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Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials. 

Computational modeling of assembly is challenging for many systems, because their timescales can vastly exceed those accessible to simulations. This article describes the multiMSM, which is a general framework that uses Markov state models (MSMs) to enable simulating self-assembly and self-organization of finite-sized structures on timescales that are orders of magnitude longer than those accessible to brute-force dynamics simulations. As with traditional MSM approaches, the method efficiently overcomes free energy barriers and other dynamical bottlenecks. In contrast to previous MSM approaches to simulating assembly, the framework describes simultaneous assembly of many clusters and the consequent depletion of free subunits or other small oligomers. The algorithm accounts for changes in transition rates as concentrations of monomers and intermediates evolve over the course of the reaction. Using two model systems, we show that the multiMSM accurately predicts the concentrations of the full ensemble of intermediates on timescales required to reach equilibrium. Importantly, after constructing a multiMSM for one system concentration, yields at other concentrations can be approximately calculated without any further sampling. This capability allows for orders of magnitude additional speedup. In addition, the method enables highly efficient calculation of quantities such as free energy profiles, nucleation timescales, flux along the ensemble of assembly pathways, and entropy production rates. Identifying contributions of individual transitions to entropy production rates reveals sources of kinetic traps. The method is broadly applicable to systems with equilibrium or nonequilibrium dynamics and is trivially parallelizable and, thus, highly scalable. Published by the American Physical Society2024 

The spontaneous formation of contractile asters is ubiquitous in reconstituted active materials composed of biopolymers and molecular motors. Asters are radially oriented biopolymers or biopolymer bundles with a dense motor-rich core. The microscopic origins of their material properties and their stability are unknown. Recent efforts highlighted how motor-filament and filament-filament interactions control the formation of asters composed of microtubules and kinesin motors. However, the impact of motor-motor interactions is less understood, despite growing evidence that molecular motors often spontaneously aggregate, both and . In this article, we combine experiments and simulations to reveal the origin of the arrested coarsening, aging, and stability of contractile asters composed of microtubules, clusters of adenosine triphosphate (ATP)-powered kinesin-1 motors, and a depletant. Asters coalesce into larger asters upon collision. We show that the spontaneous aggregation of motor clusters drives the solidification of aster cores, arresting their coalescence. We detect aggregation of motor clusters at the single microtubule level, where the uncaging of additional ATP drives the delayed but sudden detachment of large motor aggregates from isolated microtubules. Computer simulations of cytoskeletal assemblies demonstrate that decreasing the motors' unbinding rate slows down the aster's coalescence. Changing the motors' binding rate did not impact the aster's coalescence dynamics. Finally, we show that the aggregation of motor clusters and aster aging result from the combined effects of depletion forces and nonspecific binding of the clusters to themselves. We propose alternative formulations that mitigate these effects, and prevent aster aging. The resulting self-organized structures have a finite lifetime, which reveals that motor aggregation is crucial for maintaining aster's stability. Overall, these experiments and simulations enhance our understanding of how to rationally design long-lived and stable contractile materials from cytoskeletal proteins. Published by the American Physical Society2025 

We apply optimal control theory to drive a polar active fluid into new behaviors: relocating asters, reorienting waves, and on-demand switching between states. This study reveals general principles to program active matter for useful functions. 

This review accompanies the Special Issue on the subject of physical virology, which features work presented at the recent Gordon Research Conference (GRC) on this topic [...] 

Deep learning-based optical flow (DLOF) extracts features in video frames with deep convolutional neural networks to estimate the inter-frame motions of objects. DLOF computes velocity fields more accurately than PIV for densely labeled systems. 

Programmable self-assembly has seen an explosion in the diversity of synthetic crystalline materials, but developing strategies that target “self-limiting” assemblies has remained a challenge. Among these, self-closing structures, in which the local curvature defines the finite global size, are prone to polymorphism due to thermal bending fluctuations, a problem that worsens with increasing target size. Here, we show that assembly complexity can be used to eliminate this source of polymorphism in the assembly of tubules. Using many distinct components, we prune the local density of off-target geometries, increasing the selectivity of the tubule width and helicity to nearly 100%. We further show that by reducing the design constraints to target either the pitch or the width alone, fewer components are needed to reach complete selectivity. Combining experiments with theory, we reveal an economical limit, which determines the minimum number of components required to create arbitrary assembly sizes with full selectivity. 

We propose and investigate an extension of the Caspar–Klug symmetry principles for viral capsid assembly to the programmable assembly of size-controlled triply periodic polyhedra, discrete variants of the Primitive, Diamond, and Gyroid cubic minimal surfaces. Inspired by a recent class of programmable DNA origami colloids, we demonstrate that the economy of design in these crystalline assemblies—in terms of the growth of the number of distinct particle species required with the increased size-scale (e.g., periodicity)—is comparable to viral shells. We further test the role of geometric specificity in these assemblies via dynamical assembly simulations, which show that conditions for simultaneously efficient and high-fidelity assembly require an intermediate degree of flexibility of local angles and lengths in programmed assembly. Off-target misassembly occurs via incorporation of a variant of disclination defects, generalized to the case of hyperbolic crystals. The possibility of these topological defects is a direct consequence of the very same symmetry principles that underlie the economical design, exposing a basic tradeoff between design economy and fidelity of programmable, size controlled assembly. 

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and concentrations of subunits. By contrast, we introduce an assembly paradigm that allows large error-tolerance in the subunit affinity and helps avoid kinetic traps. Our combined experimental and computational approach uses a model system of triangular subunits programmed to assemble intoT= 3 icosahedral capsids comprising 60 units. The experimental platform uses DNA origami to create monodisperse colloids whose three-dimensional geometry is controlled to nanometer precision, with two distinct bonds whose affinities are controlled tok_BTprecision, quantified in situ by static light scattering. The computational model uses a coarse-grained representation of subunits, short-ranged potentials, and Langevin dynamics. Experimental observations and modeling reveal that when the bond affinities are unequal, two distincthierarchicalassembly pathways occur, in which the subunits first form dimers in one case and pentamers in another. These hierarchical pathways produce complete capsids faster and are more robust against affinity variation than egalitarian pathways, in which all binding sites have equal strengths. This finding suggests that hierarchical assembly may be a general engineering principle for optimizing self-assembly of complex target structures.