Coarse-grained Method Research Articles

The importance of angular bending of Gemini surfactants on their encapsulation efficiency

The self–assembly of nonionic X-shaped Gemini surfactants is studied here, with the purpose of determining their encapsulation efficiency. Using the coarse-grained simulation method called dissipative particle dynamics, we perform a set of simulations of ternary water/oil/surfactant systems, fixing the oil/surfactant ratio at 1:1. Two surfactant structures are modeled and their efficiency to encapsulate monomeric oil is measured as a function of surfactant concentration. It is found that the encapsulation efficiency is better when the X-shaped surfactant has a more “stretched” structure. Additionally, it is shown that the number of oil/surfactant aggregates formed by surfactants with a relatively “open” structure is less stable as a function of the surfactant concentration. This is in contrast to the surfactants with the stretched structure, which makes the aggregates formed a better alternative as oil trappers. These results should be useful when synthesizing X-shaped Gemini surfactants for their use as thickening agents or drug carriers.

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

Coarse-graining of CFD-DEM for simulation of sand production in the modified cohesive contact model

Sand production is an important issue for many hydrocarbon recovery applications in unconsolidated reservoirs. The model using the computational fluid dynamics coupled with discrete element method (CFD-DEM) can capture micro-scale features of sand transport problems. In this study, coarse-graining methods of a three-dimensional CFD-DEM model are developed to investigate the sand production phenomenon. The modified cohesive contact model is utilized for the simulations with a sample based on particle size distribution obtained from the unconsolidated sandstone reservoir in Kazakhstan. The derivation of scaling from the fine model to the coarse model is presented rigorously. The original (fine scale) model is validated to the laboratory results including the cumulative sand production rate. The results of the original model is compared to the same statistic weight (SSW) and the same size parcel (SSP) coarse-gained models. The original and coarse-grained models show good agreement in the fluid streamlines, fluid and particle velocities. In terms of sand production rate, the SSW model results are more accurate than the SSP model. Moreover, the particle size distribution of the produced particles of the SSW model is consistent with the results of the original particle model. The SSP model performs better in terms of speedup by accelerating the original model up to 9.4 times in parallel computing.

CFD-DEM investigation on the agglomeration behavior of micron-sized combusted iron fines

The occurrence of agglomeration due to particle sintering is one of the most significant technical issues hindering the direct reduction process of iron oxide fines (DRI) in a high-temperature fluidized bed. To get a better understanding of the agglomeration mechanism, the (de-)fluidization behavior of micron-sized iron oxide particles in a 3-D fluidized bed is investigated using Computational Fluid Dynamics coupled with a coarse-grained Discrete Element Method (CFD-cgDEM). The inter-particle cohesive force is considered as a temperature-dependent solid bridge force. The coarse-grained method is first verified by comparing results for different scaling factors. Subsequently, the effects of temperature on the bed pressure drop, void fraction distribution, particle velocity distribution, and agglomerate size are investigated. In conclusion, the developed model shows the capacity for reliable prediction of particle agglomeration behavior, which can shed light on the design of the DRI process in high-temperature fluidized beds.

Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis.

The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.

Coarse-grained methods for heterogeneous vesicles with phase-separated domains: Elastic mechanics of shape fluctuations, plate compression, and channel insertion

We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains. We perform simulations both of passive shape fluctuations and of active systems where vesicles are subjected to compression between two plates or subjected to insertion into narrow channels. Analysis methods are developed for mapping particle configurations to continuum fields with spherical harmonics representations. Heterogeneous vesicles are found to exhibit rich behaviors where the heterogeneity can amplify surface two-point correlations, reduce resistance during compression, and augment vesicle transport times in channels. The developed methods provide general approaches for characterizing the mechanics of coarse-grained heterogeneous systems taking into account the roles of thermal fluctuations, geometry, and phase separation.

Understanding Energy Pathways in the Gulf Stream

Abstract The Gulf Stream (GS) is one of the strongest ocean currents on the planet. Eddy-rich resolution models are needed to properly represent the dynamics of the GS; however, kinetic energy (KE) can be in excess in these models if not dissipated efficiently. The question of how and how much energy is dissipated and in particular how it flows through ocean scales thus remains an important and largely unanswered question. Using a high-resolution (∼2 km) ocean model [Coastal and Regional Ocean Community (CROCO)], we characterize the spatial and temporal distribution of turbulent cascades in the GS based on a coarse-grained method. We show that the balanced flow is associated with an inverse cascade while the forward cascade is explained by ageostrophic advection associated with frontogenesis. Downscale fluxes are dominant at scales smaller than about 20 km near the surface and most intense at the GS North Wall. There is also strong seasonal variability in KE flux, with the forward cascade intensifying in winter and the inverse cascade later in spring. The forward cascade, which represents an interior route to dissipation, is compared with both numerical and boundary dissipation processes. The contribution of interior dissipation is an order of magnitude smaller than that of the other energy sinks. We thus evaluate the sensitivity of horizontal momentum advection schemes on energy dissipation and show that the decrease in numerical dissipation in a high-order scheme leads to an increase in dissipation at the boundaries, not in the downscale flux.

Reverse to forward density segregation depending on gas inflow velocity in vibrated fluidized beds

Particle density segregations in vibrated fluidized beds depending on gas inflow velocity under the same vertical vibration condition are studied. Coarse-graining discrete element method and computational fluid dynamics numerical simulations are employed to capture the behaviors of reverse segregation in which heavy particles are located above light particles at zero gas inflow velocity or at velocities considerably lower than the minimum fluidization velocity of light particles. Furthermore, upon increasing the gas inflow velocity slightly, the forward segregation occurs, such that heavy particles are located below light particles. The mechanisms are also elucidated using the simulation results. Because of the relative motions between the particles and bed caused by vertical vibration, negative gauge pressure is observed to be dependent on the vibration phase. In the reverse segregation case, the accumulative effect of the downward gas pressure gradient force induced by vibration overcomes the upward force of the forced air flow. The wall friction transports both the heavy and light particles in the vicinity of the sidewall to the bed bottom, where the local void fraction is comparatively high and reverse segregation mainly occurs. Reverse segregation results from the combined effects of the downward gas pressure gradient force, particle transport, and local formation of the high void region. The increase in gas inflow velocity enhances the upward pressure gradient force, resulting in forward segregation.

Bottom-Up Informed and Iteratively Optimized Coarse-Grained Non-Markovian Water Models with Accurate Dynamics.

Molecular dynamics (MD) simulations based on coarse-grained (CG) particle models of molecular liquids generally predict accelerated dynamics and misrepresent the time scales for molecular vibrations and diffusive motions. The parametrization of Generalized Langevin Equation (GLE) thermostats based on the microscopic dynamics of the fine-grained model provides a promising route to address this issue, in conjunction with the conservative interactions of the CG model obtained with standard coarse graining methods, such as iterative Boltzmann inversion, force matching, or relative entropy minimization. We report the application of a recently introduced bottom-up dynamic coarse graining method, based on the Mori-Zwanzig formalism, which provides accurate estimates of isotropic GLE memory kernels for several CG models of liquid water. We demonstrate that, with an additional iterative optimization of the memory kernels (IOMK) for the CG water models based on a practical iterative optimization technique, the velocity autocorrelation function of liquid water can be represented very accurately within a few iterations. By considering the distinct Van Hove function, we demonstrate that, with the presented methods, an accurate representation of structural relaxation can be achieved. We consider several distinct CG potentials to study how the choice of the CG potential affects the performance of bottom-up informed and iteratively optimized models.

Coarse-graining modeling of primitive chains in entangled polymer melts

In this paper, we provide a coarse-graining method to obtain the primitive chain from a polymer chain configuration in the entangled polymer melt by knocking out its high-frequency Rouse modes. Adopting this coarse-graining procedure, we theoretically demonstrate that the tube step length is twice the tube diameter. Moreover, a simple method to visualize the tube by adding high-frequency modes to the primitive chain is also provided.

Coarse-grained intrinsically disordered protein simulations with explicit ions parameterized by salting-out constants.

Implicit solvent coarse-grained models have emerged as an effective method for simulating intrinsically disordered proteins (IDPs) due to the long length and time-scale need to effectively simulate their equilibrium behavior. These coarse-grained methods typically rely on Debye-Hückel theory to explain the effects of salt in the simulations. However, Debye-Hückel electrostatics are only accurate over a limited range of ionic strengths and are also unable to capture ion-type specific effects, typically called Hofmeister effects that arise at higher salt concentrations. To address this, we developed an explicit ion model of potassium chloride that can interface with the otherwise implicit solvent hydrophobicity scale model for intrinsically disordered protein. Parameters for the explicit ion model were determined by matching the salting-out constants of several small peptide-like molecules. Salting-out constants were determined in the simulation by creating a system with an ion concentration gradient via an external potential and performing umbrella sampling along the concentration gradient. The remaining amino acids for which there is no data for the salting-out constant are determined using a scaled Kapcha-Rosky hydrophobicity scale determined from the umbrella sampling simulations. The parameters are validated against a set FRET data from a diverse set of linker peptides at several salt concentrations. This explicit ion model is able to capture the changes in protein expansion as a function of salt concentration at concentration above 0.5 M where Debye-Hückel screened electrostatics predict no change in protein expansion.

CFD-CGDEM coupling model for scour process simulation of submarine pipelines

Scour is a key issue affecting the service life of submarine pipelines. Understanding the scour process and scour mechanism around the pipelines is crucial for developing the reasonable and effective scour prevention measures. In this paper, the computational fluid dynamics and coarse-grained discrete element method (CFD-CGDEM) is used to simulate the scour process around the pipeline. The influence of the drag force model which is an important factor for analyzing the scour process of the submarine pipelines is investigated, and the results indicate that the choice of the drag force model has less influence on the simulation results. The characteristic of the CG method is studied considering different CG factors, particle radius and flow velocities. The results show that the CG method which can reduce the large need of computing resources and excessive computation time does not bring large deviation to the results, especially the scour depth beneath the pipelines. The evolution of the scour depth obtained by the CFD-CGDEM model agree well with the experimental results. A reasonable scour phenomenon around the pipeline is shown by the numerical simulation, which implies that the CFD-CGDEM model established in present study can simulate the scour process around the pipelines well.

Evaluation of coarse-grained CFD-DEM models with the validation of PEPT measurements

Computational Fluid Dynamics coupled with Discrete Element Method (CFD-DEM) is a commonly used numerical method to model gas-solid flow in fluidised beds and other multiphase systems. A significant limitation of CFD-DEM is the feasibility of the realistic simulation of large numbers of particles. Coarse-graining (CG) approaches, through which groups of multiple individual particles are represented by single, larger particles, can substantially reduce the total number of particles while maintaining similar system dynamics. As these three CG models have not previously been compared, there remains some debate, however, about the best practice in the application of CG in CFD-DEM simulations. In this paper, we evaluate the performance of three typical CG methods based on simulations of a bubbling fluidised bed. This is achieved through the use of a numerical validation framework, which makes full use of the high-resolution 3D positron emission particle tracking (PEPT) measurements to rigorously validate the outputs of CFD-DEM simulations conducted using various different coarse-graining models, and various different degrees of coarse-graining. The particle flow behaviours in terms of the particle occupancy field, velocity field, circulation time, and bubble size and velocity, are comprehensively analysed. It is shown that the CG simulation starts to fail when the size ratio between the bed chamber and the particles decreases to approximately 20. It is also observed, somewhat surprisingly, that the specific CG approach applied to interparticle contact parameters does not have a substantial effect on the simulation results for the bubbling bed simulations across a wide range of CG factors.

Improved coarse-graining methods for two dimensional tensor networks including fermions

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson-Majorana fermions and for the two flavor Gross-Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher’s zeros.

Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods.

Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target-PROTAC-E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parametrization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current force field, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Mirror buckling analysis of freestanding graphene membranes by coarse-grained molecular dynamics method

Up to now, the analysis has rarely been conducted of thermal-mechanical mirror buckling behavior of freestanding graphene membranes discovered in scan tunneling microscope experiments. One of the potential applications of the out-of-plane deformational behavior of graphene membranes is energy harvesting system. Whether in the experiments or for energy harvesting systems, the size of graphene membrane needs to be down to micron scale. According to previous researches, traditional molecular dynamics method is a suitable method to characterize nano-scale mirror buckling. However, owing to the limit of algorithm, when dealing with micro size model by molecular dynamics method, two problems arise: low computational efficiency and too long calculation time. Therefore, for analyzing the mirror buckling of micro size graphene membranes, the coarse-grained molecular dynamics method is utilized in this work. Graphene membranes with a fan-shaped cross section and various depth-span ratios are under mechanical or thermal loads. Effects of each factor on the mirror buckling are investigated. The calculations indicate that for graphene membranes with various depth-span ratios under mechanical load mirror buckling can be observed. And the critical loading increases with the depth-span ratio increasing. Under thermal load graphene membranes only with low depth-span ratios can undergo complete flipping phenomenon. For high depth-span ratio graphene, the center height decreases with temperature rising. However, it is hard to flip over completely. The understanding of the effects of various factors on the mirror buckling phenomenon of graphene membranes can provide theoretical guidance for designing the energy harvesting systems.

Computational investigation of the phase behavior of colloidal squares with offset magnetic dipoles.

Colloidal particles with anisotropic shapes and interactions display rich phase behavior and have potential as structural bases for materials with controllable properties. In this paper, we explore the self-assembling characteristics of a new class of particles that have been shown experimentally to form reconfigurable structures: microscopic cube-shaped colloids with a magnetic dipole that is transversely offset from the particle's center of mass. We have performed in silico simulations of the dynamics of large numbers of dipolar squares in two-dimensions using discontinuous molecular dynamics (DMD). We use a coarse-grain method where the dipolar microcubes are represented by a group of four hard circles bonded together to create a rigid square in two-dimensions and two opposite charges are embedded within the square to represent a magnetic dipole. Annealing, or "slow-cooling", simulations are conducted to determine the equilibrium structures. Systems of dipolar squares tend to assemble into one of two different types of conformations: either single- or double-stranded assemblies, each with unique structures and phase diagrams in the temperature-density plane. Single-stranded assemblies form highly interconnected percolated, or gel-like, networks. In contrast, double stranded assemblies tend to form globally-aligned nematic states at high densities, although this is not seen consistently in all runs. The phase behavior of systems of dipolar squares depends not only on the system's temperature and density, but also on the type of dipole embedded within the square and on the relative number of squares with an opposite "handedness" that are present within the system.

Coarse-grained molecular dynamics modeling and analysis of graded porous electrodes of reversible solid oxide cells sintered in two steps

Two-step sintering is proposed for preparing a tri-layer skeleton composed of graded nanoparticles. Effects on the sintering behavior, microstructure and stress for graded nanoparticles are investigated by a coarse-grained molecular dynamics method.

Numerical study on the effect of airflow on powder mixing in a container blender

Powder mixing is critical in many industries. Despite the wide variety of available mixers, the container blender is favored in industries due to easy manufacturing and convenient operation. As fine powders are frequently encountered in practical mixing, the presence of air during mixing may significantly impact the process. However, a scientific understanding of air–particle interactions in powder mixing has not been established so far. From a physical view, the air drag force on particles might be significant when the gas velocity is high and the particles are fine. Therefore, this novel study numerically investigates the effects of particle size and air presence on powder mixing under typical conditions, such that the relationship between particle–fluid dynamics and mixing performance is clarified for the first time. In the calculation, our advanced computational fluid dynamics–discrete element method, namely, the flexible Eulerian–Lagrangian method with an implicit algorithm, is utilized. To examine the effect of particle size on powder mixing, the coarse-grained discrete element method is employed for fine particle systems. Through the advanced numerical framework, the effect of airflow on powder mixing can be discussed in depth. The numerical results show that airflow accelerates the mixing of fine particles under the investigated rotation speeds. Based on the calculation results of the particle kinetics and fluid velocity distribution, it is clarified that the accelerated mixing results from the fine particle movement under air entrainment. How the movement of fine particles is developed is also elucidated by the continuously circulating gas flow and fluid drag force. Thus, this study provides a new understanding of the effects of airflow on powder mixing, which has not been scientifically clarified in previous studies.