Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session M21: Disordered and Glassy Systems IRecordings Available
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Sponsoring Units: DSOFT Chair: Varda Hagh, University of Chicago Room: McCormick Place W-185D |
Wednesday, March 16, 2022 8:00AM - 8:12AM |
M21.00001: Local dynamical heterogeneity in glass formers Patrick Charbonneau, Giulio Biroli, Yi Hu, Giampaolo Folena, Francesco Zamponi Assessing the role of local order and of more extended static correlations on the dynamics of deeply supercooled liquids is one of the foremost open problems in the physics of glasses. We here study the local dynamical fluctuations in glass-forming models. Our calculation for d→∞ systems reveals that single-particle observables, such as squared particle displacements, display diverging fluctuations around the dynamical (or mode-coupling) transition, due to the emergence of nontrivial correlations between displacements along different directions. This effect also gives rise to a diverging non-Gaussian parameter, α2(t). The local dynamics therefore becomes quite rich upon approaching the glass transition. The finite-d remnant of this phenomenon further provides a long sought-after, first-principle explanation for the growth of α2(t) around the glass transition that is not based on multi-particle correlations. |
Wednesday, March 16, 2022 8:12AM - 8:24AM |
M21.00002: Fragility in Glassy Liquids: A Structural Approach Based on Machine Learning Indrajit Tah, Sean A Ridout, Andrea J Liu The rapid growth of viscosity or relaxation time upon supercooling is universal hallmark of glassy liquids. The temperature dependence of the viscosity, however, is quite non-universal for glassy liquids and is characterized by the system's ``fragility,'' with liquids with a nearly Arrhenius temperature-dependent viscosities referred to as strong liquids and those with strongly 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 glassy liquids that range from extremely strong to extremely fragile, using ``softness,'' a structural variable identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as a linear combination of structural quantities, and show that the same linear combination is successful in predicting rearrangements across the entire family of glassy liquids. We find that fragility is reflected in the softness-dependence of energy barriers. |
Wednesday, March 16, 2022 8:24AM - 8:36AM |
M21.00003: The high-dimensional fate of hard spheres liquids, glasses, and crystals Peter K Morse, Patrick Charbonneau Simple liquids and crystals are, to a great extent, well described by the hard sphere model. Yet, while much is known about hard sphere liquids in both high and low dimensions, little is known about their crystal branch for d > 3 or about their liquid branch in intermediate dimensions. This question is particularly important because controlled theoretical developments for dense liquids and glasses are often only accessible in the limit d → ∞. I first consider the the thermodynamic fate of the crystal in high dimensions. Three scenarios are considered: A) crystallization is impeded, making the glass the densest packing; B) crystallization is possible but dynamically prohibited; or C) the crystal phase is thermodynamically accessible from the liquid state. Simulation results in d=3-10 show that the crystal remains thermodynamically stable, and the dimensional trend suggests that scenario C is most likely. Second, I show that the hypernetted chain closure captures both the structure and dynamics of hard sphere liquids and gives a reasonable approximation to the dynamical (mode-coupling) crossover in intermediate dimensions. This identification thus provides a path for relating the high-d liquid branch to actual supercooled liquids. |
Wednesday, March 16, 2022 8:36AM - 8:48AM |
M21.00004: Experimentally observing the Gardner transition in a 3D colloidal glass Eric M Schwen, Meera Ramaswamy, Danilo B Liarte, Itai Cohen As a glassy suspension is compressed towards jamming it can undergo an additional phase transition known as the Gardner transition. This fundamental phase transition corresponds to the emergence of a marginal glass phase where energy basins split into a hierarchy of marginally stable subbasins. The Gardner transition has recently been found for mean-field systems in infinite dimensions but evidence from simulations and experiments in three dimensions is limited. Here, we investigate the Gardner transition by directly observing the marginal states in a binary glass of silica microspheres. We use a custom-built compression cell mounted on a confocal microscope to induce shifts between marginally stable configurations by repeatedly compressing the glass. We image and track the silica particles over time to measure the glass cage size and the subcage spacing between marginal configurations. We further investigate the diverging timescale, susceptibility, and length scale associated with fluctuations in the cage size and spacing at the Gardner transition. These experiments will provide a foundation for further exploration of the marginal glass phase and are an important step in relating glass formation to jamming. |
Wednesday, March 16, 2022 8:48AM - 9:00AM |
M21.00005: Rearrangements in cyclically sheared small jammed packings Chloe W Lindeman, Keyer Thyme, Sidney R Nagel Jammed systems which are cyclically sheared will often find periodic orbits after just a few cycles, thus encoding a memory of their drive amplitude. Aspects of these periodic orbits can be captured by models of interacting hysteretic regions ("hysterons") that represent particle rearrangements in the jammed packings. However, there remains a gap between hysterons as a model and the rearrangements they aim to describe. Here, we study small jammed systems — on the order of 10 particles — to probe the nature of rearrangements. We find that a substantial fraction of such packings include avalanches, a hallmark of interactions between rearrangements, in their periodic orbits. By examining the rearrangements in detail, we can begin to paint a more robust picture of the extent to which they behave like model hysterons. |
Wednesday, March 16, 2022 9:00AM - 9:12AM |
M21.00006: Using Nuclear Resonance Time Domain Interferometry to study Relaxation Times of Glycerol and other Liquids Marc T Pavlik, Dennis E Brown, Michael Y Hu, Jiyong Zhao, Larry B Lurio, Esen E Alp Nuclear resonance time domain interferometry (NR-TDI) is a new spectroscopy method used to study the slow dynamics of liquids at the atomic and molecular length scales. By employing a new method of using a stationary two-line magnetized 57Fe foil as a source and a stationary single-line stainless steel foil analyzer. Our new technique of adding an annular slit before the silicon avalanche photodiode (APD) detector enables a wide range of momentum transfers 1 to 100 nm-1 with high count rates of up to 160 Hz with a Dq resolution of ±1.7 nm-1. The Kohlrausch-Williams-Watts (KWW) model was used to extract relaxation times for glycerol ranging from 2 to 600 ns as a function of temperature through the glass transition. These relaxation times give insight into the dynamics of the electron density fluctuations of glycerol as a function of temperature and momentum transfers. |
Wednesday, March 16, 2022 9:12AM - 9:24AM |
M21.00007: A Theory of Localized Excitations in Supercooled Liquids Muhammad R Hasyim, Kranthi K Mandadapu The dynamics of glass-forming liquids dramatically slow down with decreasing temperature and are accompanied by dynamical heterogeneity. These phenomena can be understood from a structure-based perspective, where the relaxation behavior originates from the static properties, or a dynamics-based perspective, e.g., dynamical facilitation (DF) theory, where localized excitations, whose origin is assumed to be independent of structure, drive glassy dynamics by facilitating the relaxation of nearby excitations. Our work [1] shows that excitations are connected to the liquid inherent structure and elasticity by constructing a theory where excitations are localized pure-shear events, induced by T1 transitions that re-organize the first solvation shell. The theory predicts that the energy barrier to form excitations is a function of the inherent shear modulus and radial distribution function. The predicted energy barrier is compared to that of DF theory, where quantitative agreement is found across six models of poly-disperse glass formers. These results demonstrate a strong connection between the two competing perspectives of glassy dynamics. |
Wednesday, March 16, 2022 9:24AM - 9:36AM |
M21.00008: Characterization of Void space, Large-Scale Structure, and Transport Properties of Maximally Random Jammed Packings of Superballs Charles E Maher, Salvatore Torquato, Frank H Stillinger The study of dense packings of nonspherical particles enables one to ascertain how rotational degrees of freedom affect packing behavior. We generate dense, maximally random jammed packings of convex superballs, a family of deformations of the sphere, whose degree of deformation is characterized by the deformation parameter p and interpolate between cuboidal and octahedral shapes via the sphere. Here, we characterize their large-scale structure by examining the small wavenumber behavior of their structure factors and spectral densities, and find these packings are effectively hyperuniform. We also compute their distribution of pore sizes, which tend to become smaller as the particles become more aspherical. We also estimate how their transport properties vary as a function of shape. Each of the structural characteristics computed here exhibits an extremum at the sphere point and varies nonanalytically as the particles become aspherical. We find the nonanalytic behavior in the packing fraction on either side of the sphere point is nearly linear, and determine that the rattler fraction decreases rapidly as the particles become aspherical. |
Wednesday, March 16, 2022 9:36AM - 9:48AM |
M21.00009: Nucleation is absent in the equilibration of supercooled liquids below TMCT Rahul N Chacko, François P Landes, Giulio Biroli, Olivier Dauchot, Andrea J Liu, David R Reichman According to random first-order transition (RFOT) theory, supercooled liquids cooled below the mode-coupling temperature TMCT exhibit a transition to a state in which deep energy landscape minima are ubiquitous, but transitions between them are slow [1]. Nucleation has been suggested to play a key role in the sub-TMCT dynamics of these systems, with structural evolution proceeding via the size-limited nucleation of droplets of "aperiodic crystal" [2] within domains of different, incompatible states [3,4]. In this talk, we present direct evidence from molecular dynamics simulations that nucleation is absent from the equilibration dynamics of supercooled liquids below TMCT. The structural evolution, characterized by softness [5], is instead remarkably local. We find that softness sensitively captures the evolution of local structure on long time scales while remaining insensitive to large but mostly-reversible changes in the inherent state on short time scales. |
Wednesday, March 16, 2022 9:48AM - 10:00AM |
M21.00010: A pinned contact line encodes a detailed memory of its motion. Ashbell Abraham, Shae Cole, Esmeralda Orozco, Charity Lizardo, Nate Martin, Alex Johnson, Aaron Bock, Nathan C Keim The contact line around a drop of liquid generally has an irregular shape, revealing the disorder of the solid substrate beneath. In this talk we show that the shape of a contact line also encodes a memory of the magnitude of its past motion. We “train” the system by cyclically injecting and withdrawing a constant volume of liquid. The contact line reaches a steady state and is not changed by further cycles. Once encoded, the memory of this volume can be retrieved by additional cycles of driving, beginning with small amplitudes and increasing. When the training amplitude is reached, the shape of the contact line is recovered. However, when the amplitude iteration exceeds that of the training amplitude, the memory is erased and the steady state is lost. This behavior is reminiscent of return-point memory, a phenomenon best known in ferromagnets. Return-point memory, and the process of reaching a steady state, can provide insight on pinning sites, energy barriers, and dynamics of the contact line, and offers a new framework for manipulating its shape. |
Wednesday, March 16, 2022 10:00AM - 10:12AM |
M21.00011: Exploring the relationship between excess entropy and machine learned softness in glass-forming systems Ian R Graham, Robert Riggleman, Paulo Arratia Characterizing the structure of disordered systems and connecting it to dynamic properties is a major challenge. A vast number of observables have accumulated in the toolkits of those studying glassy and supercooled systems. One of these familiar quantities is excess entropy, which has long been known to correlate well with the transport-coefficients of liquids at equilibrium. Additionally in recent years, data driven methods have profoundly improved our understanding of the structure-dynamics relationship in glassy system. Notable among these examples is the concept of softness, a machine learning model associated with the particles’ susceptability to rearrange based solely upon its static structure. Are these two quantities, softness and excess entropy, correlated? To answer this question, we analyze standard Lennard-Jones style glass formers in the supercooled regime. We find a strong relationship between the excess entropy calculated both locally and globally and the empirical entropic barriers to rearrangement extracted from softness. |
Wednesday, March 16, 2022 10:12AM - 10:24AM |
M21.00012: Beyond Quality and Quantity: Contact Distribution Encodes Frictional Strength Sam J Dillavou, Yohai Bar-Sinai, Michael P Brenner, Shmuel M Rubinstein The quantity of real contact between two bodies is classically considered a proxy for frictional resistance. It has been shown more recently that bond density between the two bodes, often called quality of contact, is also a highly relevant factor. Contemporary debate therefore often revolves around the relative importance of these two factors, often completely ignoring the distribution of contact. In this work we take static friction measurements and image the contact plane, then use the contact distribution to predict static friction 5 to 20 times better than existing benchmarks (e.g. total area of contact). Our model has no access to quality of contact, and we therefore conclude that a large portion of the interfacial state is encoded in the spatial distribution of contact, rather than its quality or quantity. |
Wednesday, March 16, 2022 10:24AM - 10:36AM |
M21.00013: Consistency of dynamic heterogeneities characterized by X-ray experiments and MD simulations Ryoichi Yamamoto, Kousei Kanayama, Taiki Hoshino To understand the origin of slow dynamics near the glass transition, "dynamic heterogeneity" plays a crucial role. In the previous studies, quantitative evaluation of dynamical heterogeneity has been attempted from analyzing speckle patterns in X-ray scattering experiments or from four-body correlation functions in molecular dynamics (MD) simulations. However, the physical relationship between those observed dynamic heterogeneities has not been clarified. This study proposes a connection between the dynamic heterogeneities characterized by the speckle pattern and those followed by the four-body correlation function. The validity of the relationship has also been clarified. |
Wednesday, March 16, 2022 10:36AM - 10:48AM |
M21.00014: Automated detection of spatial and dynamical heterogeneity of nano-domains in supercooled liquids via implementation of Machine Learning Algorithms Viet T Nguyen Understanding the physics of supercooled liquids near glassy transition remains one of the major challenges in condensed matter science. There has been long recognized that both dynamical and spatial structures in supercooled liquids are heterogeneous. As liquid is cooled far below its melting point fast, dynamics in some regions of the sample can be orders of magnitude faster than the dynamics in other regions only a few nanometers away. However, to identify such domain structures and the connection between structures and dynamics remains elusive. We developed a theoretical approach via implementation of Principle Component Analysis (PCA) and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify domain structures of a supercooled Kob-Andersen binary Lennard-Jones liquid. In our approach, raw features data are collected from the coordination numbers of particles smoothed using radial distribution function and are used as an order-parameter for training GM clustering after dimensionality reduction from the PCA. To transfer the knowledge from features space to real space, another GM clustering is performed using the Cartesian coordinates as an order-parameter with the initial values from GM in features space. Both GM clustering are performed iteratively until convergence. Final results show the appearance of aggregated clusters of nano-domains over sufficient long timescale with heterogeneous dynamics. More importantly, from these studies we consistently observe nano-domain structures as a function of temperature regardless of finite size effect and our approach can be broadly applied to more complex systems of interest. |
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