Active Matter

Active matter research focuses on understanding systems of self-propelled particles, from microscopic colloids to macroscopic robots, that convert energy into directed motion, exhibiting behavior beyond equilibrium statistical mechanics. Current research employs advanced computational techniques, including deep learning for analyzing complex flow fields and estimating entropy production, and reinforcement learning for optimizing particle navigation. These studies are improving our ability to model and predict the collective behavior of active matter, with implications for diverse fields such as materials science, robotics, and biological systems. The development of new experimental platforms, like modular robotic swarms, further enhances the ability to test and validate theoretical models.