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The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles.

Present hopes to conquer the Covid-19 epidemic are largely based on the expectation of a rapid availability of vaccines. However, once vaccine production starts, it will probably take time before there is enough vaccine for everyone, evoking the question how to distribute it best. While present vaccination guidelines largely focus on individual-based factors, i.e. on the question to whom vaccines should be provided first, e.g. to risk groups or to individuals with a strong social-mixing tendency, here we ask if a strategic spatiotemporal distribution of vaccines, e.g. to prioritize certain cities, can help to increase the overall survival rate of a population subject to an epidemic disease. To this end, we propose a strategy for the distribution of vaccines in time and space, which sequentially prioritizes regions with the most new cases of infection during a certain time frame and compare it with the standard practice of distributing vaccines demographically. Using a simple statistical model we find that, for a locally well-mixed population, the proposed strategy strongly reduces the number of deaths (by about a factor of two for basic reproduction numbers of R 0 ∼ 1.5 - 4 and by about 35% for R 0 ∼ 1 ). The proposed vaccine distribution strategy establishes the idea that prioritizing individuals not only regarding individual factors, such as their risk of spreading the disease, but also according to the region in which they live can help saving lives. The suggested vaccine distribution strategy can be tested in more detailed models in the future and might inspire discussions regarding the importance of spatiotemporal distribution rules for vaccination guidelines.

If two phases exist at the same time, such as a gas and a liquid, they have the same temperature. This fundamental law of equilibrium physics is known to apply even to many non-equilibrium systems. However, recently, there has been much attention in the finding that inertial self-propelled particles like Janus colloids in a plasma or microflyers could self-organize into a hot gas-like phase that coexists with a colder liquid-like phase. Here, we show that a kinetic temperature difference across coexisting phases can occur even in equilibrium systems when adding generic (overdamped) self-propelled particles. In particular, we consider mixtures of overdamped active and inertial passive Brownian particles and show that when they phase separate into a dense and a dilute phase, both phases have different kinetic temperatures. Surprisingly, we find that the dense phase (liquid) cannot only be colder but also hotter than the dilute phase (gas). This effect hinges on correlated motions where active particles collectively push and heat up passive ones primarily within the dense phase. Our results answer the fundamental question if a non-equilibrium gas can be colder than a coexisting liquid and create a route to equip matter with self-organized domains of different kinetic temperatures. Inertial active matter can self-organize into coexisting phases that feature different temperatures, but experimental realizations are limited. Here, the authors report the coexistence of hot liquid and cold gas states in mixtures of overdamped active and inertial passive Brownian particles, giving a broader relevance.

Active matter comprises self-driven units, such as bacteria and synthetic microswimmers, that can spontaneously form complex patterns and assemble into functional microdevices. These processes are possible thanks to the out-of-equilibrium nature of active-matter systems, fueled by a one-way free-energy flow from the environment into the system. Here, we take the next step in the evolution of active matter by realizing a two-way coupling between active particles and their environment, where active particles act back on the environment giving rise to the formation of superstructures. In experiments and simulations we observe that, under light-illumination, colloidal particles and their near-critical environment create mutually-coupled co-evolving structures. These structures unify in the form of active superstructures featuring a droplet shape and a colloidal engine inducing self-propulsion. We call them active droploids—a portmanteau of droplet and colloids. Our results provide a pathway to create active superstructures through environmental feedback. Active matter can spontaneously form complex patterns and assemblies via a one-way energy flow from the environment into the system. Here, the authors demonstrate that a two-way coupling, where active particles act back on the environment can give rise to novel superstructures, named as active droploids.

After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages—if vaccination progresses too slowly—mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

As compared to the well explored problem of how to steer a macroscopic agent, like an airplane or a moon lander, to optimally reach a target, optimal navigation strategies for microswimmers experiencing hydrodynamic interactions with walls and obstacles are far-less understood. Here, we systematically explore this problem and show that the characteristic microswimmer-flow-field crucially influences the navigation strategy required to reach a target in the fastest way. The resulting optimal trajectories can have remarkable and non-intuitive shapes, which qualitatively differ from those of dry active particles or motile macroagents. Our results provide insights into the role of hydrodynamics and fluctuations on optimal navigation at the microscale, and suggest that microorganisms might have survival advantages when strategically controlling their distance to remote walls. While control theory for optimal navigation is relevant across scales, from aeronautics to targeted drug delivery, the role of thermal fluctuations and hydrodynamic interactions with interfaces, walls and obstacles at the microscale remains an open question. Here, the authors explore optimal microswimming in the presence of walls or obstacles, and study how hydrodynamic microswimmer-wall interactions impact on optimal microswimming strategies.

One of the key achievements of equilibrium polymer physics is the prediction of scaling laws governing the viscoelastic properties of entangled polymer systems, validated in both natural polymers, such as DNA, and synthetic polymers, including polyethylene, which form materials like plastics. Recently, focus has shifted to active polymers systems composed of motile units driven far from equilibrium, such as California blackworms, self-propelled biopolymers, and soft robotic grippers. Despite their growing importance, we do not yet understand their viscoelastic properties and universal scaling laws. Here, we use Brownian dynamics simulations to investigate the viscoelastic properties of highly-entangled, flexible self-propelled polymers. Our results demonstrate that activity enhances the elasticity by orders of magnitude due to the emergence of grip forces at entanglement points, leading to its scaling with polymer length ∼  L . Furthermore, activity fluidizes the suspension, with the long-time viscosity scaling as ∼  L 2 , compared to ∼  L 3 in passive systems. These insights open new avenues for designing activity-responsive polymeric materials. Entanglement of polymers underlies a variety of phenomena across multiple scales that are driven by different active processes. Here, the authors study the viscoelastic properties of highly entangled, flexible, self-propelled polymers using Brownian dynamics simulations and show that the active motion of the polymers increase the elasticity of the solution by orders of magnitude due to the emergence of grip forces at entanglement points and increases with polymer length and activity.

A large variety of microorganisms produce molecules to communicate via complex signaling mechanisms such as quorum sensing and chemotaxis. The biological diversity is enormous, but synthetic inanimate colloidal microswimmers mimic microbiological communication (synthetic chemotaxis) and may be used to explore collective behaviour beyond the one-species limit in simpler setups. In this work we combine particle based and continuum simulations as well as linear stability analyses, and study a physical minimal model of two chemotactic species. We observed a rich phase diagram comprising a “hunting swarm phase”, where both species self-segregate and form swarms, pursuing, or hunting each other, and a “core-shell-cluster phase”, where one species forms a dense cluster, which is surrounded by a (fluctuating) corona of particles from the other species. Once formed, these clusters can dynamically eject their core such that the clusters almost turn inside out. These results exemplify a physical route to collective behaviours in microorganisms and active colloids, which are so-far known to occur only for comparatively large and complex animals like insects or crustaceans.

A key goal in evacuation management is to quickly and safely remove panicking crowds from buildings, festivals, or airplanes while preventing crush fatalities. Recently, there has been much progress in realistically modeling crowds in complex environments, based on social force models, cellular automata, and machine learning. However, current models assume specific social interactions and do not allow to systematically explore how to optimize crowd cooperation and evacuation. In contrast, the present work focuses on the question, how an ideal crowd of superintelligent agents, comprising humans, robots, or smart active particles, would cooperate to optimize evacuation. A method is developed that uses multiagent reinforcement learning combined with self‐play to learn optimal crowd behavior from scratch. Crucially, the agents in this approach are pressure‐aware and autonomously learn collision and crushing avoidance. After training, they adopt interpretable evacuation strategies like queuing and zipper merging and outperform traditional evacuation models in terms of fatality avoidance and evacuation rate. Our method can be used to enhance guidelines for mass evacuation, potentially saving lives. How would a superintelligent crowd behave in emergencies? This study develops an approach using multiagent deep reinforcement learning combined with self‐play to discover optimal evacuation strategies for pressure‐aware agents. The model learns behaviors such as queuing and zipper‐merging that significantly surpass traditional approaches in fatality avoidance and evacuation rate, potentially guiding future crowd control strategies and saving lives.

We demonstrate that repulsive power law interactions can induce deterministic directed transport of particles in dissipative ac-driven periodic potentials, in regimes where the underlying noninteracting system exhibits localized oscillations. Contrasting the well-established single particle ratchet mechanism, this interaction induced transport is based on the collective behaviour of the interacting particles yielding a spatiotemporal nonequilibrium pattern comprising persistent travelling excitations.