Session Y06: Memory Formation in Matter: Encoding, Reading, and Design II

Live

The densest packing of spheres, although known for millennia to be a Face-Centered Cubic (FCC) crystal with volume fraction φ~0.74, has only recently been proven mathematically by Hales. An equally ancient problem is “Random Close Packing”, RCP, the densest packing of spheres poured into a jar described in Biblical times (Luke 6:38, KJV) as, “pressed down, and shaken together, and running over”, a problem which has escaped a noncontroversial definition although many experiments and simulations agree to a value φ_RCP~0.64. Here we show that a simple model, “Biased Random Organization”, BRO, exhibits a dynamical phase transition between absorbing (non-overlapping) and active states that appears to have RCP as its critical endpoint. BRO, an absorbing state model, remains in the Manna (sand-pile) universality class. Such models are hyperuniform at critical, S(q→0)~q^α. For the Manna class, α_3D=0.25. At φ_cmax, we show that BRO and two other protocols for RCP have very similar S(q) with α~0.25. We conjecture that the highest density absorbing state for an isotropic RO or BRO model, produces an ensemble of configurations that characterizes the state conventionally known as RCP. This characterization requires neither randomness nor jamming which rather become emergent properties.   

Live

Computable Information Density (CID), the ratio of the length of a losslessly compressed data file to that of the uncompressed file, is a measure of order and correlation in both equilibrium and nonequilibrium systems. Here we show that correlation lengths can be obtained by decimation, thinning a configuration by sampling data at increasing intervals and recalculating the CID. When the sampling interval is larger than the system's correlation length, the data becomes incompressible. The correlation length and its critical exponents are thus accessible with no a-priori knowledge of an order parameter or even the nature of the ordering. The correlation length measured in this way agrees well with that computed from the decay of two-point correlation functions g₂(r) when they exist. But the CID reveals the correlation length and its scaling even when g₂(r) has no structure, as we demonstrate by "cloaking" the data with a Rudin-Shapiro sequence.   

Live

Most field theories for active matter neglect effects of memory and inertia. However, recent experiments have found inertial delay to be important for the motion of self-propelled particles. A major challenge in the theoretical description of these effects, which makes the application of standard methods very difficult, is the fact that orientable particles have both translational and orientational degrees of freedom which do not necessarily relax on the same time scale. In this work, we combine modern mathematical methods from particle physics and nonlinear dynamics to derive the general mathematical form of a field theory for soft-matter systems with two different time scales. This allows to obtain a phase field crystal model for polar (i.e., nonspherical or active) particles with translational and orientational memory. Notably, this theory is of third order in temporal derivatives and can thus be seen as a spatiotemporal jerky dynamics. An analysis of the model reveals interesting effects of memory on the dynamics of active systems.   

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Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organism’s environment, and evolution in changing environments is still poorly understood. We study a particular model of antibiotic resistance evolution in bacteria where the antibiotic concentration is an environmental parameter, and the landscapes incorporate tradeoffs between adaptation to low and high antibiotic concentration. With evolutionary dynamics that follow fitness gradients, the evolution of the system under slowly changing antibiotic concentration resembles the athermal dynamics of disordered physical systems under quasistatic external drives. Specifically, our model can be described as a Preisach system with interacting hysterons, and it exhibits effects of memory such as hysteresis loops under antibiotic concentration cycling. Indeed the model can show a rich variety of phenomena, including the existence of return point memory for certain biologically relevant parameter choices. This approach provides a novel way of discovering and studying motifs of evolutionary dynamics in biological systems in a changing environment.   

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We perform experimental and numerical studies of a granular system under cyclic-compression to investigate reversibility and memory effects. We focus on the quasi-static forcing of dense systems, which is most relevant to a wide range of geophysical, industrial, and astrophysical problems. We find that soft-sphere simulations with proper stiffness and friction quantitatively reproduce both the translational and rotational displacements of the grains. We then utilize these simulations to demonstrate that this system is capable of storing the history of previous compressions. While both mean translational and rotational displacements encode memory of compression history, the response is fundamentally different for translations compared to rotations. Finally, for translational displacements, we observe that memory of prior forcing depends on the coefficient of static inter-particle friction, but rotational memory is not altered by the level of friction.   

Live

Hard sphere liquids, when compressed, form jammed structures whose properties depend both on the density of the original liquid and on the compression scheme. Liquids prepared below an onset density jam at the same density, while those prepared above that onset jam at a density that increases with the initial density. This process, whereby the solid remembers its liquid state, is called inherent state memory. We have recently shown that during the compression, the dynamics become markedly sluggish beyond a certain point. This slowdown coincides with the contact network beginning to form, suggesting a first appearance of saddles in the effective compression (optimization) landscape. This transition appears related to the Gardner transition seen in both quasi-equilibrium hard spheres and in mean-field models, albeit out-of-equilibrium. In this talk, we will explore the properties of this dynamical Gardner-like transition--itself is a second form of memory--and its relationship to the aforementioned onset. We also consider its robustness to changes of the model. Such characterization gives hope for understanding Gardner-like phenomena in experimental systems and paves the way for their theoretical understanding.   

Live

Embedding physical memory into materials is of crucial importance to a range of technologically important materials including gels, polymers, and suspensions as well as shape memory alloys and ceramics. Despite their importance, there are very few strategies for imparting memory into such materials, which limits their use in applications. Here, I discuss new strategies for creating and destroying memories in three of these systems: gels, suspensions, and shape memory actuators. First, I will show that oscillatory shear training can embed memories of specific shear protocols in colloidal gels and that such systems can support memories both along and orthogonal to the training flow direction. Second, I will discuss memories in the context of shear thickening suspensions where disruption of the force chains responsible for thickening, i.e. erasing the flow induced memory, can be used to dethicken the suspension. Third, I will discuss a new class of reconfigurable micron-scale shape memory actuators. They function by the electrochemical oxidation/reduction of a nm thin platinum film surface, creating a strain in the oxidized layer that causes bending. They, operate at moderate voltages (~1 V), bend to the smallest radius of curvature of any electrically controlled microactuator (~ 500 nm), and are fast (< 100 ms operation). Finally, I will discuss various applications of tuning memory in these gels, suspensions, and actuators for technologies ranging from food preparation, to geological flows, and even microscoic robots.   

Live

Disordered materials with suppressed large-scale density fluctuations (as compared, e.g., to equilibrium liquids or glasses), so-called disordered hyperuniform (DH) materials, are known to exhibit unique mechanical, optical and transport properties. To date, DH structures were demonstrated only in 2D and 3D. Here, for the first time, we report a granular quasi-1D system spontaneously developing DH spatiotemporal patterns. We apply microfluidics to generate chains of droplets rearranging in a stationary external co-flow and find that the rearrangements are hyperuniform in time and space. We find that the suppression of the fluctuations is linked to the correlations between subsequent rearrangements - a manifestation of system ‘memory’ - and associated with strain propagation along the chain. We develop a model which predicts that the length of the memory is set by the relaxation time and the frequency of droplet generation, in agreement with observations. Finally, we also discuss how the generated structures could be used to encode information about the content of the droplets, opening way to new types of microfluidic assays for high-throughput screening appliactions.   

Live

We introduce a universal protocol to estimate local entropy production by data compression. The Kullback-Leibler divergence (KLD), or relative entropy, between the probability of observing a trajectory with time running forward and its time reversal quantitatively characterizes the breakdown of time-reversal symmetry and gives the entropy production of non-equilibrium steady states. Here we use a cross-parsing compression algorithm (Ziv-Merhav) to estimate KLD between two individual sequences. By compressing the forward time sequence of states using its time reversed, we obtain a close estimation of the entropy production for finite state systems. A natural spatial decomposition of entropy production can then be defined by applying the algorithms on time series of local representation of states, which gives a spatial distribution of the local entropy production. We validate this method on motility-induced phase separation of active Brownian particles systems. The local entropy production distributions show good agreement among MD simulation, lattice model and active field theory. Experimental systems such as bacterias driven by funnel structures are also analyzed.   

Live

Disordered systems of particles undergoing oscillatory shear below yield have the remarkable
tendency to fall into trajectories that revisit a configuration encountered by a previous cycle.
This is not only surprising in that a disordered system can happen to do this at all, but instead
in the incredible ease at which these systems reach these states and the rapid and directed
evolution in stroboscopic dynamics. Unfortunately, we currently lack a clear structural
measure that can be associated with this change in dynamical behavior. Here we consider 2D
simulations of jammed Hertzian bidisperse particles and subject the samples to athermal
quasistatic shear. We analyze the structural changes in stoboscopic frames using persistent
homology techniques and find that microscopic topological features develop in these systems
that encode memory of the training amplitude. We apply similar methodology to an experimental system, a cyclic-sheared 2D jammed colloidal suspension, to understand structural signals associated with yielding and memory.   

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Memory formation by cyclic shear is one of the most fascinating yet unclear mechanical properties of jammed solids. When repeatedly sheared with cycles of a certain strain amplitude, the system reaches a limit cycle after which no further changes occur under the same cyclic shear transformation. Although memory encoding has been observed both in simulations and experiments of amorphous solids, it is still unclear what triggers memory formation in real space and how this phenomenon relates to the stability of the system. We perform cyclic shear training on marginally stable and highly stable packings, the latter being produced via a constrained minimization of both positional and radial degrees of freedom. We further characterize memory encoding in both systems and find that while marginally stable packings easily encode memories for a wide range of strain amplitudes, highly stable packings need to lose their stability and become marginally stable to be able to encode a memory.   

Live

Random organization models of monodisperse spherical particles have been shown to undergo an absorbing state transition, exhibiting behavior characteristic of the Manna universality class. Here, we study the random organization model in 2D with the addition of repulsive displacements between particles. We show that the introduction of repulsive displacements results in three distinct phases: an absorbing inactive phase, a disordered active phase, as well as a polycrystalline active phase which is not observed in the absence of repulsion. Through a comparison of hyperuniformity and critical exponents, we show that the absorbing-to-disordered and absorbing-to-polycrystalline transitions belong to different universality classes, with only the former belonging to the Manna universality class. We compare the behavior of the two active phases using a computable information density approach and demonstrate how particle dynamics differ in each phase.