Microscopic Dynamics Research Articles

From creep to flow: Granular materials under cyclic shear

When unperturbed, granular materials form stable structures that resemble the ones of other amorphous solids like metallic or colloidal glasses. Whether or not granular materials under shear have an elastic response is not known, and also the influence of particle surface roughness on the yielding transition has so far remained elusive. Here we use X-ray tomography to determine the three-dimensional microscopic dynamics of two granular systems that have different roughness and that are driven by cyclic shear. Both systems, and for all shear amplitudes Γ considered, show a cross-over from creep to diffusive dynamics, indicating that rough granular materials have no elastic response and always yield, in stark contrast to simple glasses. For the system with small roughness, we observe a clear dynamic change at Γ ≈ 0.1, accompanied by a pronounced slowing down and dynamical heterogeneity. For the large roughness system, the dynamics evolves instead continuously as a function of Γ. We rationalize this roughness dependence using the potential energy landscape of the systems: The roughness induces to this landscape a micro-corrugation with a new length scale, whose ratio over the particle size is the relevant parameter. Our results reveal the unexpected richness in relaxation mechanisms for real granular materials.

Microscopic Morphology and Dynamics of Polyampholyte and Cationic Ionomers

Microscopic Morphology and Dynamics of Polyampholyte and Cationic Ionomers

Dynamics of Linkers in Metal-Organic Framework Glasses.

Metal-organic framework (MOF) glasses have emerged as a new class of organic-inorganic hybrid glass materials. Considerable efforts have been devoted to unraveling the macroscopic dynamics of MOF glasses by studying their rheological behavior; however, their microscopic dynamics remain unclear. In this work, we studied the effect of vitrification on linker dynamics in ZIF-62 by solid-state 2H nuclear magnetic resonance (NMR) spectroscopy. 2H NMR relaxation analysis provided a detailed picture of the mobility of the ZIF-62 linkers, including local restricted librations and a large-amplitude twist; these details were verified by molecular dynamics. A comparison of ZIF-62 crystals and glasses revealed that vitrification does not drastically affect the fast individual flipping motions with large-amplitude twists, whereas it facilitates slow cooperative large-amplitude twist motions with a decrease in the activation barrier. These observations support the findings of previous studies, indicating that glassy ZIF-62 retains permanent porosity and that short-range disorder exists in the alignment of ligands because of distortion of the coordination angle.

Biased motility-induced phase separation: from chemotaxis to traffic jams

We propose a one-dimensional model of active particles interpolating between quorum sensing models used in the study of motility-induced phase separation (MIPS) and models of congestion of traffic flow on a single-lane highway. Particles have a target velocity with a density-dependent magnitude and a direction that flips with a finite rate that is biased toward moving right. Two key parameters are the bias and the speed relaxation time. MIPS is known to occur in such models at zero bias and zero relaxation time (overdamped dynamics), while a fully biased motion with no velocity reversal models traffic flow on a highway. Using both numerical simulations and continuum equations derived from the microscopic dynamics, we show that a single phase-separated state extends from the usual MIPS to congested traffic flow in the phase diagram defined by the bias and the speed relaxation time. However, in the fully biased case, inertia is essential to observe phase separation, making MIPS and congested traffic flow seemingly different phenomena if not simultaneously considering inertia and tumbling. We characterize the velocity of the dense phase, which is static for usual MIPS and moves backward in traffic congestion. We also find that in presence of bias, the phase diagram becomes richer, with an additional transition between phase separation and a microphase separation that is seen above a threshold bias or relaxation rate.

Reimagining and reinterpreting Cooper pairs, the Fermi sea, Pauli blocking and superfluidity: The Pauli principle in collective motion

Abstract Identifying possible microscopic mechanisms underlying superfluidity has been the goal of various studies since the introduction of the original BCS theory. Recently a series of papers have proposed microscopic dynamics based on normal modes to describe superfluidity without the use of real-space Cooper pairs. Multiple properties were determined with excellent agreement with experimental data. The group theoretic basis of this general N-body approach has allowed the microscopic behavior underlying these results to be analyzed in detail. This reimagination is now used to reinterpret several interrelated phenomena including Cooper pairs, the Fermi sea, and Pauli blocking. This approach adheres closely to the early tenets of superconductivity/superfluidity which assumed pairing only in momentum space, not in real space. The Pauli principle is used, in its recently revealed role in collective motion, to select the allowed normal modes. The expected properties of superfluidity including the rigidity of the wave function, interactions between the fermions in different pairs, convergence of the momentum and the gap in the excitation spectrum are discussed.

Particle traps and stationary currents captured by an active 1D model

We investigate the onset of a non-equilibrium phase transition in a one-dimensional ring, constituted by two urns connected by two strands, called active and passive channels. A set of N particles move inside the ring with constant individual speeds; collisions against the channel entries produce reflections with certain probabilities, that differ between active and passive channels. The microscopic dynamics differs from a classical 1D billiard owing to the presence of an interaction mechanism acting inside the active channel, which potentially reverses velocities of its particles. We outline a general theory for the feedback-controlled system which describes quantitatively the phase diagram of the model, based on a mixing property, that is analytically predicted and numerically verified. The probability distributions we define and evolve in time are 1D projections of uniform distributions on d-dimensional spherical surfaces, with d≥1 and d=∞. Consequently results that apply to higher dimensional systems are recovered.

Microscopic dynamics of gas molecules confined in porous channel-like ice structure

Microscopic dynamics of gas molecules confined in porous channel-like ice structure

Milestoning estimators of dissipation in systems observed at a coarse resolution

Many nonequilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases, the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycles. As a result, the observations appear less irreversible, and it is very challenging to infer the degree of broken time-reversal symmetry. Here we show, contrary to intuition, that by ignoring parts of the already coarse-grained state space we may-via a process called milestoning-improve entropy-production estimates. We present diverse examples where milestoning systematically renders observations "closer to underlying microscopic dynamics" and thereby improves thermodynamic inference from lumped data assuming a given range of memory, and we hypothesize that this effect is quite general. Moreover, whereas the correct general physical definition of time reversal in the presence of memory remains unknown, we here show by means of physically relevant examples that at least for semi-Markov processes of first and second order, waiting-time contributions arising from adopting a naive Markovian definition of time reversal generally must be discarded.

A novel Voronoi-based convolutional neural network framework for pushing person detection in crowd videos

AbstractAnalyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: (i) feature extraction and (ii) video detection. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings demonstrate that the proposed framework outperforms state-of-the-art approaches, as well as seven baseline methods used for comparative analysis.

Uncertainty Analysis for Ferromagnetic-Paramagnetic Phase Transition Behavior of Magnetic Materials

AbstractThis study aims to model the phase transition of ferromagnetic materials using two- (2D) and three-dimensional (3D) Ising models, incorporating long-range magnetic spin-to-spin interactions and the influence of an external magnetic field. The 2D Ising model is investigated mainly for a $$4\times 4$$ 4 × 4 domain, while its extension to larger domains is also explored. For the 3D Ising model, the selection of representative volume element is discussed in terms of free energy. An uncertainty quantification formulation is integrated into the Ising model to capture the uncertainties related to the external magnetic field and evaluate their effects on the phase transition from a ferromagnetic to a paramagnetic state. Despite the common use and known effectiveness of the Ising model, there is still no universally accepted exact solution for the 3D domains, making it an ongoing research focus. The research also acknowledges the complexities and limitations arising from experimental methods, despite the insights they provide on the microscopic dynamics of ferromagnetic materials during phase transitions.

Lack-of-fit reduction in non-equilibrium thermodynamics applied to the Kac–Zwanzig model

Abstract Even when microscopic particle dynamics is purely mechanistic and thus reversible, the behavior of macroscopic systems composed of those particles is irreversible. In other words, effectively irreversible behavior emerges out of purely reversible dynamics when we do not observe all degrees of freedom of the detailed dynamics. But how can we find the irreversible macroscopic evolution equations when we only know the reversible microscopic equations? Using the so-called lack-of-fit reduction, which gives the reduced evolution as a sum of Hamiltonian and gradient dynamics, we reduce the purely Hamiltonian Kac–Zwanzig model to a set of irreversible evolution equations with no fitting parameters.

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms.

In power electronics systems, system design and operation often involve multiple time and space scales, ranging from nanosecond switching dynamics to hour-level system operation behavior. Due to the complexity of these systems and the rise of wide-gap semiconductor technology, a series of multi-scale phenomena have emerged that are difficult to ignore. The high frequency of switching operations makes multi-scale effects particularly significant, including the fast dynamic response of the power loop, EMI, and heat conduction problems. They are key factors that must be considered in the design to ensure the efficient and reliable operation of power electronic devices. This study proposes the construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms to address the multi-scale phenomenon and limitations of single-scale power electronic converters. Firstly, a joint scale model for power electronic converters at both macro and micro-scales was established, targeting both single-scale models and simple combinations of multiple scale models for power electronic converters. The traditional single-scale model is sufficient to describe the average behavior of the converter, but it has serious limitations in capturing fast transient processes and high-frequency switching behavior in power electronic systems. These limitations often manifest themselves when there is a need to capture fine timescales of detail. By transforming between the time domain and the frequency domain, wavelet decomposition enables the model to capture both macroscopic average characteristics and microscopic transient dynamics. The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. Secondly, this algorithm is used to analyze BC in short time scale. Finally, the short time scale characteristics of power electronic converters are analyzed. Experimental results show that the fusion of wavelet decomposition and reconstruction algorithm enhances the accuracy of the power electronic converter model and improves the performance of the system. The model achieves an error reduction of nearly 3% in the calculation step size of 10-7s, which has a significant impact on the high precision requirements of high-frequency operations. In addition, the optimal calculation step size of 8×10-8s achieves an error reduction of more than 14%, making an important contribution to the transient analysis and fine structure simulation. The wavelet algorithm can improve the accuracy of multi-scale modeling in power electronic system and reduce the simulation time. The reduction of error not only shows the improvement of the accuracy of the model, but also shows its practical significance in the design and test of the actual power electronic system. The reduction in error reveals the ability to more accurately predict and mitigate potential performance problems in matching tests with actual hardware, as well as its ability to adapt to emerging wide bandgap semiconductor materials and structures.

An Alternative Explanation of the Microscopic Dynamics of Cyclic Polymers

AbstractAccording to recent reviews and experiments, some key dynamic properties of cyclic polymers from neutron spin echo spectroscopy, molecular dynamics simulations, and rheological measurements are at variance with the predictions from theories based on motions restricted by fixed obstacles. These dynamic properties including non‐Gaussianity, heterogeneity, and subdiffusive center of mass mean square displacements turn out to be hallmarks of cooperative dynamics found in entangled linear polymers, and in other many‐units interacting systems that are not polymers. The current situation suggests new theory emphasizing cooperative many‐chain dynamics is needed to explain the properties. The Coupling Model is such a theory. Its predictions are applied to the dynamic properties of cyclic polymers in this paper to show consistency with experiments and simulations.This article is protected by copyright. All rights reserved

Ab initio generalized Langevin equation

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

Microscopic derivation of Vlasov–Dirac–Benney equation with short-range pair potentials

We present a probabilistic proof of the mean-field limit and propagation of chaos of a N-particle system in three dimensions with pair potentials of the form N3β−1ϕ(Nβx) for β∈[0,17] and ϕ∈ L∞(R3)∩L1(R3) . In particular, for typical initial data, we show convergence of the Newtonian trajectories to the characteristics of the Vlasov–Dirac–Benney system with delta-like interactions. The proof is based on a Gronwall estimate for the maximal distance between the exact microscopic dynamics and the approximate mean-field dynamics. Thus our result leads to a derivation of the Vlasov–Dirac–Benney equation from the microscopic N-particle dynamics with a strong short range force.

Amorphous-Like Ultralow Thermal Transport in Crystalline Argyrodite Cu7PS6.

Due to their amorphous-like ultralow lattice thermal conductivity both below and above the superionic phase transition, crystalline Cu- and Ag-based superionic argyrodites have garnered widespread attention as promising thermoelectric materials. However, despite their intriguing properties, quantifying their lattice thermal conductivities and a comprehensive understanding of the microscopic dynamics that drive these extraordinary properties are still lacking. Here, an integrated experimental and theoretical approach is adopted to reveal the presence of Cu-dominated low-energy optical phonons in the Cu-based argyrodite Cu7PS6. These phonons yield strong acoustic-optical phonon scattering through avoided crossing, enabling ultralow lattice thermal conductivity. The Unified Theory of thermal transport is employed to analyze heat conduction and successfully reproduce the experimental amorphous-like ultralow lattice thermal conductivities, ranging from 0.43 to 0.58Wm-1 K-1, in the temperature range of 100-400 K. The study reveals that the amorphous-like ultralow thermal conductivity of Cu7PS6 stems from a significantly dominant wave-like conduction mechanism. Moreover, the simulations elucidate the wave-like thermal transport mainly results from the contribution of Cu-associated low-energy overlapping optical phonons. This study highlights the crucial role of low-energy and overlapping optical modes in facilitating amorphous-like ultralow thermal transport, providing a thorough understanding of the underlying complex dynamics of argyrodites.

Structure-Property Relationships of Granular Hybrid Hydrogels Formed through Polyelectrolyte Complexation.

Hybrid hydrogels are hydrogels that exhibit heterogeneity in the network architecture by means of chemical composition and/or microstructure. The different types of interactions, together with structural heterogeneity, which can be created on different length scales, determine the mechanical properties of the final material to a large extent. In this work, the microstructure-mechanical property relationships for a hybrid hydrogel that contains both electrostatic and covalent interactions are investigated. The hybrid hydrogel is composed of a microphase-separated polyelectrolyte complex network (PEC) made of poly(4-styrenesulfonate) and poly(diallyldimethylammonium chloride) within a soft and elastic polyacrylamide hydrogel network. The system exhibits a granular structure, which is attributed to the liquid-liquid phase separation into complex coacervate droplets induced by the polymerization and the subsequent crowding effect of the polyacrylamide chains. The coacervate droplets are further hardened into PEC granules upon desalting the hydrogel. The structure formation is confirmed by a combination of electron microscopic imaging and molecular dynamics simulations. The interpenetration of both networks is shown to enhance the toughness of the resulting hydrogels due to the dissipative behavior of the PEC through the rupture of electrostatic interactions. Upon cyclic loading-unloading, the hydrogels show recovery of up to 80% of their original dissipative behavior in less than 300 s of rest with limited plasticity. The granular architecture and the tough and self-recoverable properties of the designed hybrid networks make them good candidates for applications, such as shape-memory materials, actuators, biological tissue mimics, and elastic substrates for soft sensors.

Dynamics of electron-hole pairs in interface exciplex OLEDs investigated by magnetic field effects

Interface exciplex organic light-emitting diodes (OLEDs) have attracted intense attention due to the advantages including well-confined recombination regions, barrier-free charge transport and thermally activated delayed fluorescence (TADF) characteristics. To investigate the spin evolutions and dynamics of electron-hole (e-h) pairs, such as polaron pairs (PPs) and charge transfer (CT) excitons, in interface exciplex OLEDs is crucial for a better understanding on their energy gain and loss mechanisms. In this work, microscopic dynamics of e-h pairs in interface exciplex OLEDs were investigated by magneto-electroluminescence (MEL) and magneto-conductivity (MC) responses. The interface exciplex OLEDs were fabricated using 4,4,4-tris(N-3-methylphenyl-N-phenylamino) triphenylamine (m-MTDATA) as the donor and 2,4,6-tris(biphenyl-3-yl)-1,3,5-triazine (T2T) as the acceptor. Hyperfine interaction (HFI)- and different g-factors between electrons and holes (Δg mechanism)-dominated spin flip processes, and recombination and dissociation processes such as triplet-triplet annihilation (TTA) and triplet-charge annihilation (TQA) were identified, and their changes with temperature and current were explored. Our results may provide valuable insights into the evolution of carriers and facilitate the development of interface exciplex-based OLEDs.

Mechanistic understanding of enhanced thermal stability of twinned copper nanowires

Thermal instability problems of nanostructured materials, including grain coarsening and recrystallization, can be alleviated by introducing nanoscale coherent twin boundary (CTB) structures. However, the detailed interaction mechanism between CTB and grain boundaries (GBs) during microstructural transformation has not been well elucidated. To gain insight, we investigate the migration behavior of CTB-intercepted GBs in twinned copper nanowires at elevated temperatures through transmission electron microscopic observations and molecular dynamics simulations. The results show that numerous partial dislocations are conveyed to the CTB-intercepted GB through the densely distributed CTBs and further transferred to the nanowire surface along the GB. The growth of the twinned region along the diminishing GB is driven by minimizing the excess energy stored in dislocation defects and GBs. This CTB-mediated GB migration in twinned copper nanowires can be deactivated by surface passivation. This study will help to elucidate how the thermal stability of nanostructured metals is influenced by the twin structure and surface passivation.

Dynamics of Formamide-Water Mixtures Investigated by Linear and Nonlinear Infrared Spectroscopy.

Formamide (FA) exhibits complete miscibility with water, offering a simplified model for exploring the solvation dynamics of peptide linkages in biophysical processes. Its liquid state demonstrates a three-dimensional hydrogen bonding network akin to water, reflecting solvent-like behavior. Analyzing the microscopic structure and dynamics of FA-water mixtures is expected to provide crucial insights into hydrogen bonding dynamics─a key aspect of various biophysical phenomena. This study is focused on the dynamics of FA-water mixtures using linear and femtosecond infrared spectroscopies. By using the intrinsic OD stretch and extrinsic probe SCN-, the local vibrational behaviors across various FA-water compositions were systematically investigated. The vibrational relaxation of OD stretch revealed a negligible impact of FA addition on the vibrational lifetime of water molecules, underscoring the mixture's water-like behavior. However, the reorientational dynamics of OD stretch slowed with increasing FA mole fraction (XFA), plateauing beyond XFA > 0.5. This suggests a correlation between OD's reorientational time and the strength of the hydrogen bond network, likely tied to the solution's changing dielectric constant. Conversely, the vibrational relaxation dynamics of SCN- was strongly correlated with XFA, highlighting a competition between water and FA molecules in solvating SCN-. Moreover, a linear relationship between rising viscosity and the prolonged correlation time of SCN-'s slow dynamics indicates that the solution's macroscopic viscosity is dictated by the extended structures formed between FA and water molecules. The relation between the reorientation dynamics of the SCN- and the macroscopic viscosity in aqueous FA-water mixture solutions was analyzed by using the Stokes-Einstein-Debye equations. The direct viscosity-diffusion coupling is observed, which can be attributed to the homogeneous dynamics feature in FA-water mixture solutions. The inclusion of these intrinsic and extrinsic probes not only enhances the comprehensiveness of our analysis but also provides valuable insights into various aspects of the dynamics within the FA-water system. This investigation sheds light on the fundamental dynamics of FA-water mixtures, emphasizing their molecular-level homogeneity in this binary mixture solution.