Benno Liebchen

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.

Writing is an ancient communication technique dating back at least 30 000 years. While even sophisticated contemporary writing techniques hinge on solid surfaces for engraving or the deposition of ink, writing within a liquid medium requires a fundamentally different approach. The study here demonstrates the writing of lines, letters, and complex patterns in water by assembling lines of colloidal particles. Unlike established techniques for underwater writing on solid substrates, these lines are fully reconfigurable and do not require any fixation onto the substrate. Exploiting gravity, an ion‐exchange bead (pen) is rolled across a layer of sedimented colloidal particles (ink). The pen evokes a hydrodynamic flow collecting ink‐particles into a durable, high‐contrast line along its trajectory. Deliberate substrate‐tilting sequences facilitate pen‐steering and thus drawing and writing. The experiments are complemented with a minimal model that quantitatively predicts the observed parameter dependence for writing in fluids and highlights the generic character of writing by line‐assembly. Overall, the approach opens a versatile route for writing, drawing, and patterning fluids—even at the micro‐scale.