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Learning general pair interactions between self-propelled particles
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AI
DOI：10.1039/D5SM00655D.png)
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Abstract
En 中文
Synthetic active matter systems; such as active colloids; often have complex interactions; which can be of hydrodynamic; chemical or electrostatic origin and cannot be computed from first principles. Here; we use Stochastic Force Inference to learn general pair interactions; including transverse forces and torques; between self-propelled Janus particles from experimental trajectories. We use data from two experiments: one where the particles flock; and one where the system remains disordered. The learned interactions are then fed to numerical simulations; which reproduce all the experimental observables and could be extrapolated to different densities. Overall; we find that the radial interaction is mostly repulsive and isotropic; while the angular interaction has a richer angular dependence; which controls the behavior of the system; the transverse interaction is negligible. Finally; testing the symmetry relations obeyed by the inferred interactions allows us to show that they cannot come from electrostatics only; so that they must have a hydrodynamic component.
Keywords:
synthetic active matter
Stochastic Force Inference
Janus particles
hydrodynamic interactions
pair interactions
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