Coarse-grained Method Research Articles

Molecular Dynamics Simulations of Polymer Grouting Material: Its Mechanical Behavior under Uniaxial Tension, Cyclic Tensile Loading, and Stress Relaxation

At present, polyurethane (PU) has been extensively used as a grouting material in civil engineering. The mechanical properties of PU are the key to achieving the desirable grouting effect. This study presents the research results of the mechanical behavior of PU matrix under tensile, successive cyclic tensile, and stress relaxation at the nanoscale, using the coarse-grained molecular dynamics simulation method. The influences of the number of molecule chains and strain rate on the tensile mechanical properties are discussed, and the tensile deformation mechanism of PU matrix is revealed. The tensile strength of PU matrix is independent of loading path, and after yielding, the strain of PU matrix contains the elastic strain, plastic strain, and viscous strain. In the stress relaxation process, the evolution of the axial stress is mainly caused by the varied van der Waals interactions. The stress relaxation behavior of PU matrix can be described by the viscoelastic model consisting of one elastic element in parallel with one Maxwell element.

A Fine-Grained Modeling Approach for Systolic Array-Based Accelerator

The systolic array provides extremely high efficiency for running matrix multiplication and is one of the mainstream architectures of today’s deep learning accelerators. In order to develop efficient accelerators, people usually employ simulators to make design trade-offs. However, current simulators suffer from coarse-grained modeling methods and ideal assumptions, which limits their ability to describe structural characteristics of systolic arrays. In addition, they do not support the exploration of microarchitecture. This paper presents FG-SIM, a fine-grained modeling approach for evaluating systolic array accelerators by using an event-driven method. FG-SIM can obtain accurate results and provide the best mapping scheme for different workloads due to its fine-grained modeling technique and deny of ideal assumption. Experimental results show that FG-SIM plays a significant role in design trade-offs and outperforms state-of-the-art simulators, with an accuracy of more than 95%.

Micromagnetic Simulations of Clusters of Nanoparticles with Internal Structure: Application to Magnetic Hyperthermia

Micromagnetic simulation results on dynamic hysteresis loops of clusters of iron oxide nanoparticles (NPs) with internal structure composed of nanorods are compared with the widely used macrospin approximation. Such calculations allowing for nanorod-composed NPs is facilitated by a previously developed coarse-graining method based on the renormalization group approach. With a focus on applications to magnetic hyperthermia, we show that magnetostatic interactions improve the heating performance of NPs in chains and triangles, and reduce heating performance in fcc arrangements. Hysteresis loops of triangular and fcc systems of complex NPs are not recovered within the macrospin approximation, especially at smaller interparticle distances. For triangular arrangements, the macrospin approximation predicts that magnetostatic interactions reduce loop area, in contrast to the complex NP case. An investigation of the local hysteresis loops of individual NPs and macrospins in clusters reveals the impact of the geometry of their neighbors on individual versus collective magnetic response, inhomogeneous heating within clusters, and further differences between simulating NPs with internal structure and the use of the macrospin approximation. Capturing the internal physical and magnetic structure of NPs is thus important for some applications.

The cortical spectrum: A robust structural continuum in primate cerebral cortex revealed by histological staining and magnetic resonance imaging.

High-level characterizations of the primate cerebral cortex sit between two extremes: on one end the cortical mantle is seen as a mosaic of structurally and functionally unique areas, and on the other it is seen as a uniform six-layered structure in which functional differences are defined solely by extrinsic connections. Neither of these extremes captures the crucial neuroanatomical finding: that the cortex exhibits systematic gradations in architectonic structure. These gradations have been shown to predict cortico-cortical connectivity, which in turn suggests powerful ways to ground connectomics in anatomical structure, and by extension cortical function. A challenge to widespread use of this concept is the labor-intensive and invasive nature of histological staining, which is the primary means of recognizing anatomical gradations. Here we show that a novel computational analysis technique can provide a coarse-grained picture of cortical variation. For each of 78 cortical areas spanning the entire cortical mantle of the rhesus macaque, we created a high dimensional set of anatomical features derived from captured images of cortical tissue stained for myelin and SMI-32. The method involved semi-automated de-noising of images, and enabled comparison of brain areas without hand-labeling of features such as layer boundaries. We applied multidimensional scaling (MDS) to the dataset to visualize similarity among cortical areas. This analysis shows a systematic variation between weakly laminated (limbic) cortices and sharply laminated (eulaminate) cortices. We call this smooth continuum the “cortical spectrum”. We also show that this spectrum is visible within subsystems of the cortex: the occipital, parietal, temporal, motor, prefrontal, and insular cortices. We compared the MDS-derived spectrum with a spectrum produced using T1- and T2-weighted magnetic resonance imaging (MRI) data derived from macaque, and found close agreement of the two coarse-graining methods. This suggests that T1w/T2w data, routinely obtained in human MRI studies, can serve as an effective proxy for data derived from high-resolution histological methods. More generally, this approach shows that the cortical spectrum is robust to the specific method used to compare cortical areas, and is therefore a powerful tool to understand the principles of organization of the primate cortex.

Global energy spectrum of the general oceanic circulation

Advent of satellite altimetry brought into focus the pervasiveness of mesoscale eddies {{{{{{{bf{{{{{{{{mathcal{O}}}}}}}}}}}}}}}}({100}) km in size, which are the ocean’s analogue of weather systems and are often regarded as the spectral peak of kinetic energy (KE). Yet, understanding of the ocean’s spatial scales has been derived mostly from Fourier analysis in small "representative” regions that cannot capture the vast dynamic range at planetary scales. Here, we use a coarse-graining method to analyze scales much larger than what had been possible before. Spectra spanning over three decades of length-scales reveal the Antarctic Circumpolar Current as the spectral peak of the global extra-tropical circulation, at ≈ 104 km, and a previously unobserved power-law scaling over scales larger than 103 km. A smaller spectral peak exists at ≈ 300 km associated with mesoscales, which, due to their wider spread in wavenumber space, account for more than 50% of resolved surface KE globally. Seasonal cycles of length-scales exhibit a characteristic lag-time of ≈ 40 days per octave of length-scales such that in both hemispheres, KE at 102 km peaks in spring while KE at 103 km peaks in late summer. These results provide a new window for understanding the multiscale oceanic circulation within Earth’s climate system, including the largest planetary scales.

Bottom-up Coarse-Graining: Principles and Perspectives.

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

The applicability of hydrodynamics in heavy ion collisions at sqrt{s_mathrm{NN}} = 2.4–7.7 GeV

To assess the degree of equilibration of the matter created in heavy-ion reactions at low to intermediate beam energies, a hadronic transport approach (SMASH) is employed. By using a coarse-graining method, we compute the energy momentum tensor of the system at fixed time steps and evaluate the degree of isotropy of the diagonal terms and the relative magnitude of the off-diagonal terms. This study focuses mostly on Au+Au collisions in the energy range {sqrt{s}_mathrm{NN}} = 2.4–7.7 GeV, but central collisions of lighter ions like C+C, Ar+KCl and Ag+Ag are considered as well. We find that the conditions concerning local equilibration for a hydrodynamic description are reasonably satisfied in a large portion of the system for a significant amount of time (several fm/c) when considering the average evolution of many events, yet they are rarely fulfilled on an event by event basis. This is relevant for the application of hybrid approaches at low beam energies as they are or will be reached by the HADES experiment at GSI, the future CBM experiment at FAIR as well as the beam energy scan program at RHIC.

Mesoscopic simulations of active nematics.

Coarse-grained, mesoscale simulations are invaluable for studying soft condensed matter because of their ability to model systems in which a background solvent plays a substantial role but is not the primary interest. Such methods generally model passive solvents; however, far-from-equilibrium systems may also be composed of complex solutes suspended in an active fluid. Yet, few coarse-grained simulation methods exist to model an active medium. We introduce an algorithm to simulate active nematics, which builds on multiparticle collision dynamics (MPCD) for passive fluctuating nematohydrodynamics by introducing dipolar activity in the local collision operator. Active nematic MPCD (AN-MPCD) simulations not only exhibit the key characteristics of active nematic turbulence but, as a particle-based algorithm, also reproduce crucial attributes of active particle models. Thus, mesoscopic AN-MPCD is an approach that bridges microscopic and continuum descriptions, allowing simulations of composite active-passive systems.

COGRIMEN: Coarse-Grained Method for Modeling of Membrane Proteins in Implicit Environments.

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.

Physical Insight into the Conditions Required in the Solid-Phase Molecular Self-Assembly of SDS Revealed by Coarse-Grained Molecular Dynamics Simulation.

Molecular self-assembled materials have attracted considerable interest in recent years. As part of the efforts to overcome the shortcoming that the solution-based methods were hardly applicable in preparing bulk macroscopic molecular self-assemblies, Yan [ CCS Chem. 2020, 2, 98-106] developed a strategy of solid-phase molecular self-assembly (SPMSA) that allows scaling up the generation of massive supramolecular films. It is highly desired to understand the physical insight into the SPMSA at a molecular level. Here, in combination with the experimental study, we report molecular dynamics (MD) simulations on the SPMSA of the surfactant sodium dodecyl sulfate (SDS) using a coarse-grained method with the Martini force field model. The MD simulations clearly manifest that a small amount of water is required to endow the SDS molecules with sufficient mobility to self-assemble, and the smaller size of the preassembled SDS particles favors their further fusion into mesophases by reducing the total surface Gibbs free energy, while the smaller interparticle distance decreases the time for the particle fusion. The simulation results agree well with the conditions required in the experiment, confirming that SMPSA is a free-energy-favored process leading to bulk self-assembled materials.

Bridging scales in a multiscale pattern-forming system

Self-organized pattern formation is vital for many biological processes. Reaction-diffusion models have advanced our understanding of how biological systems develop spatial structures, starting from homogeneity. However, biological processes inherently involve multiple spatial and temporal scales and transition from one pattern to another over time, rather than progressing from homogeneity to a pattern. To deal with such multiscale systems, coarse-graining methods are needed that allow the dynamics to be reduced to the relevant degrees of freedom at large scales, but without losing information about the patterns at small scales. Here, we present a semiphenomenological approach which exploits mass conservation in pattern formation, and enables reconstruction of information about patterns from the large-scale dynamics. The basic idea is to partition the domain into distinct regions (coarse grain) and determine instantaneous dispersion relations in each region, which ultimately inform about local pattern-forming instabilities. We illustrate our approach by studying the Min system, a paradigmatic model for protein pattern formation. By performing simulations, we first show that the Min system produces multiscale patterns in a spatially heterogeneous geometry. This prediction is confirmed experimentally by in vitro reconstitution of the Min system. Using a recently developed theoretical framework for mass-conserving reaction-diffusion systems, we show that the spatiotemporal evolution of the total protein densities on large scales reliably predicts the pattern-forming dynamics. Our approach provides an alternative and versatile theoretical framework for complex systems where analytical coarse-graining methods are not applicable, and can, in principle, be applied to a wide range of systems with an underlying conservation law.

Phase Separation in Systems of Interacting Active Brownian Particles

The aim of this paper is to discuss the mathematical modeling of Brownian active particle systems, a recently popular paradigmatic system for self-propelled particles. We present four microscopic models with different types of repulsive interactions between particles and their associated macroscopic models, which are formally obtained using different coarse-graining methods. The macroscopic limits are integro-differential equations for the density in phase space (positions and orientations) of the particles and may include nonlinearities in both the diffusive and advective components. In contrast to passive particles, systems of active particles can undergo phase separation without any attractive interactions, a mechanism known as motility-induced phase separation (MIPS). We explore the onset of such a transition for each model in the parameter space of occupied volume fraction and P\'eclet number via a linear stability analysis and numerical simulations at both the microscopic and macroscopic levels. We establish that one of the models, namely the mean-field model which assumes long-range repulsive interactions, cannot explain the emergence of MIPS. In contrast, MIPS is observed for the remaining three models that assume short-range interactions that localize the interaction terms in space.

A coarse-grained concurrent multiscale method for simulating brittle fracture

This paper proposed a concurrent multiscale method for modeling crack propagation in brittle solids. The method couples the continuum finite element domain with a recently-developed coarse-grained atomistic potential, which has been developed to simulate crack nucleation and propagation within the molecular dynamics domain. The main advantage of this potential is to suppress dislocation nucleation from the crack tip which significantly eases the coupling procedure. In this multiscale framework, complexities like large deformation, crack nucleation and propagation are simulated in the atomistic domain while the rest such as elastic wave propagation and boundary conditions are modeled in the continuum domain. As a result, the atomistic area will be limited around the cracking zone which increases the computational efficiency. Two examples of edge crack propagation and sliding surfaces in contact are presented and compared with the fully atomistic models to verify and check the efficiency of the proposed multiscale method. The comparison shows that the proposed method can accurately capture the crack formation while the computation cost reduces significantly.

HDFEF: A hierarchical and dynamic feature extraction framework for intrusion detection systems

Network intrusion detection plays a vital role in modern cyberspace security systems. Although deep learning has been widely used for automatic feature extraction in intrusion detection, capturing effective feature representations in network flows is still challenging using current methods due to the increasing complexity of real-world network environments. Network flows have a clear hierarchical structure, which has not been fully considered by existing methods. Additionally, most existing methods are coarse-grained methods that only leverage a single packet or network flow. As a result, the maliciousness of an attack cannot be fully reflected, which may lead to unsatisfactory detection performance. To address the aforementioned issues, we propose a novel network intrusion detection method based on a hierarchical and dynamic feature extraction framework (HDFEF). Specifically, a complete network activity is defined as a sequence of packets with multiple network flows. Then, a hierarchical network model, which dynamically adjusts the distribution of the feature representations of multiple temporally correlated network packets with an attention mechanism, is designed. Finally, after combining the vectors obtained from the multispace mapping, the final discriminant vectors are obtained and used for classification. The superiority of our HDFEF over other state-of-the-art methods is shown through the results of the experiments on the CSE-CIC-IDS2018, CIC-IDS2017 and UNSW-NB15 datasets.

Coarse-grained CFD-DEM study of Gas-solid flow in gas cyclone

Gas cyclones are common device in various industries to separate solid particles and/or droplets from gas streams. Nonetheless, the mathematical modelling of gas cyclone has been very challenging since huge number of multi-sized particles are involved in the system. In this work, a numerical study of the gas–solid flow in a gas cyclone with wide range of particle size distribution (from 1 to 200 µm) is carried out by developing a coarse-grained combined Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) model. It shows that the coarse-grained CFD-DEM model is able to capture the typical flow features in a gas cyclone including the solid strands and dust ceiling phenomena. It also shows that modelling of van der Waals force is critical for the prediction of separation efficiency in gas cyclone especially for fine particles. Furthermore, it is tried to explain how the dust ceiling is formed.

Coarse-Grained Water Model Development for Accurate Dynamics and Structure Prediction.

Several coarse-graining (CG) methods have been combined to develop a CG model of water capable of the accurate prediction of structure and dynamics properties. The multiscale coarse-graining (MS-CG) method based on force matching and the PDF-based coarse-graining method were used for accurate dynamics prediction. The iterative Boltzmann inversion (IBI) method was added for accurate structure representation. The approach is applied to bulk water, and the results show close reproduction of the CG structure when compared with the reference atomistic data. The combination of MS-CG and IBI methods facilitates the development of CG force fields at different temperatures based on a single MS-CG coarse-graining procedure. The dynamic properties of the CG water model closely match those obtained from the reference atomistic system. The general application of this approach to any existing coarse-graining methods is discussed.

Incorporation of flamelets generated manifold method in coarse-grained Euler-Lagrange simulations of pulverized coal combustion

Large Eddy Simulations (LES) combined with the Flamelet Generated Manifold (FGM) approach has become popular for modeling turbulent flame/combustion of pulverized coal or biomass. In this paper, we incorporate a coarse-graining discrete element method (CG-DEM) with the FGM-LES for cost-effective Euler–Lagrange (EL) simulations of turbulent coal combustion. This developed method significantly reduces the computational burden in both Eulerian phase (via FGM) and Lagrangian phase (via CG) compared to conventional reactive EL simulations. The study is based on the experiment of the CRIEPI (Central Research Institute of Electric Power Industry) burner, which has been extensively used for model validation in literature. It is shown that the developed model can effectively reduce the computing cost while maintaining a good accuracy in predicting the pulverized coal flow dynamics as well as combustion performance. For this particular case, an optimal coarse-graining factor, the size ratio of coarse-grained parcels to original particles, of 2 is determined. A larger coarse-graining factor will create local concentrated regions of fuel and consequently lead to different combustion behavior.

Particle generation to minimize the computing time of the discrete element method for particle packing simulation

There are computation time constraints caused by the number and size of particles in the powder packing simulation using DEM. In this paper, newly suggested packing model transforms a general packing sequence —particle generation, stack, and compression-into particle generation and packing by growing particles. To verify the new packing model, it was compared using three contact models widely used in DEM, in terms of radial distribution function, porosity, and coordination number. As a result, contact between particles showed a similar trend, and the pore distribution was also similar. Using the new packing model can reduce simulation time by 400 % compared to the normal packing model without any other coarse graining methods. This model has only been applied to particle packing simulations in this paper, but it can be expanded to other simulations with complex domain based on DEM.

Pore Formation Mechanism of A-Beta Peptide on the Fluid Membrane: A Combined Coarse-Grained and All-Atomic Model.

In Alzheimer’s disease, ion permeability through the ionic channel formed by Aβ peptides on cellular membranes appears to underlie neuronal cell death. An understanding of the formation mechanism of the toxic ionic channel by Aβ peptides is very important, but remains unclear. Our simulation results demonstrated the dynamics and mechanism of channel formation by Aβ1-28 peptides on the DPPC and POPC membrane by the coarse-grained method. The ionic channel formation is driven by the gyration of the radius and solvent accessible molecular surface area of Aβ1-28 peptides. The ionic channel formation mechanism was explored by the free energy profile based on the distribution of the gyration of the radius and solvent accessible molecular surface area of Aβ1-28 peptides on the fluid membrane. The stability and water permeability of the ionic channel formed by Aβ peptides was investigated by all-atomic model simulation. Our simulation showed that the ionic channel formed by Aβ1-28 peptides is very stable and has a good water permeability. This could help us to understand the pore formation mechanism by Aβ1-28 peptides on the fluidic membrane. It also provides us with a guideline by which to understand the toxicity of Aβ1-28 peptides’ pores to the cell.

Coarse Graining on Financial Correlation Networks

Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at different levels. However, few studies pay attention to these multi-part features of financial networks. In this study, we present a geometric coarse graining method based on Voronoi regions of a financial network. Rather than studying the dense structure of the network, we perform our analysis on the triangular maximally filtering of a financial network. Such filtered topology emerges as an efficient approach because it keeps local clustering coefficients steady and it underlies the network geometry. Moreover, in order to capture changes in coarse grains geometry throughout a financial stress, we study Haantjes curvatures of paths that are the farthest from the center in each of the Voronoi regions. We performed our analysis on a network representation comprising the stock market indices BIST (Borsa Istanbul), FTSE100 (London Stock Exchange), and Nasdaq-100 Index (NASDAQ), across three financial crisis periods. Our results indicate that there are remarkable changes in the geometry of coarse grains.