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BP: Fachverband Biologische Physik

BP 38: Active Matter VI (joint session DY/BP)

BP 38.2: Vortrag

Freitag, 13. März 2026, 10:00–10:15, ZEU/0160

Learning effective hydro-phoretic interactions in active matter — •Palash Bera, Aritra K. Mukhopadhyay, and Benno Liebchen — Technische Universität Darmstadt, Darmstadt, Germany.

In the quest to understand collective behaviors in active matter systems, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. Despite the substantial progress in identifying effective models, existing approaches often rely on minimalistic approximations, neglecting many-body interactions and the near-field contributions to the full interaction dynamics. We propose a machine learning-based framework to systematically learn hydro-phoretic interactions among active colloids from first principles. By combining high-fidelity simulations with symmetry-preserving descriptors and neural network architectures, our approach captures the effective representations of both near- and far-field interactions. This framework bridges the gap between microscopic continuum models and coarse-grained active matter simulations, enabling scalable many-particle modeling without explicitly resolving the fluid flow or concentration fields. Built on two-body interactions, the coarse-grained model captures clustering phenomena consistent with those observed experimentally in active matter systems. We envision that the principles and tools developed here will have broad applicability across a wide range of active and nonequilibrium systems, including driven colloids, active gels, and field-responsive materials, providing a robust framework for modeling emergent behaviors in living and life-like systems.

Keywords: Active matter; Hydro-phoretic interactions; Machine learning; Coarse-graining; Collective behaviors