Integrating Multiscale Simulation with Machine Learning to Screen and Design FIL@COFs for Ethane-Selective Separation

Efficient and economical separation of C2H6/C2H4 is an imperative and extremely challenging process in the petrochemical industry. The C2H6-selective adsorbents with high working capacity and high selectivity are highly desirable from a practical application standpoint. In this study, we constructed a database of fluorinated ionic liquid@covalent organic frameworks (FIL@COFs) and screened out the high-performing FIL@COFs for C2H6-selective separation. Utilizing the optimal machine learning (ML) algorithm (XGBoost) and hyperparameters, we further revealed the key factors influencing the separation performance. The multiscale simulation not only validated the prediction accuracy of ML but also demonstrated that adjusting the largest cavity diameter of COFs with FILs could yield FIL@COFs with high performance for C2H6-selective separation. Our work provides essential guidance for designing new FIL@COF adsorbents for value-added gas purification.

Effect of Nanoscale in Situ Interface Welding on the Macroscale Thermal Conductivity of Insulating Epoxy Composites: A Multiscale Simulation Investigation

Insulating thermally conductive polymer composites are in great demand in integrated-circuit packages, for efficient heat dissipation and to alleviative short-circuit risk. Herein, the continuous oriented hexagonal boron nitride (h-BN) frameworks (o-BN@SiC) were prepared via self-assembly and in situ chemical vapor infiltration (CVI) interface welding. The insulating o-BN@SiC/epoxy (o-BN@SiC/EP) composites exhibited enhanced thermal conductivity benefited from the CVI-SiC-welded BN-BN interface. Further, multiscale simulation, combining first-principles calculation, Monte Carlo simulation, and finite-element simulation, was performed to quantitatively reveal the effect of the welded BN-BN interface on the heat transfer of o-BN@SiC/EP composites. Phonon transmission in solders and phonon-phonon coupling of filler-solder interfaces enhanced the interfacial heat transfer between adjacent h-BN microplatelets, and the interfacial thermal resistance of the dominant BN-BN interface was decreased to only 3.83 nK·m2/W from 400 nK·m2/W, plunging by over 99%. This highly weakened interfacial thermal resistance greatly improved the heat transfer along thermal pathways and resulted in a 26% thermal conductivity enhancement of o-BN@SiC/EP composites, compared with physically contacted oriented h-BN/EP composites, at 15 vol % h-BN. This systematic multiscale simulation broke through the barrier of revealing the heat transfer mechanism of polymer composites from the nanoscale to the macroscale, which provided rational cognition about the effect of the interfacial thermal resistance between fillers on the thermal conductivity of polymer composites.

The Cellular Structure and Mechanical Properties of Polypropylene/Nano-CaCO3/Ethylene-propylene-diene-monomer Composites Prepared by an In-Mold-Decoration/Microcellular-Injection-Molding Process

Polypropylene (PP)-composite foams were prepared by a combination process of microcellular injection molding (MIM) and in-mold decoration (IMD). The effect of ethylene propylene diene monomer (EPDM) on the crystallization properties, rheological properties, microstructure, and mechanical properties of PP-composite foams was studied. The effect of the additives on the strength and toughness of PP-composite foam as determined by the multiscale simulation method is discussed. The results showed that an appropriate amount of EPDM was beneficial to the cell growth and toughening of the PP blends. When the content of EPDM was 15 wt%, the PP-composite foams obtained the minimum cellular size, the maximum cellular density, and the best impact toughness. At the same time, the mesoscopic simulation shows that the stress concentration is the smallest, which indicates that 15 wt% EPDM has the best toughening effect in these composite materials.

Inductive Determination of Rate-Reaction Equation Parameters for Dislocation Structure Formation Using Artificial Neural Network

The reaction-diffusion equation approach, which solves differential equations of the development of density distributions of mobile and immobile dislocations under mutual interactions, is a method widely used to model the dislocation structure formation. A challenge in the approach is the difficulty in the determination of appropriate parameters in the governing equations because deductive (bottom-up) determination for such a phenomenological model is problematic. To circumvent this problem, we propose an inductive approach utilizing the machine-learning method to search a parameter set that produces simulation results consistent with experiments. Using a thin film model, we performed numerical simulations based on the reaction-diffusion equations for various sets of input parameters to obtain dislocation patterns. The resulting patterns are represented by the following two parameters; the number of dislocation walls (p2), and the average width of the walls (p3). Then, we constructed an artificial neural network (ANN) model to map between the input parameters and the output dislocation patterns. The constructed ANN model was found to be able to predict dislocation patterns; i.e., average errors in p2 and p3 for test data having 10% deviation from the training data were within 7% of the average magnitude of p2 and p3. The proposed scheme enables us to find appropriate constitutive laws that lead to reasonable simulation results, once realistic observations of the phenomenon in question are provided. This approach provides a new scheme to bridge models for different length scales in the hierarchical multiscale simulation framework.

Nature of Catalytic Behavior of Cobalt Oxides for CO2 Hydrogenation

Cobalt oxide (CoO _x ) catalysts are widely applied in CO₂ hydrogenation but suffer from structural evolution during the reaction. This paper describes the complicated structure-performance relationship under reaction conditions. An iterative approach was employed to simulate the reduction process with the help of neural network potential-accelerated molecular dynamics. Based on the reduced models of catalysts, a combined theoretical and experimental study has discovered that CoO(111) provides active sites to break C-O bonds for CH₄ production. The analysis of the reaction mechanism indicated that the C-O bond scission of *CH₂O species plays a key role in producing CH₄. The nature of dissociating C-O bonds is attributed to the stabilization of *O atoms after C-O bond cleavage and the weakening of C-O bond strength by surface-transferred electrons. This work may offer a paradigm to explore the origin of performance over metal oxides in heterogeneous catalysis.

Robust Spirobifluorene Core Based Hole Transporters with High Mobility for Long-Life Green Phosphorescent Organic Light-Emitting Devices

Using a tailored high triplet energy hole transport layer (HTL) is a suitable way to improve the efficiency and extend the lifetime of organic light-emitting devices (OLEDs), which can use all molecular excitons of singlets and triplets. In this study, dibenzofuran (DBF)-end-capped and spirobifluorene (SBF) core-based HTLs referred as TDBFSBF1 and TDBFSBF2 were effectively developed. TDBFSBF1 exhibited a high glass transition temperature of 178 °C and triplet energy of 2.5 eV. Moreover, a high external quantum efficiency of 22.0 %, long operational lifetime at 50 % of the initial luminance of 89,000 h, and low driving voltage at 1000 cd m^-2 of 2.95 V were achieved in green phosphorescent OLEDs using TDBFSBF1. Further, a high-hole mobility μ_h value of 1.9×10^-3  cm²  V^-1  s^-1 was recorded in TDBFSBF2. A multiscale simulation successfully reproduced the experimental μ_h values and indicated that the reorganization energy was the primary factor in determining the mobility differences among these SBF core based HTLs.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

A Multiscale Simulation of Polymer Melt Injection Molding Filling Flow Using SPH Method with Slip-Link Model

In this article, a multiscale simulation method of polymer melt injection molding filling flow is established by combining an improved smoothed particle hydrodynamics method and clustered fixed slip-link model. The proposed method is first applied to the simulation of HDPE melt in a classic Poiseuille flow case, and then two high-speed and high-viscosity injection molding flow cases in two simple long 2D rectangular cavities with and without a circular obstacle, respectively, are analyzed. For each case, the macro velocity results, and the micro average number of entanglements Z_ave and orientation degree S results are demonstrated and discussed, and the changing trends of Z_ave and S are analyzed. The results of the two injection molding cases are compared, and the influence of the obstacle on the injection flow at both the macro and micro levels is analyzed. Furthermore, based on the multiscale results, reason of some structural features and defects in injection molded products are analyzed.

Multiscale Simulation of Composite Structures: Damage Assessment, Mechanical Analysis and Prediction

Composites can be engineered to exhibit high strength, high stiffness, and high toughness. Composite structures have been used increasingly in various engineering applications. In recent decades, most fundamentals of science have expanded their reach by many orders of magnitude. Currently, one of the primary goals of science and technology seems to be the quest to develop reliable methods for linking the physical phenomena that occur over multiple length scales, particularly from a nano-/micro-scale to a macroscale. The aim of this Special Issue is to assemble high quality papers that advance the field of multiscale simulation of composite structures, through the application of any modern computational and/or analytical methods alone or in conjunction with experimental techniques, for damage assessment or mechanical analysis and prediction.

Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling

In-stent restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute coronary syndrome. An uncertainty quantification of a model for in-stent restenosis with four uncertain parameters (endothelium regeneration time, the threshold strain for smooth muscle cell bond breaking, blood flow velocity and the percentage of fenestration in the internal elastic lamina) is presented. Two quantities of interest were studied, namely the average cross-sectional area and the maximum relative area loss in a vessel. Owing to the high computational cost required for uncertainty quantification, a surrogate model, based on Gaussian process regression with proper orthogonal decomposition, was developed and subsequently used for model response evaluation in the uncertainty quantification. A detailed analysis of the uncertainty propagation is presented. Around 11% and 16% uncertainty is observed on the two quantities of interest, respectively, and the uncertainty estimates show that a higher fenestration mainly determines the uncertainty in the neointimal growth at the initial stage of the process. The uncertainties in blood flow velocity and endothelium regeneration time mainly determine the uncertainty in the quantities of interest at the later, clinically relevant stages of the restenosis process.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

ACM REFERENCE FORMAT

Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

Multiscale Modeling and Simulation of Polymer Blends in Injection Molding: A Review

Modeling and simulation of the morphology evolution of immiscible polymer blends during injection molding is crucial for predicting and tailoring the products’ performance. This paper reviews the state-of-the-art progress in the multiscale modeling and simulation of injection molding of polymer blends. Technological development of the injection molding simulation on a macroscale was surveyed in detail. The aspects of various models for morphology evolution on a mesoscale during injection molding were discussed. The current scale-bridging strategies between macroscopic mold-filling flow and mesoscopic morphology evolution, as well as the pros and cons of the solutions, were analyzed and compared. Finally, a comprehensive summary of the above models is presented, along with the outlook for future research in this field.

Prediction of the Ultimate Strength of Notched and Unnotched IM7/977-3 Laminated Composites Using a Micromechanics Approach

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina’s representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, −60, 30]_2S, [0, 45, 90, −45]_2S, and [60, 0, −60]_3S, subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, −45]_2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.

Multiscale Simulation of Semi-Crystalline Polymers to Predict Mechanical Properties

A multiscale simulation method for the determination of mechanical properties of semi-crystalline polymers is presented. First, a four-phase model of crystallization of semi-crystalline polymers is introduced, which is based on the crystallization model of Strobl. From this, a simulation on the nanoscale is derived, which models the formation of lamellae and spherulites during the cooling of the polymer by using a cellular automaton. In the solidified state, mechanical properties are assigned to the formed phases and thus the mechanical behavior of the nanoscale is determined by a finite element (FE) simulation. At this scale, simulations can only be performed up to a simulation range of a few square micrometers. Therefore, the dependence of the mechanical properties on the degree of crystallization is determined by means of homogenization. At the microscale, the cooling of the polymer is simulated by a cellular automaton according to evolution equations. In combination with the mechanical properties determined by homogenization, the mechanical behavior of a macroscopic component can be predicted.

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Designing Shape Morphing Behavior through Local Programming of Mechanical Metamaterials

Shape morphing implicates that a specific condition leads to a morphing reaction. The material thus transforms from one shape to another in a predefined manner. In this paper, not only the target shape but rather the evolution of the material's shape as a function of the applied strain is programmed. To rationalize the design process, concepts from informatics (processing functions, for example, Poisson's ratio (PR) as function of strain: ν = f(ε) and if-then-else conditions) will be introduced. Three types of shape morphing behavior will be presented: (1) achieving a target shape by linearly increasing the amplitude of the shape, (2) filling up a target shape in linear steps, and (3) shifting a bulge through the material to a target position. In the first case, the shape is controlled by a geometric gradient within the material. The filling kind of behavior was implemented by logical operations. Moreover, programming moving hillocks (3) requires to implement a sinusoidal function ε_y  = sin (ε_x ) and an if-then-else statement into the unit cells combined with a global stiffness gradient. The three cases will be used to show how the combination of mechanical mechanisms as well as the related parameter distribution enable a programmable shape morphing behavior in an inverse design process.

Uncertainty quantification patterns for multiscale models

Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Shape Effect of Surface Defects on Nanohardness by Quasicontinuum Method

Nanoindentation on a platinum thin film with surface defects in a rectangular shape and triangular shape was simulated using the quasicontinuum method to study the shape effect of surface defects on nanohardness. The results show that the nanohardness of thin film with triangular defects is basically larger than those with rectangular defects, which is closely related to the height of the surface defects at the boundary near to the indenter. Moreover, the triangular defect might have an enhancement effect on nanohardness by a certain size of the defects and the boundary orientation of the defect, where such an enhancement effect increases as the defect grows. Furthermore, the nanohardness decreases when the defect is folded from wide to narrow in the same atom cavity, and particularly expresses a more obvious drop when the height of the defects increases. In addition, larger sizes of the rectangular defect induce more reduction in nanohardness, while the nanohardness of the triangular surface defect is sensitive to the periodic arrangement of atoms changed by the boundary orientation of the defect, which is well explained and demonstrated by the calculation formula theory of necessary load for dislocation emission.

Multiscale Assessment of Nanoscale Manufacturing Process on the Freeform Copper Surface

The nanocutting has been paid great attention in ultra-precision machining and high sealing mechanical devices due to its nanometer level machining accuracy and surface quality. However, the conventional methods applicable to reproduce the cutting process numerically such as finite element (FE) and molecular dynamics (MD) are challenging to unveil the cutting machining mechanism of the nanocutting due to the limitation of the simulation scale and computational cost. Here a modified quasi-continuous method (QC) is employed to analyze the dynamic nanocutting behavior (below 10 nm) of the copper sample. After preliminary validation of the effectiveness via the wave propagation on the copper ribbon, we have assessed the effects of cutting tool parameters and back-engagement on the cutting force, stress distribution and surface metamorphic layer depth during the nanocutting process of the copper sample. The cutting force and depth of the surface metamorphic layer is susceptible to the back-engagement, and well tolerant to the cutting tool parameters such as the tool rank angle and tool rounded edge diameter. The results obtained by the QC method are comparable to those from the MD method, which indicate the effectiveness and applicability of the modified QC method in the nanocutting process. Overall, our work provides an applicable and efficient strategy to investigate the nanocutting machining mechanism of the large-scale workpiece and shed light on its applications in the super-precision and high surface quality devices.

First principles-based multiscale atomistic methods for input into first principles nonequilibrium transport across interfaces

This issue of PNAS features "nonequilibrium transport and mixing across interfaces," with several papers describing the nonequilibrium coupling of transport at interfaces, including mesoscopic and macroscopic dynamics in fluids, plasma, and other materials over scales from microscale to celestial. Most such descriptions describe the materials in terms of the density and equations of state rather than specific atomic structures and chemical processes. It is at interfacial boundaries where such atomistic information is most relevant. However, there is not yet a practical way to couple these phenomena with the atomistic description of chemistry. The starting point for including such information is the quantum mechanics (QM). However, practical QM calculations are limited to a hundred atoms for dozens of picoseconds, far from the scales required to inform the continuum level with the proper atomistic description. To bridge this enormous gap, we need to develop practical methods to extend the scale of the atomistic simulation by several orders of magnitude while retaining the level of QM accuracy in describing the chemical process. These developments would enable continuum modeling of turbulent transport at interfaces to incorporate the relevant chemistry. In this perspective, we will focus on recent progress in accomplishing these extensions in first principles-based atomistic simulations and the strategies being pursued to increase the accuracy of very large scales while dramatically decreasing the computational effort.

pH-Dependent Distribution of Functional Groups on Titanium-Based MXenes

MXenes are a new rapidly developing class of two-dimensional materials with suitable properties for a broad range of applications. It has been shown that during synthesis of these materials the surfaces are usually functionalized by O, OH, and F and further suggested that controlling the surface allows controlling the material properties. However, a proper understanding of the surface structure is still missing, with a significant discrepancy between computational and experimental studies. Experiments consistently show formation of surfaces with mixed terminations, whereas computational studies point toward pure terminated surfaces. Here, we explain the formation of mixed functionalization on the surface of titanium-based two-dimensional carbides, Ti2C and Ti3C2, using a multiscale modeling scheme. Our scheme is based on calculating Gibbs free energy of formation by a combination of electronic structure calculations with cluster expansion and Monte Carlo simulations. Our calculations show formation of mixtures of O, OH, and F on the surface with the composition depending on pH, temperature, and the work function. On the other hand, our results also suggest a limited stable range of compositions, which challenges the paradigm of a high tunability of MXene properties.

Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications

Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.

Highly-Ductile Magnesium Alloys: Atomistic-Flow Mechanisms and Alloy Designing

This special issue, “Highly-Ductile Magnesium Alloys: Atomistic-Flow Mechanisms and Alloy Designing”, was introduced to cover all aspects associated with the improvement of the ductility of magnesium (Mg) materials through controlling the atomistic flow mechanisms in Mg. The structural aspects that can also influence the ductility of polycrystalline Mg alloys, such as grain size and basal texture characteristics, are addressed in this issue. Therefore, we hope that the contributions to the present issue can effectively support the research on the ductility of Mg.

Cholesterol Interaction Sites on the Transmembrane Domain of the Hedgehog Signal Transducer and Class F G Protein-Coupled Receptor Smoothened

Transduction of Hedgehog signals across the plasma membrane is facilitated by the class F G-protein-coupled-receptor (GPCR) Smoothened (SMO). Recent studies suggest that SMO is modulated via interactions of its transmembrane (TM) domain with cholesterol. We apply molecular dynamics simulations of SMO embedded in cholesterol containing lipid bilayers, revealing a direct interaction of cholesterol with the TM domain at regions distinct from those observed in class A GPCRs. In particular the extracellular tips of helices TM2 and TM3 form a well-defined cholesterol interaction site. Potential of mean force calculations yield a free energy landscape for cholesterol binding. Alongside analysis of equilibrium cholesterol occupancy, this reveals the existence of a dynamic "greasy patch" interaction with the TM domain of SMO, which may be compared with previously identified lipid interaction sites on other membrane proteins. These predictions provide molecular-level insights into cholesterol interactions with a class F GPCR, suggesting potential druggable sites.

Molecular Modeling Approach to Determine the Flory-Huggins Interaction Parameter for Polymer Miscibility Analysis

In this work, we present a thorough procedure for estimating the Flory-Huggins χ-parameter for use in atomistic and mesoscale molecular simulations in computational materials science. In particular, we propose improvements upon traditional Flory-Huggins theory by implementing a Connolly volume normalization (CVN). We apply this technique to several test systems, including a blend of poly (epichlorohydrin) and poly (methyl acrylate), a blend of polyethylene glycol and poly (methyl methacrylate), a blend of polystyrene and deuterated polystyrene, and three molecular-weight variants (monomer, dimer, and trimer) of a triblock copolymer for use in multicompartment micelle applications. Our results demonstrate that the newly developed procedure offers high accuracy and efficiency in predicting the Flory-Huggins χ-parameter for miscibility analysis compared to traditional experimental and computational methods. There are still several factors that cause the magnitude of the χ-parameter to vary between simulations performed on molecular species with the same identity but different degrees of polymerization; although we discuss possible explanations for these factors, this is nonetheless a primary focus for further exploration into this new methodology.

"Skin-Core-Skin" Structure of Polymer Crystallization Investigated by Multiscale Simulation

“Skin-core-skin” structure is a typical crystal morphology in injection products. Previous numerical works have rarely focused on crystal evolution; rather, they have mostly been based on the prediction of temperature distribution or crystallization kinetics. The aim of this work was to achieve the “skin-core-skin” structure and investigate the role of external flow and temperature fields on crystal morphology. Therefore, the multiscale algorithm was extended to the simulation of polymer crystallization in a pipe flow. The multiscale algorithm contains two parts: a collocated finite volume method at the macroscopic level and a morphological Monte Carlo method at the microscopic level. The SIMPLE (semi-implicit method for pressure linked equations) algorithm was used to calculate the polymeric model at the macroscopic level, while the Monte Carlo method with stochastic birth-growth process of spherulites and shish-kebabs was used at the microscopic level. Results show that our algorithm is valid to predict “skin-core-skin” structure, and the initial melt temperature and the maximum velocity of melt at the inlet mainly affects the morphology of shish-kebabs.

Multiscale simulation of the interaction of calreticulin-thrombospondin-1 complex with a model membrane microdomain

Cell surface calreticulin (CRT) binding to thrombospondin-1 (TSP1), regulates cell adhesion, migration, anoikis resistance, and collagen production. Due to the essential role of membrane microdomains in CRT-mediated focal adhesion disassembly, we previously studied the effect of raft-like bilayers on TSP1-CRT interactions with all-atom molecular dynamics (AAMD) simulations. However, the simulated systems of protein on the surface of the bilayer(s) in the explicit solvent are too large for long timescale AAMD simulations due to computational expense. In this study, we adopted a multiscale modeling approach of combining AAMD, coarse-grained molecule dynamics (CGMD), and reversed AAMD (REV AAMD) simulations to investigate the interactions of single CRT or of the TSP1-CRT complex with a membrane microdomain at microsecond timescale. Results showed that CRT conformational stabilization by binding of TSP1 in AAMD simulation was undetectable in CGMD simulation, but it was recovered in REV AAMD simulation. Similarly, interactions of the CRT N-domain and TSP1 with the membrane microdomain were lost in CGMD simulations but they were re-gained in the REV AAMD simulations. There was the higher coordination of the CRT P-domain in the TSP1-CRT complex with the lipid components of membrane microdomain compared to that of single CRT, which could directly affect the conformation of CRT and further mediate CRT recruitment of LDL receptor-related protein for signaling events. This study provides structural and molecular insights into TSP1-CRT interactions in a membrane microdomain environment and demonstrates the feasibility of using multiscale simulations to investigate the interactions between protein and membrane microdomains at a long timescale.

A multiscale approach for determining the morphology of endothelial cells at a coronary artery

The morphology of endothelial cells (ECs) may be an indication for determining atheroprone sites. Until now, there has been no clinical imaging technique to visualize the morphology of ECs in the arteries. The present study introduces a computational technique for determining the morphology of ECs. This technique is a multiscale simulation consisting of the artery scale and the cell scale. The artery scale is a fluid-structure interaction simulation. The input for the artery scale is the geometry of the coronary artery, that is, the dynamic curvature of the artery due to the cardiac motion, blood flow, blood pressure, heart rate, and the mechanical properties of the blood and the arterial wall, the measurements of which can be obtained for a specific patient. The results of the artery scale are wall shear stress (WSS) and cyclic strains as the mechanical stimuli of ECs. The cell scale is an inventive mass-and-spring model that is able to determine the morphological response of ECs to any combination of mechanical stimuli. The results of the multiscale simulation show the morphology of ECs at different locations of the coronary artery. The results indicate that the atheroprone sites have at least 1 of 3 factors: low time-averaged WSS, high angle of WSS, and high longitudinal strain. The most probable sites for atherosclerosis are located at the bifurcation region and lie on the myocardial side of the artery. The results also indicated that a higher dynamic curvature is a negative factor and a higher pulse pressure is a positive factor for protection against atherosclerosis.

Synergistic Anti-arrhythmic Effects in Human Atria with Combined Use of Sodium Blockers and Acacetin

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Developing effective and safe anti-AF drugs remains an unmet challenge. Simultaneous block of both atrial-specific ultra-rapid delayed rectifier potassium (K⁺) current (I_Kur) and the Na⁺ current (I_Na) has been hypothesized to be anti-AF, without inducing significant QT prolongation and ventricular side effects. However, the antiarrhythmic advantage of simultaneously blocking these two channels vs. individual block in the setting of AF-induced electrical remodeling remains to be documented. Furthermore, many I_Kur blockers such as acacetin and AVE0118, partially inhibit other K⁺ currents in the atria. Whether this multi-K⁺-block produces greater anti-AF effects compared with selective I_Kur-block has not been fully understood. The aim of this study was to use computer models to (i) assess the impact of multi-K⁺-block as exhibited by many I_Kur blokers, and (ii) evaluate the antiarrhythmic effect of blocking I_Kur and I_Na, either alone or in combination, on atrial and ventricular electrical excitation and recovery in the setting of AF-induced electrical-remodeling. Contemporary mathematical models of human atrial and ventricular cells were modified to incorporate dose-dependent actions of acacetin (a multichannel blocker primarily inhibiting I_Kur while less potently blocking I_to, I_Kr, and I_Ks). Rate- and atrial-selective inhibition of I_Na was also incorporated into the models. These single myocyte models were then incorporated into multicellular two-dimensional (2D) and three-dimensional (3D) anatomical models of the human atria. As expected, application of I_Kur blocker produced pronounced action potential duration (APD) prolongation in atrial myocytes. Furthermore, combined multiple K⁺-channel block that mimicked the effects of acacetin exhibited synergistic APD prolongations. Synergistically anti-AF effects following inhibition of I_Na and combined I_Kur/K⁺-channels were also observed. The attainable maximal AF-selectivity of I_Na inhibition was greatly augmented by blocking I_Kur or multiple K⁺-currents in the atrial myocytes. This enhanced anti-arrhythmic effects of combined block of Na⁺- and K⁺-channels were also seen in 2D and 3D simulations; specially, there was an enhanced efficacy in terminating re-entrant excitation waves, exerting improved antiarrhythmic effects in the human atria as compared to a single-channel block. However, in the human ventricular myocytes and tissue, cellular repolarization and computed QT intervals were modestly affected in the presence of actions of acacetin and I_Na blockers (either alone or in combination). In conclusion, this study demonstrates synergistic antiarrhythmic benefits of combined block of I_Kur and I_Na, as well as those of I_Na and combined multi K⁺-current block of acacetin, without significant alterations of ventricular repolarization and QT intervals. This approach may be a valuable strategy for the treatment of AF.

Symmetry-related proton transfer pathways in respiratory complex I

Complex I functions as the initial electron acceptor in aerobic respiratory chains of most organisms. This gigantic redox-driven enzyme employs the energy from quinone reduction to pump protons across its complete approximately 200-Å membrane domain, thermodynamically driving synthesis of ATP. Despite recently resolved structures from several species, the molecular mechanism by which complex I catalyzes this long-range proton-coupled electron transfer process, however, still remains unclear. We perform here large-scale classical and quantum molecular simulations to study the function of the proton pump in complex I from Thermus thermophilus The simulations suggest that proton channels are established at symmetry-related locations in four subunits of the membrane domain. The channels open up by formation of quasi one-dimensional water chains that are sensitive to the protonation states of buried residues at structurally conserved broken helix elements. Our combined data provide mechanistic insight into long-range coupling effects and predictions for site-directed mutagenesis experiments.

Full-length, Oligomeric Structure of Wzz Determined by Cryoelectron Microscopy Reveals Insights into Membrane-Bound States

Wzz is an integral inner membrane protein involved in regulating the length of lipopolysaccharide O-antigen glycans and essential for the virulence of many Gram-negative pathogens. In all Wzz homologs, the large periplasmic domain is proposed to be anchored by two transmembrane helices, but no information is available for the transmembrane and cytosolic domains. Here we have studied purified oligomeric Wzz complexes using cryoelectron microscopy and resolved the transmembrane regions within a semi-continuous detergent micelle. The transmembrane helices of each monomer display a right-handed super-helical twist, and do not interact with the neighboring transmembrane domains. Modeling, flexible fitting and multiscale simulation approaches were used to study the full-length complex and to provide explanations for the influence of the lipid bilayer on its oligomeric status. Based on structural and in silico observations, we propose a new mechanism for O-antigen chain-length regulation that invokes synergy of Wzz and its polymerase partner, Wzy.

Chimaera simulation of complex states of flowing matter

We discuss a unified mesoscale framework (chimaera) for the simulation of complex states of flowing matter across scales of motion. The chimaera framework can deal with each of the three macro-meso-micro levels through suitable 'mutations' of the basic mesoscale formulation. The idea is illustrated through selected simulations of complex micro- and nanoscale flows.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations

Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

Scalability Test of Multiscale Fluid-Platelet Model for Three Top Supercomputers

We have tested the scalability of three supercomputers: the Tianhe-2, Stampede and CS-Storm with multiscale fluid-platelet simulations, in which a highly-resolved and efficient numerical model for nanoscale biophysics of platelets in microscale viscous biofluids is considered. Three experiments involving varying problem sizes were performed: Exp-S: 680,718-particle single-platelet; Exp-M: 2,722,872-particle 4-platelet; and Exp-L: 10,891,488-particle 16-platelet. Our implementations of multiple time-stepping (MTS) algorithm improved the performance of single time-stepping (STS) in all experiments. Using MTS, our model achieved the following simulation rates: 12.5, 25.0, 35.5 μs/day for Exp-S and 9.09, 6.25, 14.29 μs/day for Exp-M on Tianhe-2, CS-Storm 16-K80 and Stampede K20. The best rate for Exp-L was 6.25 μs/day for Stampede. Utilizing current advanced HPC resources, the simulation rates achieved by our algorithms bring within reach performing complex multiscale simulations for solving vexing problems at the interface of biology and engineering, such as thrombosis in blood flow which combines millisecond-scale hematology with microscale blood flow at resolutions of micro-to-nanoscale cellular components of platelets. This study of testing the performance characteristics of supercomputers with advanced computational algorithms that offer optimal trade-off to achieve enhanced computational performance serves to demonstrate that such simulations are feasible with currently available HPC resources.

A numerical framework to investigate hemodynamics during endovascular mechanical recanalization in acute stroke

Ischemic stroke, caused by embolism of cerebral vessels, inflicts high morbidity and mortality. Endovascular aspiration of the blood clot is an interventional technique for the recanalization of the occluded arteries. However, the hemodynamics in the Circle of Willis (CoW) are not completely understood, which results in medical misjudgment and complications during surgeries. In this study we establish a multiscale description of cerebral hemodynamics during aspiration thrombectomy. First, the CoW is modeled as a 1D pipe network on the basis of computed tomography angiography (CTA) scans. Afterwards, a vascular occlusion is placed in the middle cerebral artery and the relevant section of the CoW is transferred to a 3D computational fluid dynamic (CFD) domain. A suction catheter in different positions is included in the CFD simulations. The boundary conditions of the 3D domain are taken from the 1D domain to ensure system coupling. A Eulerian-Eulerian multiphase simulation describes the process of thrombus aspiration. The physiological blood flow in the 1D and 3D domains is validated with literature data. Further on, it is proved that domain reduction and pressure coupling at the boundaries are an appropriate method to reduce computational costs. Future work will apply the developed framework to various clinical questions.

Rapid Dye Regeneration Mechanism of Dye-Sensitized Solar Cells

During the light-harvesting process of dye-sensitized solar cells (DSSCs), the hole localized on the dye after the charge separation yields an oxidized dye, D(+). The fast regeneration of D(+) using the redox pair (typically the I(-)/I3(-) couple) is critical for the efficient DSSCs. However, the kinetic processes of dye regeneration remain uncertain, still promoting vigorous debates. Here, we use molecular dynamics simulations to determine that the inner-sphere electron-transfer pathway provides a rapid dye regeneration route of ∼4 ps, where penetration of I(-) next to D(+) enables an immediate electron transfer, forming a kinetic barrier. This explains the recently reported ultrafast dye regeneration rate of a few picoseconds determined experimentally. We expect that our MD based comprehensive understanding of the dye regeneration mechanism will provide a helpful guideline in designing TiO2-dye-electrolyte interfacial systems for better performing DSSCs.

Linking 3D and 2D binding kinetics of membrane proteins by multiscale simulations

Membrane proteins are among the most functionally important proteins in cells. Unlike soluble proteins, they only possess two translational degrees of freedom on cell surfaces, and experience significant constraints on their rotations. As a result, it is currently challenging to characterize the in situ binding of membrane proteins. Using the membrane receptors CD2 and CD58 as a testing system, we developed a multiscale simulation framework to study the differences of protein binding kinetics between 3D and 2D environments. The association and dissociation processes were implemented by a coarse-grained Monte-Carlo algorithm, while the dynamic properties of proteins diffusing on lipid bilayer were captured from all-atom molecular dynamic simulations. Our simulations show that molecular diffusion, linker flexibility and membrane fluctuations are important factors in adjusting binding kinetics. Moreover, by calibrating simulation parameters to the measurements of 3D binding, we derived the 2D binding constant which is quantitatively consistent with the experimental data, indicating that the method is able to capture the difference between 3D and 2D binding environments. Finally, we found that the 2D dissociation between CD2 and CD58 is about 100-fold slower than the 3D dissociation. In summary, our simulation framework offered a generic approach to study binding mechanisms of membrane proteins.

MDGRAPE-4: a special-purpose computer system for molecular dynamics simulations

We are developing the MDGRAPE-4, a special-purpose computer system for molecular dynamics (MD) simulations. MDGRAPE-4 is designed to achieve strong scalability for protein MD simulations through the integration of general-purpose cores, dedicated pipelines, memory banks and network interfaces (NIFs) to create a system on chip (SoC). Each SoC has 64 dedicated pipelines that are used for non-bonded force calculations and run at 0.8 GHz. Additionally, it has 65 Tensilica Xtensa LX cores with single-precision floating-point units that are used for other calculations and run at 0.6 GHz. At peak performance levels, each SoC can evaluate 51.2 G interactions per second. It also has 1.8 MB of embedded shared memory banks and six network units with a peak bandwidth of 7.2 GB s(-1) for the three-dimensional torus network. The system consists of 512 (8×8×8) SoCs in total, which are mounted on 64 node modules with eight SoCs. The optical transmitters/receivers are used for internode communication. The expected maximum power consumption is 50 kW. While MDGRAPE-4 software has still been improved, we plan to run MD simulations on MDGRAPE-4 in 2014. The MDGRAPE-4 system will enable long-time molecular dynamics simulations of small systems. It is also useful for multiscale molecular simulations where the particle simulation parts often become bottlenecks.

Performance of distributed multiscale simulations

Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.

Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS

We describe a novel method for calculating the quasi-static electrical potential on tetrahedral meshes, which we call E-Field. The E-Field method is implemented in STEPS, which performs stochastic spatial reaction-diffusion computations in tetrahedral-based cellular geometry reconstructions. This provides a level of integration between electrical excitability and spatial molecular dynamics in realistic cellular morphology not previously achievable. Deterministic solutions are also possible. By performing the Rallpack tests we demonstrate the accuracy of the E-Field method. Efficient node ordering is an important practical consideration, and we find that a breadth-first search provides the best solutions, although principal axis ordering suffices for some geometries. We discuss potential applications and possible future directions, and predict that the E-Field implementation in STEPS will play an important role in the future of multiscale neural simulations.

Multiscaled exploration of coupled folding and binding of an intrinsically disordered molecular recognition element in measles virus nucleoprotein

Numerous relatively short regions within intrinsically disordered proteins (IDPs) serve as molecular recognition elements (MoREs). They fold into ordered structures upon binding to their partner molecules. Currently, there is still a lack of in-depth understanding of how coupled binding and folding occurs in MoREs. Here, we quantified the unbound ensembles of the α-MoRE within the intrinsically disordered C-terminal domain of the measles virus nucleoprotein. We developed a multiscaled approach by combining a physics-based and an atomic hybrid model to decipher the mechanism by which the α-MoRE interacts with the X domain of the measles virus phosphoprotein. Our multiscaled approach led to remarkable qualitative and quantitative agreements between the theoretical predictions and experimental results (e.g., chemical shifts). We found that the free α-MoRE rapidly interconverts between multiple discrete partially helical conformations and the unfolded state, in accordance with the experimental observations. We quantified the underlying global folding-binding landscape. This leads to a synergistic mechanism in which the recognition event proceeds via (minor) conformational selection, followed by (major) induced folding. We also provided evidence that the α-MoRE is a compact molten globule-like IDP and behaves as a downhill folder in the induced folding process. We further provided a theoretical explanation for the inherent connections between "downhill folding," "molten globule," and "intrinsic disorder" in IDP-related systems. Particularly, we proposed that binding and unbinding of IDPs proceed in a stepwise way through a "kinetic divide-and-conquer" strategy that confers them high specificity without high affinity.

Multiscale Simulation as a Framework for the Enhanced Design of Nanodiamond-Polyethylenimine-based Gene Delivery

Nanodiamonds (NDs) are emerging carbon platforms with promise as gene/drug delivery vectors for cancer therapy. Specifically, NDs functionalized with the polymer polyethylenimine (PEI) can transfect small interfering RNAs (siRNA) in vitro with high efficiency and low cytotoxicity. Here we present a modeling framework to accurately guide the design of ND-PEI gene platforms and elucidate binding mechanisms between ND, PEI, and siRNA. This is among the first ND simulations to comprehensively account for ND size, charge distribution, surface functionalization, and graphitization. The simulation results are compared with our experimental results both for PEI loading onto NDs and for siRNA (C-myc) loading onto ND-PEI for various mixing ratios. Remarkably, the model is able to predict loading trends and saturation limits for PEI and siRNA, while confirming the essential role of ND surface functionalization in mediating ND-PEI interactions. These results demonstrate that this robust framework can be a powerful tool in ND platform development, with the capacity to realistically treat other nanoparticle systems.

GRIFFIN: A versatile methodology for optimization of protein-lipid interfaces for membrane protein simulations

As new atomic structures of membrane proteins are resolved, they reveal increasingly complex transmembrane topologies, and highly irregular surfaces with crevices and pores. In many cases, specific interactions formed with the lipid membrane are functionally crucial, as is the overall lipid composition. Compounded with increasing protein size, these characteristics pose a challenge for the construction of simulation models of membrane proteins in lipid environments; clearly, that these models are sufficiently realistic bears upon the reliability of simulation-based studies of these systems. Here, we introduce GRIFFIN, which uses a versatile framework to automate and improve a widely-used membrane-embedding protocol. Initially, GRIFFIN carves out lipid and water molecules from a volume equivalent to that of the protein, so as to conserve the system density. In the subsequent optimization phase GRIFFIN adds an implicit grid-based protein force-field to a molecular dynamics simulation of the pre-carved membrane. In this force-field, atoms inside the implicit protein volume experience an outward force that will expel them from that volume, whereas those outside are subject to electrostatic and van-der-Waals interactions with the implicit protein. At each step of the simulation, these forces are updated by GRIFFIN and combined with the intermolecular forces of the explicit lipid-water system. This procedure enables the construction of realistic and reproducible starting configurations of the protein-membrane interface within a reasonable timeframe and with minimal intervention. GRIFFIN is a standalone tool designed to work alongside any existing molecular dynamics package, such as NAMD or GROMACS.