Learning Hydro-Phoretic Interactions in Active Matter

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.

• Publication year

  2026

• Venue

  Unknown venue

• Publication date

  2026-01-05

• Fields of study

  Physics

• Identifiers
  arXiv 2601.02277
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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