Colloidal systems as experimental platforms for physics-informed machine learning

Colloidal systems offer a unique experimental window for investigating condensed matter phenomena, uniquely enabling simultaneous access to microscopic particle dynamics and emergent macroscopic responses. Their particle-scale size, thermal motion, and tuneable interactions allow for real-time, real-space, and single-particle-resolved imaging. These features make it possible to directly connect local structural changes, dynamic rearrangements, and mechanical deformation with system-level behaviours. Such capabilities remain largely inaccessible in atomic or molecular systems. This review presents colloidal modelling as a predictive framework that addresses persistent challenges in materials research, including phase classification, dynamic arrest, and defect-mediated mechanics. We describe methodologies for extracting structural, dynamical, and mechanical descriptors from experimental imaging data, show how these features capture governing variables of material behaviour, and illustrate their application in machine learning approaches for phase identification, dynamics prediction, and inverse design. Rather than treating colloidal data as limited to model systems, we emphasize its value as a training ground for developing interpretable and physics-informed models. By linking microscopic mechanisms with macroscopic observables in a single experimental system, colloids generate structured and generalizable datasets. Their integration with data-driven methods offer a promising pathway toward predictive and transferable materials design strategies.