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Active matter taps into external energy sources to power its own processes. Systems of passive particles ordinarily lack this capacity, but can become active if the constituent particles interact with each other nonreciprocally. By reformulating the theory of classical wave-matter interactions, we demonstrate that interactions mediated by scattered waves generally are not constrained by Newton's third law. The resulting center-of-mass forces propel clusters of scatterers, enabling them to extract energy from the wave and rendering them active. This form of activity is an emergent property of the scatterers' state of organization and can arise in any system where mobile objects scatter waves. Emergent activity flips the script on conventional active matter whose nonreciprocity emerges from its activity, and not the other way around. We combine theory, experiment, and simulation to illustrate how emergent activity arises in wave-matter composite systems and to explore the phenomenology of emergent activity in experimentally accessible models. These preliminary studies suggest that heterogeneity is a singular perturbation to the dynamics of wave-matter composite systems, and induces emergent activity under all but the most limited circumstances. 

Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps toward designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks. 

A quarter century of progress in holographic optical trapping has yielded fundamental advances in the science of classical wave-matter interactions. These efforts have drawn attention to the connection between wavefront topology and wave-mediated forces, including the interrelated roles of orbital and spin angular momentum, and the interplay between conservative intensity-gradient forces and non-conservative phase-gradient forces. Holographically structured force landscapes can act as knots, micromachines and even tractor beams and have permeated application areas ranging from biomedical research to quantum computing. Lessons learned from holographic optical trapping recently have been applied to acoustic micromanipulation, with remarkable effect. Beyond an overall leap in the force scales that can be achieved with sound, advances in acoustic trapping are casting new light on the nature of wave-matter interactions, including the role of nonreciprocal wave-mediated interactions in creating novel states of organization. 

Abstract RNA molecules aggregate under certain conditions. The resulting condensates are implicated in human neurological disorders, and can potentially be designed towards specified bulk properties in vitro. However, the mechanism for aggregation—including how aggregation properties change with sequence and environmental conditions—remains poorly understood. To address this challenge, we introduce an analytical framework based on multimer enumeration. Our approach reveals the driving force for aggregation to be the increased configurational entropy associated with the multiplicity of ways to form bonds in the aggregate. Our model uncovers rich phase behavior, including a sequence-dependent reentrant phase transition, and repeat parity-dependent aggregation. We validate our results by comparison to a complete computational enumeration of the landscape, and to previously published molecular dynamics simulations. Our work unifies and extends published results, both explaining the behavior of CAG-repeat RNA aggregates implicated in Huntington’s disease, and enabling the rational design of programmable RNA condensates. 

Despite significant advances in particle imaging technologies over the past two decades, few ad- vances have been made in particle tracking, i.e. linking individual particle positions across time series data. The state-of-the-art tracking algorithm is highly effective for systems in which the particles behave mostly independently. However, these algorithms become inaccurate when particle motion is highly correlated, such as in dense or strongly interacting systems. Accurate particle tracking is essential in the study of the physics of dense colloids, such as the study of dislocation formation, nucleation, and shear transformations. Here, we present a new method for particle tracking that incorporates information about the correlated motion of the particles. We demonstrate significant improvement over the state-of-the-art tracking algorithm in simulated data on highly correlated systems." 

Significance Engineering at the nanoscale is rich and complex: researchers have designed small-scale structures ranging from smiley faces to intricate sensors. However, designing specific dynamical features within these structures has proven to be significantly harder than designing the structures themselves. Biology, on the other hand, demonstrates fine-tuned kinetic control at nearly all scales: viruses that form too quickly are rarely infectious, and proper embryonic development depends on the relative rate of tissue growth. Clearly, kinetic features are designable and critical for biological function. We demonstrate a method to control kinetic features of complex systems and apply it to two classic self-assembly systems. Studying and optimizing for kinetic features, rather than static structures, opens the door to a different approach to materials design.