# Mixing of a binary passive particle system using smart active particles

## TL;DR

Smart active particles, guided by machine learning, can mix passive particles more efficiently than traditional methods.

## Contribution

A novel approach using smart active particles with adaptive behavior to achieve optimal mixing of passive particles.

## Key findings

- Smart active particles outperform conventional run-and-tumble particles in mixing efficiency.
- Optimal mixing occurs when active particles are confined to an eccentric zone, inducing global rotation.
- The passive particles' motion transitions directionally toward the system center.

## Abstract

The controlled activity of active entities interacting with a passive environment can generate emergent system-level phenomena, positioning such systems as promising platforms for potential downstream applications in targeted drug delivery, adaptive and reconfigurable materials, microfluidic transport, and related fields. The present work aims to realise an optimal mixing of two segregated species of passive particles by introducing a small fraction of active particles (\documentclass[12pt]{minimal}
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\begin{document}$$2\%$$\end{document} by composition) with adaptive and intelligent behaviour, directed by a trained Artificial Neural Network-based agent. While conventional run-and-tumble particles can induce mixing in the system, the smart active particles demonstrate enhanced performance, achieving faster and more efficient mixing. Interestingly, an optimal mixing strategy doesn’t involve a uniform dispersion of active particles in the domain, but rather limiting their motion to an eccentrically placed zone of activity, inducing a global rotational motion of the passive particles about the system centre. A transition in the directionality of the passive particles’ motion is observed along the radius towards the centre, likening the active particles’ motion to an ellipse-shaped void with a defined surface speed. Situated at the intersection of active matter and machine learning, this work highlights the potential of integrating adaptive learning frameworks into traditional models of active matter.