arXiv subjects

Andrea J. Liu

Publications and source records attributed to Andrea J. Liu.

At least 55 records · Page 3Linked to original sources

arXiv2020

Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica

The fragility of a glassforming liquid characterizes how rapidly its relaxation dynamics slow down with cooling. The viscosity of strong liquids follows an Arrhenius law with a temperature-independent barrier height to rearrangements responsible for relaxation, whereas fragile liquids experience a much faster increase in their dynamics, suggesting a barrier height that increases with decreasing temperature. Strong glassformers are typically network glasses, while fragile glassformers are typically molecular or hard-sphere-like. As a result of these differences at the microscopic level, strong and fragile glassformers are usually treated separately from a theoretical point of view. Silica is the archetypal strong glassformer at low temperatures, but also exhibits a mysterious strong-to-fragile crossover at higher temperatures. Here we show that softness, a structure-based machine learned parameter that has previously been applied to fragile glassformers provides a useful description of model liquid silica in the strong and fragile regimes, and through the strong-to-fragile crossover. Just as for fragile glassformers, the relationship between softness and dynamics is invariant and Arrhenius in all regimes, but the average softness changes with temperature. The strong-to-fragile crossover in silica is not due to a sudden, qualitative change in structure, but can be explained by a simple Arrhenius form with a continuously and linearly changing local structure. Our results unify the study of liquid silica under a single simple conceptual picture.

cond-mat.soft↗

arXiv2020

Effect of aging on the non-linear elasticity and memory formation in materials

Disordered solids often change their elastic response as they slowly age. Using experiments and simulations, we study how aging disordered planar networks under an applied stress affects their nonlinear elastic response. We are able to modify dramatically the elastic properties of our systems in the non-linear regime. Using simulations, we study two models for the microscopic evolution of properties of such a material; the first considers changes in the material strength while the second considers distortions in the microscopic geometry. Both models capture different aspects of the experiments including the encoding of memories of the aging history of the system and the dramatic effects on the material's nonlinear elastic properties. Our results demonstrate how aging can be used to create complex elastic behavior in the nonlinear regime.

cond-mat.soft↗

arXiv2020

Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function

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.

cond-mat.stat-mech↗

arXiv2019

The hidden topological structure of flow network functionality

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.

cond-mat.soft↗

arXiv2019

Revealing structure-function relationships in functional flow networks via persistent homology

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.

physics.soc-ph↗

arXiv2019

Tuning and jamming reduced to their minima

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.

cond-mat.soft↗

arXiv2019

Periodic training of creeping solids

We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local "allosteric" interactions. Periodic driving can couple an applied "source" strain to a target strain over a path in the energy landscape. This coupling allows control of the system's response even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.

cond-mat.soft↗

arXiv2019

Directed aging, memory and Nature's greed

Disordered materials are often out of equilibrium and evolve very slowly. This allows a memory of the imposed strains or preparation conditions to be encoded in the material. Here we consider "directed aging", where the elastic properties of a material evolve in the direction defined by its aging history. The evolution to a lower-energy configuration is controlled by steepest decent and affects stressed regions differently from unstressed ones. This process can be considered to be a "greedy algorithm" of Nature. Our experiments and simulations illustrate directed aging in examples in which the material's elasticity evolves as a direct consequence of the imposed deformation; the material itself decides how to evolve in order to produce responses that were not present inherently in the material.

cond-mat.soft↗

arXiv2019

Machine Learning Characterization of Structural Defects in Amorphous Packings of Dimers and Ellipses

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.

cond-mat.soft↗

arXiv2018

The limits of multifunctionality in tunable networks

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.

cond-mat.soft↗

arXiv2018

Machine learning determination of atomic dynamics at grain boundaries

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.

cond-mat.mtrl-sci↗

arXiv2018

Statistical properties of 3D cell geometry from 2D slices

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\%. This framework could be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.

q-bio.QM↗

arXiv2018

Two diverging length scales in the structure of jammed packings

At densities higher than the jamming transition for athermal, frictionless repulsive spheres we find two distinct length scales, both of which diverge as a power law as the transition is approached. The first, $ξ_{Z}$, is associated with the two-point correlation function for the number of contacts on two particles as a function of the particle separation. The second, $ξ_{f}$, is associated with contact-number fluctuations in subsystems of different sizes. On scales below $ξ_{f}$ the fluctuations are highly suppressed, similar to the phenomenon of hyperuniformity usually associated with density fluctuations. The exponents for the divergence of $ξ_{Z}$ and $ξ_{f}$ are different and appear to be different in two and three dimensions.

cond-mat.soft↗

arXiv2017

Role of local response in manipulating the elastic properties of disordered solids by bond removal

We explore the range over which the elasticity of disordered spring networks can be manipulated by the removal of selected bonds. By taking into account the local response of a bond, we demonstrate that the effectiveness of pruning can be improved so that auxetic (i.e., negative Poisson's ratio) materials can be designed without the formation of cracks even while maintaining the global isotropy of the network. The bulk, $B$, and shear, $G$, moduli scale with the number of bonds removed and we estimate the exponents characterizing these power laws. We also find that there are spatial correlation lengths in the change of $B$ and $G$ upon removing different bonds that diverge as the network approaches the isostatic limit where the excess coordination number $ΔZ\rightarrow0$.

cond-mat.soft↗

arXiv2017

Linking microscopic and macroscopic response in disordered solids

The modulus of a rigid network of harmonic springs depends on the sum of the energies in each of the bonds due to the applied distortion: compression in the case of the bulk modulus, $B$, or shear in the case of the shear modulus, $\mathcal{G}$. The distortion need not be global and we introduce a local modulus, $L_{i}$, associated with changing the equilibrium length of a single bond, $i$, in the network. We show that $L_{i}$ is useful for understanding many aspects of the mechanical response of the entire system. For example, it allows an understanding, and efficient computation, of how each bond in a network contributes to global properties such as $B$ and $\mathcal{G}$ and sheds light on how a particular bond's contribution to one modulus is, or is not, correlated with its contribution to another.

cond-mat.soft↗

arXiv2017

Period proliferation in periodic states in cyclically sheared jammed solids

Athermal disordered systems can exhibit a remarkable response to an applied oscillatory shear: after a relatively few shearing cycles, the system falls into a configuration that had already been visited in a previous cycle. After this point the system repeats its dynamics periodically despite undergoing many particle rearrangements during each cycle. We study the behavior of orbits as we approach the jamming point in simulations of jammed particles subject to oscillatory shear at fixed pressure and zero temperature. As the pressure is lowered, we find that it becomes more common for the system to find periodic states where it takes multiple cycles before returning to a previously visited state. Thus, there is a proliferation of longer periods as the jamming point is approached.

cond-mat.soft↗

arXiv2017

Instabilities of jammed packings of frictionless spheres under compression

We consider the contribution to the density of vibrational states and the distribution of energy barrier heights of incipient instabilities in a glass modeled by a jammed packing of spheres. On approaching an instability, the frequency of a normal mode and the height of the energy barrier to cross into a new ground state both vanish. These instabilities produce a contribution to the density of vibrational states that scales as $ω^3$ at low frequencies $ω$, but which vanishes in the thermodynamic limit. In addition, they affect an anharmonic property, the distribution of energy barriers $ΔH$, giving a contribution that scales as $ΔH^{-1/3}$ at low barrier heights, which should be universal for jammed and glassy systems.

cond-mat.soft↗

arXiv2017

Emergent SO(3) Symmetry of the Frictionless Shear Jamming Transition

We study the shear jamming of athermal frictionless soft spheres, and find that in the thermodynamic limit, a shear-jammed state exists with different elastic properties from the isotropically-jammed state. For example, shear-jammed states can have a non-zero residual shear stress in the thermodynamic limit that arises from long-range stress-stress correlations. As a result, the ratio of the shear and bulk moduli, which in isotropically-jammed systems vanishes as the jamming transition is approached from above, instead approaches a constant. Despite these striking differences, we argue that in a deeper sense, the shear jamming and isotropic jamming transitions actually have the same symmetry, and that the differences can be fully understood by rotating the six-dimensional basis of the elastic modulus tensor.

cond-mat.soft↗