Heterogeneous Activation, Local Structure, and Softness in Supercooled Colloidal Liquids

Unraveling the dynamic slowdown in supercooled water: The role of dynamic disorder in jump motions

When a liquid is rapidly cooled below its melting point without inducing crystallization, its dynamics slow down significantly without noticeable structural changes. Elucidating the origin of this slowdown has been a long-standing challenge. Here, we report a theoretical investigation into the mechanism of the dynamic slowdown in supercooled water, a ubiquitous yet extraordinary substance characterized by various anomalous properties arising from local density fluctuations. Using molecular dynamics simulations, we found that the jump dynamics, which are elementary structural change processes, deviate from Poisson statistics with decreasing temperature. This deviation is attributed to slow variables competing with the jump motions, i.e., dynamic disorder. The present analysis of the dynamic disorder showed that the primary slow variable is the displacement of the fourth nearest oxygen atom of a jumping molecule, which occurs in an environment created by the fluctuations of molecules outside the first hydration shell. As the temperature decreases, the jump dynamics become slow and intermittent. These intermittent dynamics are attributed to the prolonged trapping of jumping molecules within extended and stable low-density domains. As the temperature continues to decrease, the number of slow variables increases due to the increased cooperative motions. Consequently, the jump dynamics proceed in a higher-dimensional space consisting of multiple slow variables, becoming slower and more intermittent. It is then conceivable that with further decreasing temperature, the slowing and intermittency of the jump dynamics intensify, eventually culminating in a glass transition.

A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

Modeling the dynamics of glassy systems has been challenging in physics for several decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in capturing particle dynamics from the graph structure of glassy systems. However, current GNN methods do not take the dynamic patterns established by neighboring particles explicitly into account. In contrast to these approaches, this paper introduces a novel dynamical parameter termed "smoothness" based on the theory of graph signal processing, which explores the dynamic patterns from a graph perspective. Present graph-based approaches encode structural features without considering smoothness constraints, leading to a weakened correlation between structure and dynamics, particularly on short timescales. To address this limitation, we propose a Geometry-enhanced Graph Neural Network (Geo-GNN) to learn the smoothness of dynamics. Results demonstrate that our method outperforms state-of-the-art baselines in predicting glassy dynamics. Ablation studies validate the effectiveness of each proposed component in capturing smoothness within dynamics. These findings contribute to a deeper understanding of the interplay between glassy dynamics and static structure.

Orientational Order in Dense Colloidal Liquids and Glasses

We construct structural order parameters based on local angular and radial distribution functions in dense colloidal suspensions. All the order parameters show significant correlations to local dynamics in the supercooled and glass regime. In particular, the correlations between the orientational order and dynamical heterogeneity are consistently higher than those between the conventional two-body structural entropy and local dynamics. The structure-dynamics correlations can be explained by a excitation model with the energy barrier depending on local structural order. Our results suggest that in dense disordered packings, local orientational order is higher than translational order, and plays a more important role in determining the dynamics in glassy systems.

Depletion-driven antiferromagnetic, paramagnetic, and ferromagnetic behavior in quasi-two-dimensional buckled colloidal solids

We investigate quasi-two-dimensional buckled colloidal monolayers on a triangular lattice with tunable depletion interactions. Without depletion attraction, the experimental system provides a colloidal analog of the well-known geometrically frustrated Ising antiferromagnet [Y. Han et al., Nature 456, 898-903 (2008)]. In this contribution, we show that the added depletion attraction can influence both the magnitude and sign of an Ising spin coupling constant. As a result, the nearest-neighbor Ising "spin" interactions can be made to vary from antiferromagnetic to para- and ferromagnetic. Using a simple theory, we compute an effective Ising nearest-neighbor coupling constant, and we show how competition between entropic effects permits for the modification of the coupling constant. We then experimentally demonstrate depletion-induced modification of the coupling constant, including its sign, and other behaviors. Depletion interactions are induced by rod-like surfactant micelles that change length with temperature and thus offer means for tuning the depletion attraction in situ. Buckled colloidal suspensions exhibit a crossover from an Ising antiferromagnetic to paramagnetic phase as a function of increasing depletion attraction. Additional dynamical experiments reveal structural arrest in various regimes of the coupling-constant, driven by different mechanisms. In total, this work introduces novel colloidal matter with "magnetic" features and complex dynamics rarely observed in traditional spin systems.

Fragility in glassy liquids: A structural approach based on machine learning

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.

Structural origin of excitations in a colloidal glass-former

Despite decades of intense research, whether the transformation of supercooled liquids into glass is a kinetic phenomenon or a thermodynamic phase transition remains unknown. Here, we analyzed optical microscopy experiments on 2D binary colloidal glass-forming liquids and investigated the structural links of a prominent kinetic theory of glass transition. We examined a possible structural origin for localized excitations, which are building blocks of the dynamical facilitation theory—a purely kinetic approach for the glass transition. To accomplish this, we utilize machine learning methods to identify a structural order parameter termed “softness” that has been found to be correlated with reorganization events in supercooled liquids. Both excitations and softness qualitatively capture the dynamical slowdown on approaching the glass transition and motivated us to explore spatial and temporal correlations between them. Our results show that excitations predominantly occur in regions with high softness and the appearance of these high softness regions precedes excitations, thus suggesting a causal connection between them. Thus, unifying dynamical and thermodynamical theories into a single structure-based framework may provide a route to understand the glass transition.

Are strongly confined colloids good models for two dimensional liquids?

Quasi-two-dimensional (quasi-2D) colloidal hard-sphere suspensions confined in a slit geometry are widely used as two-dimensional (2D) model systems in experiments that probe the glassy relaxation dynamics of 2D systems. However, the question to what extent these quasi-2D systems indeed represent 2D systems is rarely brought up. Here, we use computer simulations that take into account hydrodynamic interactions to show that dense quasi-2D colloidal bi-disperse hard-sphere suspensions exhibit much more rapid diffusion and relaxation than their 2D counterparts at the same area fraction. This difference is induced by the additional vertical space in the quasi-2D samples in which the small colloids can move out of the 2D plane, therefore allowing overlap between particles in the projected trajectories. Surprisingly, this difference in the dynamics can be accounted for if, instead of using the surface density, one characterizes the systems by means of a suitable structural quantity related to the radial distribution function. This implies that in the two geometries, the relevant physics for glass formation is essentially identical. Our results provide not only practical implications on 2D colloidal experiments but also interesting insights into the 3D-to-2D crossover in glass-forming systems.

Correlation of plastic events with local structure in jammed packings across spatial dimensions

SignificanceMean-field theories, exact in the limit of infinite spatial dimensions, succeed in describing many features of glasses and amorphous solids in low dimensions, leading to considerable effort to understand how behavior evolves with dimension. Until now, all evidence has supported a picture in which "localized physics," responsible for deviations from mean-field behavior in low dimensions, fades away with rising dimension. Our work shows that rearrangements, in which particles change relative positions leading to fluid-like response, reveal a different picture of dimensional cross-over. We find that rearrangements, which are localized in two- and three-dimensional systems and correlated with local structure, remain just as correlated with local structure up to five dimensions, suggesting that local structure is important even in high dimensions.

Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

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 nevertheless 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.

Large heat-capacity jump in cooling-heating of fragile glass from kinetic Monte Carlo simulations based on a two-state picture

The specific-heat capacity c_{v} of glass formers undergoes a hysteresis when subjected to a cooling-heating cycle, with a larger c_{v} and a more pronounced hysteresis for fragile glasses than for strong ones. Here we show that these experimental features, including the unusually large magnitude of c_{v} of fragile glasses, are well reproduced by kinetic Monte Carlo and equilibrium study of a distinguishable particle lattice model incorporating a two-state picture of particle interactions. The large c_{v} in fragile glasses is caused by a dramatic transfer of probabilistic weight from high-energy particle interactions to low-energy ones as temperature decreases.

Glassy dynamics of sticky hard spheres beyond the mode-coupling regime

Sticky hard spheres, i.e., hard particles decorated with a short-ranged attractive interaction potential, constitute a relatively simple model with highly non-trivial glassy dynamics. The mode-coupling theory of the glass transition (MCT) offers a qualitative account of the complex reentrant dynamics of sticky hard spheres, but the predicted glass transition point is notoriously underestimated. Here we apply an improved first-principles-based theory, referred to as generalized mode-coupling theory (GMCT), to sticky hard spheres. This theoretical framework seeks to go beyond MCT by hierarchically expanding the dynamics in higher-order density correlation functions. We predict the phase diagrams from the first few levels of the GMCT hierarchy and the dynamics-related critical exponents, all of which are much closer to the empirical observations than MCT. Notably, the prominent reentrant glassy dynamics, the glass-glass transition, and the higher-order bifurcation singularity classes (A₃ and A₄) of sticky hard spheres are found to be preserved within GMCT at arbitrary order. Moreover, we demonstrate that when the hierarchical order of GMCT increases, the effect of the short-ranged attractive interactions becomes more evident in the dynamics. This implies that GMCT is more sensitive to subtle microstructural differences than MCT, and that the framework provides a promising first-principles approach to systematically go beyond the MCT regime.

Structural and short-time vibrational properties of colloidal glasses and supercooled liquids in the vicinity of the re-entrant glass transition

We investigate the short-time vibrational properties and structure of two-dimensional, bidisperse, colloidal glasses and supercooled liquids in the vicinity of the re-entrant glass transition, as a function of interparticle depletion attraction strength. The long-time spatiotemporal dynamics of the samples are measured to be non-monotonic, confirming that the suspensions evolve from repulsive glass to supercooled liquid to attractive glass with increasing depletion attraction. Here, we search for vibrational signatures of the re-entrant behavior in the short-time spatiotemporal dynamics, i.e., dynamics associated with particle motion inside its nearest-neighbor cage. Interestingly, we observe that the anharmonicity of these in-cage vibrations varies non-monotonically with increasing attraction strength, consistent with the non-monotonic long-time structural relaxation dynamics of the re-entrant glass. We also extract effective spring constants between neighboring particles; we find that spring stiffness involving small particles also varies non-monotonically with increasing attraction strength, while stiffness between large particles increases monotonically. Last, from study of depletion-dependent local structure and vibration participation fractions, we gain microscopic insight into the particle-size-dependent contributions to short-time vibrational modes in the glass and supercooled liquid states.

Characterising soft matter using machine learning

Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data.

Microrheology of thermoresponsive poly(N-isopropylacrylamide) microgel dispersions near a substrate surface

HYPOTHESIS

Relative to the bulk systems, the near-wall (<500 nm) rheological responses of soft poly(N-isopropylacrylamide) (PNIPAM) microgel dispersions may exhibit distinct dependence on the frequency (ω), temperature (T), and effective volume fraction (ϕ_eff) during the volume phase transitions. The microrheological behaviors are expected to be governed by the near-wall microstructure and its spatial heterogeneity.

EXPERIMENTS

The combination of active microrheometry (multipole magnetic tweezers) and total internal reflection microscopy (TIRM) was employed to probe the structure-rheology relationships of microgel dispersions near a substrate surface. The ω, T, and ϕ_eff-dependences of the storage modulus (G'), loss modulus (G"), and softness (J) were analyzed by power-law and Arrhenius-like scaling theories. The fluctuation of J was further analyzed to give a quantitative description of the inhomogeneity in the near-wall regions.

FINDINGS

(1) Remarkable differences in the rheological behaviors between the bulk and near-wall cases are revealed, where the latter shows a segmented overlap behavior in ϕ_eff; (2) Five regimes of ϕ_eff that correspond to distinct physical states of the microgel dispersions are determined; (3) The near-wall local structures exhibit more heterogeneity in the glass and colloidal gel regimes as compared to the liquid regime.

Absence of crystals in the phase behavior of hollow microgels

Solutions of microgels have been widely used as model systems to gain insight into atomic condensed matter and complex fluids. We explore the thermodynamic phase behavior of hollow microgels, which are distinguished from conventional colloids by a central cavity. Small-angle neutron and x-ray scattering are used to probe hollow microgels in crowded environments. These measurements reveal an interplay among deswelling, interpenetration, and faceting and an unusual absence of crystals. Monte Carlo simulations of model systems confirm that, due to the cavity, solutions of hollow microgels more readily form a supercooled liquid than for microgels with a cross-linked core.

Inferring the particle-wise dynamics of amorphous solids from the local structure at the jamming point

Jamming is a phenomenon shared by a wide variety of systems, such as granular materials, foams, and glasses in their high density regime. This has motivated the development of a theoretical framework capable of explaining many of their static critical properties with a unified approach. However, the dynamics occurring in the vicinity of the jamming point has received little attention and the problem of finding a connection with the local structure of the configuration remains unexplored. Here we address this issue by constructing physically well defined structural variables using the information contained in the network of contacts of jammed configurations, and then showing that such variables yield a resilient statistical description of the particle-wise dynamics near this critical point. Our results are based on extensive numerical simulations of systems of spherical particles that allow us to statistically characterize the trajectories of individual particles in terms of their first two moments. We first demonstrate that, besides displaying a broad distribution of mobilities, particles may also have preferential directions of motion. Next, we associate each of these features with a structural variable computed uniquely in terms of the contact vectors at jamming, obtaining considerably high statistical correlations. The robustness of our approach is confirmed by testing two types of dynamical protocols, namely molecular dynamics and Monte Carlo, with different types of interaction. We also provide evidence that the dynamical regime we study here is dominated by anharmonic effects and therefore it cannot be described properly in terms of vibrational modes. Finally, we show that correlations decay slowly and in an interaction-independent fashion, suggesting a universal rate of information loss.

Phase behavior of ultrasoft spheres show stable bcc lattices

The phase behavior of supersoft spheres is explored using solutions of ultralow cross-linked poly(N-isopropylacrylamide)-based microgels as a model system. For these microgels, the effects of the electric charges on their surfaces can be neglected and therefore only the role of softness on the phase behavior is investigated. The samples show a liquid-to-crystal transition at higher volume fraction with respect to both hard spheres and stiffer microgels. Furthermore, stable body centered cubic (bcc) crystals are observed in addition to the expected face centered cubic (fcc) crystals. Small-angle x-ray and neutron scattering with contrast variation allow the characterization of both the microgel-to-microgel distance and the architecture of single microgels in crowded solutions. The measurements reveal that the stable bcc crystals depend on the interplay between the collapse and the interpenetration of the external shell of the ultralow cross-linked microgels.

Non-Markovian Methods in Glass Transition

A model for the heterogeneity of local dynamics in polymer and other glass-forming materials is provided here. The fundamental characteristics of the glass transition phenomenology emerge when simulating a condensed matter open cluster that has a strong interaction with its heterogeneous environment. General glass transition features, such as non-exponential structural relaxations, the slowing down of relaxation times with temperature and specific off-equilibrium glassy dynamics can be reproduced by non-Markovian dynamics simulations with the minimum computer resources. Non-Markovian models are shown to be useful tools for obtaining insights into the complex dynamics involved in the glass transition phenomenon, including whether or not there is a need for a growing correlation length or the relationship between the non-exponentiality of structural relaxations and dynamic heterogeneity.

Observation of soft glassy behavior in a magnetic colloid exposed to an external magnetic field

We provide the first experimental evidence for soft glassy behavior in a sterically stabilized magnetic colloid (ferrofluid) of relatively low volume fraction (φ = 0.037) when a uniform magnetic field is applied at a sufficiently high rate (fast quench). Fast magnetic-field quenches favor structural arrest of field-induced aggregates, owing to insufficient time to settle into lower energy states, thereby pushing the system to a frustrated metastable configuration like a repulsive glass. Brownian dynamics simulations are used to show that the polydisperse ferrofluid (as in experiments) forms thick ropes aligned along the field direction, while a monodisperse ferrofluid does not. The simulations show that there is practically no ordering of the thin, monodisperse chains, while the thick, polydisperse ropes show positional ordering with a typical center-center separation between the particles in different ropes of about 0.39 μm. As a consequence of structural arrest, the ferrofluid exhibits aging with broken time-translational invariance, a hallmark of glassy dynamics. The superposition of strain and creep compliance curves obtained from rheological measurements at different waiting times in the effective time domain corroborates the soft glassy behavior when exposed to a magnetic field applied at a fast ramp rate.

Nonempirical Free Volume Viscosity Model for Alkane Lubricants under Severe Pressures

Viscosities η and diffusion coefficients D_{s} of linear and branched alkanes at pressure 0<P<0.7  GPa and temperature T=500-600  K are calculated from molecular dynamics simulations. Combining Stokes-Einstein, free volume, and random walk concepts results in an accurate viscosity model for the considered P and T. All model parameters (hydrodynamic radius, random walk step size, and step frequency) are extracted from equilibrium molecular dynamics via microscopic ensemble averages rendering η(P,T) a parameter-free predictor for lubrication simulations.

Deep learning for automated classification and characterization of amorphous materials

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel site environment features that characterize interstice distributions around atoms combined with machine learning (ML) can reliably identify plastic sites in several Cu-Zr compositions. Using only quenched structural information as input, the ML-based plastic probability estimates ("quench-in softness" metric) can identify plastic sites that could activate at high strains, losing predictive power only upon the formation of shear bands. Moreover, we reveal that a quench-in softness model trained on a single composition and quench rate substantially improves upon previous models in generalizing to different compositions and completely different MG systems (Ni₆₂Nb₃₈, Al₉₀Sm₁₀ and Fe₈₀P₂₀). Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in MGs.

Identifying structural signatures of shear banding in model polymer nanopillars

Amorphous solids are critical in the design and production of nanoscale devices, but under strong confinement these materials exhibit changes in their mechanical properties which are not well understood. Phenomenological models explain these properties by postulating an underlying defect structure in these materials but do not detail the microscopic properties of these defects. Using machine learning methods, we identify mesoscale defects that lead to shear banding in model polymer nanopillars well below the glass transition temperature as a function of pillar diameter. Our results show that the primary structural features responsible for shear banding on this scale are fluctuations in the diameter of the pillar. Surprisingly, these fluctuations are quite small compared to the diameter of the pillar, less than half of a particle diameter in size. At intermediate pillar diameters, we find that these fluctuations tend to concentrate along the minor axis of shear band planes. We also see the importance of mean "softness" as a classifier of shear banding grow as a function of pillar diameter. Softness is a new field that characterizes local structure and is highly correlated with particle-level dynamics such that softer particles are more likely to rearrange. This demonstrates that softness, a quantity that relates particle-level structure to dynamics on short time and length scales, can predict large time and length scale phenomena related to material failure.