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In jammed packings, it is usually thought that local structure only plays a significant role in specific regimes. The standard deviation of the relative excess coordination, $\sigma_Z/ Z_\mathrm{c}$, decays like $1/\sqrt{d}$, so that local structure should play no role in high spatial dimensions. Furthermore, in any fixed dimension $d \geq 2$, there are diverging length scales as the pressure vanishes approaching the unjamming transition, again suggesting that local structure should not be sufficient to describe response. Here we challenge the assumption that local structure does not matter in these cases. In simulations of jammed packings under athermal, quasistatic shear, we use machine learning to identify a local structural variable, softness, that correlates with rearrangements in dimensions $d=2$ to $d=5$. We find that softness - and even just the coordination number $Z$ - are quite predictive of rearrangements over a wide range of pressures, all the way down to unjamming, in all $d$ studied. This result provides direct evidence that local structure can play a role in higher spatial dimensions., Comment: 8 pages, 8 figures

Physical networks, such as biological neural networks, can learn desired functions without a central processor, using local learning rules in space and time to learn in a fully distributed manner. Learning approaches such as equilibrium propagation, directed aging, and coupled learning similarly exploit local rules to accomplish learning in physical networks such as mechanical, flow, or electrical networks. In contrast to certain natural neural networks, however, such approaches have so far been restricted to the quasistatic limit, where they learn on time scales slow compared to their physical relaxation. This quasistatic constraint slows down learning, limiting the use of these methods as machine learning algorithms, and potentially restricting physical networks that could be used as learning platforms. Here we explore learning in an electrical resistor network that implements coupled learning, both in the lab and on the computer, at rates that range from slow to far above the quasistatic limit. We find that up to a critical threshold in the ratio of the learning rate to the physical rate of relaxation, learning speeds up without much change of behavior or error. Beyond the critical threshold, the error exhibits oscillatory dynamics but the networks still learn successfully., 26 pages, 5 figures

The genetic manipulability and short lifespan of C. elegans make it an important model for aging research. Widely applied methods for measurements of worm aging based on manual observation are labor intensive and low-throughput. Here, we describe the Worm Collective Activity Monitoring Platform (WormCamp), a system for assaying aging in C. elegans by monitoring activity of populations of worms in standard 24-well plates. We show that metrics based on the rate of decline in collective activity can be used to estimate the average lifespan and locomotor healthspan in the population. Using the WormCamp, we assay a panel of highly divergent natural isolates of C. elegans and show that both lifespan and locomotor healthspan display substantial heritability. To facilitate analysis of large numbers of worms, we developed a robotic imaging system capable of simultaneous automated monitoring of activity, lifespan, and locomotor healthspan in up to 2,304 populations containing a total of ~90,000 animals. We applied the automated system to conduct a large-scale RNA interference screen for genes that affect lifespan and locomotor healthspan. The WormCamp system is complementary to other current automated methods for assessing C. elegans aging and is well suited for efficiently screening large numbers of conditions.

SummaryReal world memories are formed in a particular context and are not acquired or recalled in isolation 1-5. Time is a key variable in the organization of memories, since events experienced close in time are more likely to be meaningfully associated, while those experienced with a longer interval are not1-4. How does the brain segregate events that are temporally distinct? Here, we report that a delayed (12-24h) increase in the expression of the C-C chemokine receptor type 5 (CCR5), an immune receptor well known as a co-receptor for HIV infection6,7, following the formation of a contextual memory, determines the duration of the temporal window for associating or linking that memory with subsequent memories. This delayed CCR5 expression in mouse dorsal CA1 (dCA1) neurons results in a decrease in neuronal excitability, which in turn negatively regulates neuronal memory allocation, thus reducing the overlap between dCA1 memory ensembles. Lowering this overlap affects the ability of one memory to trigger the recall of the other, thus closing the temporal window for memory linking. Remarkably, our findings also show that an age-related increase in CCL5/CCR5 expression leads to impairments in memory linking in aged mice, which could be reversed with a CCR5 knockout and an FDA approved drug that inhibits this receptor, a result with significant clinical implications. All together the findings reported here provide the first insights into the molecular and cellular mechanisms that shape the temporal window for memory linking.

Nature is rife with networks that are functionally optimized to propagate inputs in order to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single "target" link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programmed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of a new class of constraint-satisfaction problems., 37 pages (single column), 14 figures, 1 table. J.W.R. and H.R. contributed equally to this work

The rapid rise of viscosity or relaxation time upon supercooling is a universal hallmark of glassy liquids. The temperature dependence of viscosity, however, is quite nonuniversal for glassy liquids and is characterized by the system’s “fragility,” with liquids with nearly Arrhenius temperature-dependent relaxation times referred to as strong liquids and those with super-Arrhenius behavior referred to as fragile liquids. What makes some liquids strong and others fragile is still not well understood. Here, we explore this question in a family of harmonic spheres that range from extremely strong to extremely fragile, using “softness,” a structural order parameter identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as the same linear combination of structural quantities across the entire family of liquids studied. We then use softness to identify the factors controlling fragility.

As liquids approach the glass transition temperature, dynamical heterogeneity emerges as a crucial universal feature of their behavior. Dynamic facilitation, where local motion triggers further motion nearby, plays a major role in this phenomenon. Here we show that long-range, elastically-mediated facilitation appears below the mode-coupling temperature, adding to the short-range component present at all temperatures. Our results suggest deep connections between the supercooled liquid and glass states, and pave the way for a deeper understanding of dynamical heterogeneity in glassy systems., 6 pages, 4 figures. SM included as ancillary

Spatiotemporal relationships between genes expressed in tissues likely reflect physicochemical principles that range from stoichiometric interactions to co-organized fractals with characteristic scaling. For key structural factors within the nucleus and extracellular matrix (ECM), gene-gene power laws are found to be characteristic across several tumor types in The Cancer Genome Atlas (TCGA) and across single-cell RNA-seq data. The nuclear filamentLMNB1scales with many tumor-elevated proliferation genes that predict poor survival in liver cancer, and cell line experiments showLMNB1regulates cancer cell cycle. Also high in the liver, lung, and breast tumors studied here are the main fibrosis-associated collagens,COL1A1andCOL1A2, that scale stoichiometrically with each other and super-stoichiometrically with a pan-cancer fibrosis gene set. However, high fibrosis predicts prolonged survival of patients undergoing therapy and does not correlate withLMNB1. Single-cell RNA-seq data also reveal scaling consistent with the pan-cancer power laws obtained from bulk tissue, allowing new power law relations to be predicted. Lastly, although noisy data frustrate weak scaling, concepts such as stoichiometric scaling highlight a simple, internal consistency check to qualify expression data.ClassificationApplied Physical Sciences (major) and Cell Biology (minor)Significance StatementNon-linear scaling analyses pervade polymer physics and chemistry and conceivably provide new insight into polymeric assemblies of genes expressed in tissues as well as co-regulated gene sets. Fractal scaling and stoichiometric scaling are among the gene-gene power law results identified here for key structural polymers in nuclei or extracellular matrix in human cancer data. Among nuclear envelope factors that might scale with DNA mass, only one nuclear filament scales with tumor proliferation and predicts poor survival in some cancer types. Collagen-1 scales with fibrosis and also tends to increase in multiple tumor types, but patients in therapy surprisingly survive longest with the highest levels of fibrosis, consistent with a therapeutic response.

Significance It is well appreciated that many disordered materials deform their shape irreversibly (plastically) under an external load (e.g., memory foam). Here, we show that this plasticity can be exploited to train materials to develop novel elastic responses by straining them periodically. By applying different periodic strains to a common viscoelastic material, we are able to design a number of different responses. These include a maximally negative Poisson’s ratio, bistable behavior, and nonlocal bond-specific responses. In contrast to computer-aided design, we rely on plasticity to self-organize the system in response to local stresses. This approach shows promise to achieve an unprecedented control over behavior at large strains well beyond the linear-response regime.

The authors show how disordered networks aged under external strain attain a memory of their aging history.

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between plastic dynamics and the structure of a particle's local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements, called "softness." Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data is limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle's observed mobility, allowing for the use of statistical models which are both simpler and provide greater accuracy in limited data sets. Second, we employ persistent homology as a systematic means of identifying simple, topologically-informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information which predicts small variations in the response is also predictive of where plastic events will localize. We also find that excellent accuracy is achieved in athermal sheared packings using simply a particle's species and the number of nearest neighbor contacts., 21 pages (double column), 12 figures

Jammed soft disks exhibit avalanches of particle rearrangements under quasistatic shear. We introduce a framework for understanding the statistics of the progression of avalanches. We follow the avalanches (simulated using steepest descent energy minimization) to decompose them into individual localized rearrangements. We characterize the local structural environment of each particle by a machine-learned quantity, softness, designed to be highly correlated with rearrangements, and analyze the interplay between softness, rearrangements, and strain. Local yield strain has long been incorporated into elastoplastic models; here we show that softness provides a useful proxy for local yield strain. Our findings demonstrate that elastoplastic models must take into account the fully tensorial strain field in order to include the effects of changes in local yield strain due to rearrangements and introduce the equations underpinning a structuro-elastoplastic model that includes local softness.

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been applied to an array of standard (Arrhenius and super-Arrhenius) glass formers, where work on "soft spots" indicates a connection between the linear vibrational response of a configuration and the energy barriers to non-linear deformations. Here we study the Voronoi model, which takes its inspiration from dense epithelial monolayers and which displays anomalous, sub-Arrhenius scaling of its dynamical relaxation time with decreasing temperature. Despite these differences, we find that the likelihood of rearrangements can vary by several orders of magnitude within the model tissue and extract a local structural quantity, "softness" that accurately predicts the temperature-dependence of the relaxation time. We use an information-theoretic measure to quantify the extent to which softness determines impending topological rearrangements; we find that softness captures nearly all of the information about rearrangements that is obtainable from structure, and that this information is large in the solid phase of the model and decreases rapidly as state variables are varied into the fluid phase., 13 pages, 7 figures, questions both answered and unanswered

Complex networks encountered in biology are often characterized by significant structural diversity. Whether it be differences in the three-dimensional structure of allosteric proteins, or the variation among the micro-scale structures of organisms' cerebral vasculature systems, identifying relationships between structure and function often poses a difficult challenge. Here we showcase an approach to characterizing structure-function relationships in complex networks applied in the context of flow networks tuned to perform specific functions. Using persistent homology, we analyze flow networks tuned to perform complex multifunctional tasks, answering the question of how local changes in the network structure coordinate to create functionality at at the scale of the entire network. We find that the response of such networks encodes hidden topological features - sectors of uniform pressure - that are not apparent in the underlying network architectures, Regardless of differences in local connectivity, these features provide a universal topological description for all networks that perform these types of functions. We show that these features correlate strongly with the tuned response, providing a clear topological relationship between structure and function and structural insight into the limits of multifunctionality., 22 pages (double column), 12 figures

We compare glassy dynamics in two liquids that differ in the form of their interaction potentials. Both systems have the same repulsive interactions but one has also an attractive part in the potential. These two systems exhibit very different dynamics despite having nearly identical pair correlation functions. We demonstrate that a properly weighted integral of the pair correlation function, which amplifies the subtle differences between the two systems, correctly captures their dynamical differences. The weights are obtained from a standard machine learning algorithm., Comment: 6 pages, 5 figures

Materials and machines are often designed with particular goals in mind, so that they exhibit desired responses to given forces or constraints. Here we explore an alternative approach, namely physical coupled learning. In this paradigm, the system is not initially designed to accomplish a task, but physically adapts to applied forces to develop the ability to perform the task. Crucially, we require coupled learning to be facilitated by physically plausible learning rules, meaning that learning requires only local responses and no explicit information about the desired functionality. We show that such local learning rules can be derived for any physical network, whether in equilibrium or in steady state, with specific focus on two particular systems, namely disordered flow networks and elastic networks. By applying and adapting advances of statistical learning theory to the physical world, we demonstrate the plausibility of new classes of smart metamaterials capable of adapting to users' needs in-situ., Comment: 18 pages, 9 figures

Amorphous solids lack long-range order. Therefore identifying structural defects - akin to dislocations in crystalline solids - that carry plastic flow in these systems remains a daunting challenge. By comparing many different structural indicators in computational models of glasses, under a variety of conditions we carefully assess which of these indicators are able to robustly identify the structural defects responsible for plastic flow in amorphous solids. We further demonstrate that the density of defects changes as a function of material preparation and strain in a manner that is highly correlated with the macroscopic material response. Our work represents an important step towards predicting how and when an amorphous solid will fail from its microscopic structure.

Nanometrically thin glassy films depart strikingly from the behavior of their bulk counterparts. We investigate whether the dynamical differences between bulk and thin film glasses can be understood by differences in local microscopic structure. We employ machine-learning methods that have previously identified strong correlations between local structure and particle rearrangement dynamics in bulk systems. We show that these methods completely fail to detect key aspects of thin-film glassy dynamics. Furthermore, we show that no combination of local structural features drawn from a very general set of two- and multi-point functions is able to distinguish between particles at the center of film and those in intermediate layers where the dynamics are strongly perturbed., Comment: 8 pages, 7 figures

Recent advances in designing meta-materials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally-efficient approach, we extend this idea in order to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any pair of target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ~1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic two- and three-dimensional systems that exhibit these responses. This targeted behavior is reminiscent of the long-range coupled conformational changes that often occur during allostery in proteins. The ease with which we create these responses may give insight into why allostery is a common means for the regulation of activity in biological molecules., Comment: 28 pages (single column), 5 figures, 2 videos

Migration through micron-size constrictions has been seen to rupture the nucleus, release nuclear-localized GFP, and cause localized accumulations of ectopic 53BP1 – a DNA repair protein. Here, constricted migration of two human cancer cell types and primary mesenchymal stem cells (MSC) increases DNA breaks throughout the nucleoplasm as assessed by endogenous damage markers and by electrophoretic ‘comet’ measurements. Migration also causes multiple DNA repair proteins to segregate away from DNA, with cytoplasmic mis-localization sustained for many hours as is relevant to delayed repair. Partial knockdown of repair factors that also regulate chromosome copy numbers is seen to increase DNA breaks in U2OS osteosarcoma cells without affecting migration and with nucleoplasmic patterns of damage similar to constricted migration. Such depletion also causes aberrant levels of DNA. Migration-induced nuclear damage is nonetheless reversible for wild-type and sub-cloned U2OS cells, except for lasting genomic differences between stable clones as revealed by DNA arrays and sequencing. Gains and losses of hundreds of megabases in many chromosomes are typical of the changes and heterogeneity in bone cancer. Phenotypic differences that arise from constricted migration of U2OS clones are further illustrated by a clone with a highly elongated and stable MSC-like shape that depends on microtubule assembly downstream of the transcription factor GATA4. Such changes are consistent with reversion to a more stem-like state upstream of cancerous osteoblastic cells. Migration-induced genomic instability can thus associate with heritable changes.

Plastic deformation produced by external strains directs the aging of disordered materials to create unusual elastic response., Disordered materials are often out of equilibrium and evolve very slowly in a rugged and tortuous energy landscape. This slow evolution, referred to as aging, is deemed undesirable as it often leads to material degradation. However, we show that aging also encodes a memory of the stresses imposed during preparation. Because of inhomogeneous local stresses, the material itself decides how to evolve by modifying stressed regions differently from those under less stress. Because material evolution occurs in response to stresses, aging can be “directed” to produce sought-after responses and unusual functionalities that do not inherently exist. Aging obeys a natural “greedy algorithm” as, at each instant, the material simply follows the path of most rapid and accessible relaxation. Our experiments and simulations illustrate directed aging in examples in which the material’s elasticity transforms as desired because of an imposed deformation.

We discuss the properties of the distributions of energies of minima obtained by gradient descent in complex energy landscapes. We find strikingly similar phenomenology across several prototypical models. We particularly focus on the distribution of energies of minima in the analytically well-understood p-spin-interaction spin glass model. We numerically find non-Gaussian distributions that resemble the Tracy-Widom distributions often found in problems of random correlated variables, and non-trivial finite-size scaling. Based on this, we propose a picture of gradient descent dynamics that highlights the importance of a first-passage process in the eigenvalues of the Hessian. This picture provides a concrete link to problems in which the Tracy-Widom distribution is established. Aspects of this first-passage view of gradient-descent dynamics are generic for non-convex complex landscapes, rationalizing the commonality that we find across models., 11 pages, 5 figures

We experimentally characterize heterogeneous nonexponential relaxation in bidisperse supercooled colloidal liquids utilizing a recent concept called "softness" [Phys. Rev. Lett. 114, 108001 (2015)PRLTAO0031-900710.1103/PhysRevLett.114.108001]. Particle trajectory and structure data enable classification of particles into subgroups with different local environments and propensities to hop. We determine residence times t_{R} between particle hops and show that t_{R} derived from particles in the same softness subgroup are exponentially distributed. Using the mean residence time t[over ¯]_{R} for each softness subgroup, and a Kramers' reaction rate model, we estimate the activation energy barriers E_{b} for particle hops, and show that both t[over ¯]_{R} and E_{b} are monotonic functions of softness. Finally, we derive information about the combinations of large and small particle neighbors that determine particle softness, and we explicitly show that multiple exponential relaxation channels in the supercooled liquid give rise to its nonexponential behavior.

Inspired by protein folding, we introduce funnels into the complex cost function landscapes of two processes, the tuning of networks, and the jamming of ideal spheres. In both processes, geometrical frustration plays a role -- tuning pressure differences between pairs of target nodes far from the source in a flow network impedes tuning of nearby pairs more than the reverse process, while unjamming the system in one region can make it more difficult to unjam elsewhere. By modifying the cost functions to control the order in which functions are tuned or regions unjam, we smooth out local minima while leaving global minima unaffected, significantly increasing the success rate of reaching global minima.

The ability to reroute and control flow is vital to the function of venation networks across a wide range of organisms. By modifying individual edges in these networks, either by adjusting edge conductances or creating and destroying edges, organisms can robustly control the propagation of inputs to perform specific tasks. However, a fundamental disconnect exists between the structure and function of these networks: networks with different local architectures can perform the same functions. Here we answer the question of how structural changes at the microscopic level are able to collectively create functionality at the scale of an entire network. Using persistent homology, we analyze networks tuned to perform complex multifunctional tasks. We find that the responses of such networks encode a hidden topological structure composed of sectors of uniform pressure. Although these sectors are not apparent in the underlying network architectures, we find that they nonetheless correlate strongly with the tuned function. We conclude that the connectivity of these sectors, rather than that of the individual nodes, provides a quantitative relationship between structure and function in flow networks. Finally, we use this topological description to place a bound on the limits of task complexity., Comment: 5 pages (double column), 3 figures. arXiv admin note: substantial text overlap with arXiv:1901.00822

Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2%. Even though we developed the method for isotropic animal tissues, we demonstrate it on an anisotropic plant tissue. This framework could also be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.

Nuclear distortion such as in 3D migration concentrates chromatin locally and causes complementary segregation of mobile factors within the nucleus. This is unavoidable because chromatin is compressible., As a cell squeezes its nucleus through adjacent tissue, penetrates a basement membrane, or enters a small blood capillary, chromatin density and nuclear factors could in principle be physically perturbed. Here, in cancer cell migration through rigid micropores and in passive pulling into micropipettes, local compaction of chromatin is observed coincident with depletion of mobile factors. Heterochromatin/euchromatin was previously estimated from molecular mobility measurements to occupy a volume fraction f of roughly two-thirds of the nuclear volume, but based on the relative intensity of DNA and histones in several cancer cell lines drawn into narrow constrictions, f can easily increase locally to nearly 100%. By contrast, mobile proteins in the nucleus, including a dozen that function as DNA repair proteins (e.g., BRCA1, 53BP1) or nucleases (e.g., Cas9, FokI), are depleted within the constriction, approaching 0%. Such losses—compounded by the occasional rupture of the nuclear envelope—can have important functional consequences. Studies of a nuclease that targets a locus in chromosome-1 indeed show that constricted migration delays DNA damage.

In the beating heart, cardiac myocytes (CMs) contract in a coordinated fashion, generating contractile wave fronts that propagate through the heart with each beat. Coordinating this wave front requires fast and robust signaling mechanisms between CMs. The primary signaling mechanism has long been identified as electrical: gap junctions conduct ions between CMs, triggering membrane depolarization, intracellular calcium release, and actomyosin contraction. In contrast, we propose here that, in the early embryonic heart tube, the signaling mechanism coordinating beats is mechanical rather than electrical. We present a simple biophysical model in which CMs are mechanically excitable inclusions embedded within the extracellular matrix (ECM), modeled as an elastic-fluid biphasic material. Our model predicts strong stiffness dependence in both the heartbeat velocity and strain in isolated hearts, as well as the strain for a hydrogel-cultured CM, in quantitative agreement with recent experiments. We challenge our model with experiments disrupting electrical conduction by perfusing intact adult and embryonic hearts with a gap junction blocker, β-glycyrrhetinic acid (BGA). We find this treatment causes rapid failure in adult hearts but not embryonic hearts—consistent with our hypothesis. Last, our model predicts a minimum matrix stiffness necessary to propagate a mechanically coordinated wave front. The predicted value is in accord with our stiffness measurements at the onset of beating, suggesting that mechanical signaling may initiate the very first heartbeats.

States of self stress, organizations of internal forces in many-body systems that are in equilibrium with an absence of external forces, can be thought of as the constitutive building blocks of the elastic response of a material. In overconstrained disordered packings they have a natural mathematical correspondence with the zero-energy vibrational modes in underconstrained systems. While substantial attention in the literature has been paid to diverging length scales associated with zero- and finite-energy vibrational modes in jammed systems, less is known about the spatial structure of the states of self stress. In this work we define a natural way in which a unique state of self stress can be associated with each bond in a disordered spring network derived from a jammed packing, and then investigate the spatial structure of these bond-localized states of self stress. This allows for an understanding of how the elastic properties of a system would change upon changing the strength or even existence of any bond in the system., Comment: 21 pages (single column), 10 figures

Structural defects within amorphous packings of symmetric particles can be characterized using a machine learning approach that incorporates structure functions of radial distances and angular arrangement. This yields a scalar field, \emph{softness}, that correlates with the probability that a particle is about to rearrange. However, when particle shapes are elongated, as in the case of dimers and ellipses, we find the standard structure functions produce imprecise softness measurements. Moreover, ellipses exhibit deformation profiles in stark contrast to circular particles. In order to account for effects of orientation and alignment, we introduce new structure functions to recover predictive performance of softness, as well as provide physical insight to local and extended dynamics. We study a model disordered solid, a bidisperse two-dimensional granular pillar, driven by uniaxial compression and composed entirely of monomers, dimers, or ellipses. We demonstrate how the computation of softness via support vector machine extends to dimers and ellipses with the introduction of new orientational structure functions. Then, we highlight the spatial extent of rearrangements and defects, as well as their cross-correlation, for each particle shape. Finally, we demonstrate how an additional machine learning algorithm, recursive feature elimination, provides an avenue to better understand how softness arises from particular structural aspects. We identify the most crucial structure functions in determining softness and discuss their physical implications., 16 pages, 12 figures

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity, softness, that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements, while finding a large variability within high-energy grain boundaries. As has been found in glasses [9,19,26], the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as the Poisson's ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a new and more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and non-linear regimes. In particular, it is shown that the Poisson's ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting two-dimensional sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks' susceptibility to tuning to design networks that posses a distribution of stiffer and more compliant bonds, and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of novel mechanical metamaterials.

We investigate the short-, medium-, and long-range structure of soft-disk configurations for a wide range of area fractions and simulation protocols by converting the real-space spectrum of volume fraction fluctuations for windows of width $L$ to the distance $h(L)$ from the window boundary over which fluctuations occur. Rapidly quenched unjammed configurations exhibit size-dependent super-Poissonian long-range features that surprisingly approach the totally random limit even close to jamming. Above and just below jamming, the spectra exhibit a plateau $h(L)={h}_{e}$ for $L$ larger than particle size and smaller than a cutoff ${L}_{c}$ beyond which there are long-range fluctuations. The value of ${h}_{e}$ is independent of protocol and characterizes the putative hyperuniform limit. This behavior is compared with that for Einstein solids, with and without hyperuniformity-destroying defects. We find that key structural features of the particle configurations are more evident, as well as easier and more intuitive to quantify, using the real-space spectrum of hyperuniformity lengths rather than the spectral density.