Learning Hydro-Phoretic Interactions in Active Matter
Soft Condensed Matter
2026-01-06 v1 Disordered Systems and Neural Networks
Fluid Dynamics
Abstract
In the quest to understand large-scale collective behavior in active matter, the complexity of hydrodynamic and phoretic interactions remains a fundamental challenge. To date, most works either focus on minimal models that do not (fully) account for these interactions, or explore relatively small systems. The present work develops a generic method that combines high-fidelity simulations with symmetry-preserving descriptors and neural networks to predict hydro-phoretic interactions directly from particle coordinates (effective interactions). This method enables, for the first time, self-contained particle-only simulations and theories with full hydro-phoretic pair interactions.
Cite
@article{arxiv.2601.02277,
title = {Learning Hydro-Phoretic Interactions in Active Matter},
author = {Palash Bera and Aritra K. Mukhopadhyay and Benno Liebchen},
journal= {arXiv preprint arXiv:2601.02277},
year = {2026}
}