in soft matter. By combining new theory, multi-modal experiments, and AI-assisted interpretation, this work provides predictive insights for designing soft materials with targeted flow, relaxation, and processing behavior—relevant to advanced manufacturing, energy-efficient electronics, and the broader physics of disordered systems.