Large-scale Simulations Research Articles

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

A unified discontinuous Galerkin formulation for interfacial multiphysics modeling of thermo-chemically driven fracture

Many engineering and natural materials exhibit coupled thermo-chemo-mechanical phenomena, which can result in embrittlement and fracture. These fractures, in turn, can alter the subsequent thermal, chemical, and mechanical response. We present a theoretical formulation and computational framework for the analysis of thermo-chemically fractured solids, with emphasis on the post-fracture thermal and chemical interfacial behavior. The theoretical model is based on the thermodynamically-consistent formulation of Loeffel and Anand (IJP, 2011). The computational method extends the scalable discontinuous Galerkin/Cohesive Zone Model (DG/CZM) of Radovitzky et al. (CMAME, 2011) to thermo-chemo-mechanics, which facilitates coupled, large-scale simulations of materials and structures containing failed interfaces. In the proposed framework, all balance laws are enforced weakly via the DG formalism, resulting in a unified formulation for multiphysics problems in solids. This naturally enables the incorporation of general interface models, e.g. to account for effects such as the aeolotropic reduction in thermochemical transport due to the presence of fractures, or the acceleration of chemical reactions along crack flanks. The approach is verified against two analytical solutions of boundary value problems drawn from thermo-poro-elasticity and thermally-driven delamination. A scalable, three-dimensional simulation of thermochemically-driven concrete cracking illustrates the complete capabilities of the interfacial multiphysics modeling framework.

Use of Self-Efficacy Scale in Mass Casualty Incidents During Drill Exercises

IntroductionMedical First Responders (MFRs) in the emergency department SUMMA 112 are tasked with handling the initial management of Mass Casualty Incidents (MCI) and building response capabilities. Training plays a crucial role in preparing these responders for effective disaster management. Yet, evaluating the impact of such training poses challenges since true competency can only be proven amid a major event. As a substitute gauge for training effectiveness, self-efficacy has been suggested.ObjectiveThe purpose of this study is to employ a pre- and post-test assessment of changes in perceived self-efficacy among MFRs following an intervention focused on the initial management of MCI. It also aimed to evaluate a self-efficacy instrument for its validity and reliability in this type of training.MethodIn this study, we used a pretest (time 1 = T1) – post-test (time 2 = T2) design to evaluate how self-efficacy changed after a training intervention with 201 MFRs in initial MCI management. ANOVA within-subjects and between subjects analyses were used.ResultsThe findings reveal a noteworthy change in self-efficacy before and after training among the 201 participants. This suggests that the training intervention positively affected participants’ perceived capabilities to handle complex situations like MCI.ConclusionThe results allow us to recommend a training program with theory components together with practical workshops and live, large-scale simulation exercises for the training of medical first responders in MCI, as it significantly increases their perception of the level of self-efficacy for developing competencies associated with disaster response.

Precision Atomistic Structures of Actinium-/Radium-/Barium-Doped Lanthanide Nanoconstructs for Radiotherapeutic Applications.

Lanthanide vanadate (LnVO4) nanoconstructs have generated considerable interest in radiotherapeutic applications as a medium for nanoscale-targeted drug delivery. For cancer treatment, LnVO4 nanoconstructs have shown promise in encapsulating and retaining radionuclides that emit alpha-particles. In this work, we examined the structure formation of LnVO4 nanoconstructs doped with actinium (Ac) and radium (Ra), both experimentally and using large-scale atomistic molecular dynamics simulations. LnVO4 nanoconstructs were synthesized via a precipitation method in aqueous media. The reaction conditions and elemental compositions were varied to control the structure, fluorescence properties, and size distribution of the LnVO4 nanoconstructs. LnVO4 nanoconstructs were characterized by X-ray diffraction, Raman spectroscopy, and fluorescence spectroscopy. Molecular dynamics simulations were performed to obtain a fundamental understanding of the structure-property relationship between radionuclides and LnVO4 nanoconstructs at the atomic length scale. Molecular dynamics simulations with well-established force field (FF) parameters show that Ra atoms tend to distribute across the nanoconstructs' surface in a broader coordination shell, while the Ac atoms are arranged inside a smaller coordination shell within the nanocluster. The Ba atoms prefer to self-assemble around the surface. These theoretical/simulation predictions of the atomistic structures and an understanding of the relationship between radionuclides and LnVO4 nanoconstructs at the atomic scale are important because they provide design principles for the future development of nanoconstructs for targeted radionuclide delivery.

Dynamic Mode Decomposition With Gaussian Process Regression for Control of High-Dimensional Nonlinear Systems

Abstract In this work, we consider the problem of learning a reduced-order model of a high-dimensional stochastic nonlinear system with control inputs from noisy data. In particular, we develop a hybrid parametric/nonparametric model that learns the “average” linear dynamics in the data using dynamic mode decomposition with control (DMDc) and the nonlinearities and model uncertainties using Gaussian process (GP) regression and compare it with total least-squares dynamic mode decomposition (tlsDMD), extended here to systems with control inputs (tlsDMDc). The proposed approach is also compared with existing methods, such as DMDc-only and GP-only models, in two tasks: controlling the stochastic nonlinear Stuart–Landau equation and predicting the flowfield induced by a jet-like body force field in a turbulent boundary layer using data from large-scale numerical simulations.

Perturbation-theory informed integrators for cosmological simulations

Large-scale cosmological simulations are an indispensable tool for modern cosmology. To enable model-space exploration, fast and accurate predictions are critical. In this paper, we show that the performance of such simulations can be further improved with time-stepping schemes that use input from cosmological perturbation theory. Specifically, we introduce a class of time-stepping schemes derived by matching the particle trajectories in a single leapfrog/Verlet drift-kick-drift step to those predicted by Lagrangian perturbation theory (LPT). As a corollary, these schemes exactly yield the analytic Zel'dovich solution in 1D in the pre-shell-crossing regime (i.e. before particle trajectories cross). One representative of this class is the popular ‘FastPM’ scheme by Feng et al. 2016[1], which we take as our baseline. We then construct more powerful LPT-inspired integrators and show that they outperform FastPM and standard integrators in fast simulations in two and three dimensions with O(1−100) timesteps, requiring fewer steps to accurately reproduce the power spectrum and bispectrum of the density field. Furthermore, we demonstrate analytically and numerically that, for any integrator, convergence is limited in the post-shell-crossing regime (to order 32 for planar-wave collapse), owing to the lacking regularity of the acceleration field, which makes the use of high-order integrators in this regime futile. Also, we study the impact of the timestep spacing and of a decaying mode present in the initial conditions. Importantly, we find that symplecticity of the integrator plays a minor role for fast approximate simulations with a small number of timesteps.

Lassoed boosting and linear prediction in the equities market

We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman on every set of selected variables. Based on the large-scale simulation experiment in Hastie, Tibshirani, and Tibshirani, lassoed boosting performs as well as the relaxed lasso in Meinshausen and, under certain scenarios, can yield a sparser model. Applied to predicting equity returns, lassoed boosting gives the smallest mean-squared prediction error compared to several other methods.

Enhancing impact resilience of thermal battery through honeycomb-structured aluminum buffering devices: Insights from large-scale gas-gun tests and simulations

Modern warfare relies heavily on electronic equipment, necessitating reliable energy sources like thermal batteries. Assessing their impact resilience, a study employed honeycomb-structured Al plates as buffering devices in a large-scale gas gun simulating artillery fire. Comparison between peak curves from gas-gun tests and simulations with varying honeycomb wall thicknesses revealed unique patterns, attributed to the buffering device's deformation-restoration process. Different honeycomb wall thicknesses led to varying deformation behavior and impact deceleration, complicating effective energy absorption assessment. Stepped honeycomb wall designs aimed to balance compression, extending energy absorption and reducing deceleration peaks. Prototype honeycomb buffering devices showed improved energy absorption and reduced deceleration during gas-gun tests. Gas-gun tests highlighted complexities in energy absorption assessment, with designs proposing improved energy absorption and reduced deceleration. The actual gas-gun test launched a projectile equipped with a thermal battery and buffering device, resulting in slight casing deformation, while battery cells remained intact, exceeding the standard discharge time (1 h).

Detailed Reaction Kinetics for Hydrocarbon Fuels: The Development and Application of the ReaxFFCHO-S22 Force Field for C/H/O Systems with Enhanced Accuracy.

Efficient and accurate reactive force fields (e.g., ReaxFF) are pivotal for large-scale atomistic simulations to comprehend microscopic combustion processes. ReaxFF has been extensively utilized to describe chemical reactions in condensed phases, but most existing ReaxFF models rely on quantum mechanical (QM) data nearly two decades old, particularly in hydrocarbon systems, constraining their accuracy and applicability. Addressing this gap, we introduce a reparametrized ReaxFFCHO-S22 for C/H/O systems, tailored for studying the pyrolysis and combustion of hydrocarbon fuel. Our approach involves high-level QM benchmarks and large database construction for C/H/O systems, global ReaxFF parameter optimization, and molecular dynamics simulations of typical hydrocarbon fuels. Density functional theory (DFT) computations utilized the M06-2X functional at the meta-generalized gradient approximation (meta-GGA) level with a large basis set (6-311++G**). Our new ReaxFFCHO-S22 model exhibits a minimum 10% enhancement in accuracy compared to the previous ReaxFF models for a large variety of hydrocarbon molecules. This advanced ReaxFFCHO-S22 not only enables efficient large-scale studies on the microscopic chemical reactions of more complex hydrocarbon fuel but also can extend to biofuels, energetic materials, polymers, and other pertinent systems, thus serving as a valuable tool for studying chemical reaction dynamics of the large-scale hydrocarbon condensed phase at an atomistic level.

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core

In atomistic spin dynamics simulations, the time cost of constructing the space- and time-displaced pair correlation function in real space increases quadratically as the number of spins N, leading to significant computational effort. The GEMM subroutine can be adopted to accelerate the calculation of the dynamical spin–spin correlation function, but the computational cost of simulating large spin systems (>40000 spins) on CPUs remains expensive. In this work, we perform the simulation on a graphics processing unit (GPU), a hardware solution widely used as an accelerator for scientific computing and deep learning. We show that GPUs can accelerate the simulation up to 25-fold compared to multi-core CPUs when using the GEMM subroutine on both. To hide memory latency, we fuse the element-wise operation into the GEMM kernel using CUTLASS which can improve the performance by 26% ∼ 33% compared to the implementation based on cuBLAS. Furthermore, we perform the ‘on-the-fly’ calculation in the epilogue of the GEMM subroutine to avoid saving intermediate results on global memory, which makes large-scale atomistic spin dynamics simulations feasible and affordable.

An ab-initio deep neural network potential for accurate large-scale simulations of the muscovite mica-water interface

Mineral-water interfaces play a critical role in several geochemical processes relevant to the habitability of our planet. These processes are often underpinned by the hydration of the ions covering the mineral surface and the resultant ion exchange with the aqueous environment. Muscovite mica, whose surface is nominally covered by K + ions, has long been considered an ideal system for probing some of these processes. However, despite several decades of both experimental and computational work, there still remains a lack of consensus on ion hydration and exchange at the mica-water interface. To obtain a detailed microscopic picture of these processes, we have developed an ab-initio based deep neural network potential (DP) describing the potential energy surface (PES) of the mica(001)-water interface. Our training dataset consisted of bulk mica, bulk liquid water, the mica(001) surface in vacuum and the explicit mica(001)-water interface, both with different surface K + arrangements. The trained model is able to reproduce the energies and forces derived from Density functional theory (DFT) based on the SCAN exchange correlation functional with good accuracy. For the surface in vacuum, K + ions located in the ditrigonal cavities composed of 2 Al atoms are predicted to be energetically more favourable, while the specific surface arrangement of K + ions does not affect the water density profile at the mica-water interface. The developed model sets the stage for estimating the energetics of the ion-exchange process at affordable computational cost without compromising the accuracy of first-principles methods.

Abstract B016: Discovering novel synthetic lethal relationships with large-scale cellular simulations

Abstract Synthetic Lethality (SL) is a compelling example of a large combinatorial space, with the number of (order-independent) pairwise combinations of human genes totaling approximately 400 million. When further considering higher-order combinations beyond pairs i.e. additional biomarkers that modulate the the efficacy of an SL pair, this space grows exponentially larger still. Understanding these higher-order combinations is crucial for the discovery of synthetic lethal pairs with better defined therapeutic windows and improved segmentation in clinical trials. As the size of this combinatorial space is far greater than can be searched by existing screening technologies, novel approaches are required to efficiently search this space and prioritize higher-order candidate SL combinations for screening. DeepOrigin is developing large-scale dynamical models of cellular systems to drive the in silico discovery of novel therapeutics across a range of diseases. These models can be simulated efficiently and in parallel, and are therefore ideally suited for searching large combinatorial spaces. We have applied our proprietary Cellular Simulation Pipeline to the problem of identifying higher-order combinations in Synthetic Lethality, so far predicting hundreds of novel synthetic lethal pairs. These pairs are currently being validated in vitro, and we present a selection of these results that demonstrate the utility of this approach. This approach could provide both novel therapeutic targets while providing additional stratification criteria for increased clinical study success. Citation Format: Oliver Purcell, Paul Lang, James Komianos, Nimi Vashi. Discovering novel synthetic lethal relationships with large-scale cellular simulations [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B016.

Achieving Tunable Erasure Coding with Cluster-Aware Redundancy Transitioning

Erasure coding has been demonstrated as a storage-efficient means against failures, yet its tunability remains a challenging issue in data centers, which is prone to induce substantial cross-cluster traffic. In this paper, we present ClusterRT , a cluster-aware redundancy transitioning approach that can dynamically tailor the redundancy degree of erasure coding in data centers. ClusterRT formulates the data relocation as the maximum flow problem to reduce cross-cluster data transfers. It then designs a parity-coordinated update algorithm, which gathers the parity chunks within the same cluster and leverage encoding dependency to further decrease the cross-cluster update traffic. ClusterRT finally rotates the parity chunks to balance the cross-cluster transitioning traffic across the data center. Large-scale simulation and Alibaba Cloud ECS experiments show that ClusterRT reduces 94.0-96.2% of transitioning traffic and reduces 70.4-88.4% of transitioning time.

Slope multi-step excavation displacement prediction surrogate model based on a long short-term memory neural network: for small sample data and multi-feature multi-task learning

ABSTRACT Machine learning-based surrogate models have become the preferred approach for large-scale and frequent simulation tasks due to its significant improvement in computational efficiency. In order to overcome the potential effects of learning with small sample data and the challenges of multi-feature multi-task learning, we developed a novel deep learning long short-term memory (LSTM) model. Taking slope excavation displacement prediction as a case study, we employed the Latin hypercube sampling method to generate a synthetic dataset for training LSTM and other mainstream models. Experimental results demonstrate that the model's prediction accuracy decreases with a reduction in sample size, while support vector regression (SVR), back propagation neural network (BPNN), LSTM and Gaussian process regression (GPR) demonstrate a stronger resistance. It is feasible to utilise excavation features as model inputs to establish a unified multi-step excavation model, but the accuracy of the SVR model decreased by 32.5% after supplementing excavation features. Even when the sample size is less than 50, both LSTM and GPR exhibit excellent performance, achieving model R-squared and RMSE surpassing 0.99 and 0.07 mm. However, when addressing multi-output learning tasks, LSTM stands out as the optimal choice. This study will assist researchers or engineers in swiftly selecting appropriate surrogate models.

Lindblad dynamics from spatio-temporal correlation functions in nonintegrable spin- 1/2 chains with different boundary conditions

We investigate the Lindblad equation in the context of boundary-driven magnetization transport in spin-1/2 chains. Our central question is whether the nonequilibrium steady state of the open system, including its buildup in time, can be described on the basis of the dynamics in the closed system. To this end, we rely on a previous study [Heitmann , ], in which a description in terms of spatio-temporal correlation functions was suggested in the case of weak driving and small system-bath coupling. Because this work focused on integrable systems and periodic boundary conditions, we here extend the analysis in three directions: (1) We consider nonintegrable systems, (2) we take into account open boundary conditions and other bath-coupling geometries, and (3) we provide a comparison to time-evolving block decimation. While we find that nonintegrability plays a minor role, the choice of the specific boundary conditions can be crucial due to potentially nondecaying edge modes. Our large-scale numerical simulations suggest that a description based on closed-system correlation functions is a useful alternative to already existing state-of-the-art approaches. Published by the American Physical Society 2024

Rational Design of Dual-Functionalized Gd@C82 Nanoparticles to Relieve Neuronal Cytotoxicity in Alzheimer's Disease via Inhibition of Aβ Aggregation.

The accumulation of amyloid-β (Aβ) peptides is a major hallmark of Alzheimer's disease (AD) and plays a crucial role in its pathogenesis. Particularly, the structured oligomeric species rich in β-sheet formations were implicated in neuronal organelle damage. Addressing this formidable challenge requires identifying candidates capable of inhibiting peptide aggregation or disaggregating preformed oligomers for effective antiaggregation-based AD therapy. Here, we present a dual-functional nanoinhibitor meticulously designed to target the aggregation driving force and amyloid fibril spatial structure. Leveraging the exceptional structural stability and facile tailoring capability of endohedral metallofullerene Gd@C82, we introduce desired hydrogen-binding sites and charged groups, which are abundant on its surface for specific designs. Impressively, these designs endow the resultant functionalized-Gd@C82 nanoparticles (f-Gd@C82 NPs) with high capability of redirecting peptide self-assembly toward disordered, off-pathway species, obstructing the early growth of protofibrils, and disaggregating the preformed well-ordered protofibrils or even mature Aβ fibrils. This results in considerable alleviation of Aβ peptide-induced neuronal cytotoxicity, rescuing neuronal death and synaptic loss in primary neuron models. Notably, these modifications significantly improved the dispersibility of f-Gd@C82 NPs, thus substantially enhancing its bioavailability. Moreover, f-Gd@C82 NPs demonstrate excellent cytocompatibility with various cell lines and possess the ability to penetrate the blood-brain barrier in mice. Large-scale molecular dynamics simulations illuminate the inhibition and disaggregation mechanisms. Our design successfully overcomes the limitations of other nanocandidates, which often overly rely on hydrophobic interactions or photothermal conversion properties, and offers a viable direction for developing anti-AD agents through the inhibition and even reversal of Aβ aggregation.

The origin of phase separation in binary aluminosilicate glasses

The quest for hard and tough transparent oxide glasses is at the core of glass science and technology. Aluminosilicate glasses exhibiting nanoscale phase separation emerge as promising candidates for such materials. Nevertheless, proper control of the phase separation represents a daunting challenge due to its elusive origins. Here we employ large-scale molecular dynamics simulations and structural analysis to unravel the underlying mechanisms of the phase separation in aluminosilicate. The observed phase separation originates from an arrangement of SiO4 and AlOn polyhedra, which manifests from the second coordination shell and extends to higher shells. This specific arrangement is driven by repulsion between the polyhedra, reaching its maximum at around 50 mol% of Al2O3. This behavior becomes pronounced around and below the glass transition temperature. This work sheds light on the origin of phase separation and provides a route for further exploration across other compositions to develop glasses with adapted mechanical performance.

Microswimmers Knead Nematics into Cholesterics.

The hydrodynamic stresses created by active particles can destabilize orientational order present in the system. This is manifested, for example, by the appearance of a bend instability in active nematics or in quasi-two-dimensional living liquid crystals consisting of swimming bacteria in thin nematic films. Using large-scale hydrodynamics simulations, we study a system consisting of spherical microswimmers within a three-dimensional nematic liquid crystal. We observe a spontaneous chiral symmetry breaking, where the uniform nematic state is kneaded into a continuously twisting state, corresponding to a helical director configuration akin to a cholesteric liquid crystal. The transition arises from the hydrodynamic coupling between the liquid crystalline elasticity and the swimmer flow fields, leading to a twist-bend instability of the nematic order. It is observed for both pusher (extensile) and puller (contractile) swimmers. Further, we show that the liquid crystal director and particle trajectories are connected: in the cholesteric state the particle trajectories become helicoidal.

LAPS: An MPI-parallelized 3D pseudo-spectral Hall-MHD simulation code incorporating the expanding box model

Numerical simulations have been an increasingly important tool in space physics. Here, we introduce an open-source three-dimensional compressible Hall-Magnetohydrodynamic (MHD) simulation code LAPS (UCLA-Pseudo-Spectral, https://github.com/chenshihelio/LAPS). The code adopts a pseudo-spectral method based on Fourier Transform to evaluate spatial derivatives, and third-order explicit Runge-Kutta method for time advancement. It is parallelized using Message-Passing-Interface (MPI) with a “pencil” parallelization strategy and has very high scalability. The Expanding-Box-Model is implemented to incorporate spherical expansion effects of the solar wind. We carry out test simulations based on four classic (Hall)-MHD processes, namely, 1) incompressible Hall-MHD waves, 2) incompressible tearing mode instability, 3) Orszag-Tang vortex, and 4) parametric decay instability. The test results agree perfectly with theory predictions and results of previous studies. Given all its features, LAPS is a powerful tool for large-scale simulations of solar wind turbulence as well as other MHD and Hall-MHD processes happening in space.

Understanding anti-corrosion mechanisms of the 2,6-dithiopurine (DTP) inhibitor molecule on iron surfaces

Purine derivatives present a high efficiency for anti-corrosion purposes while the nanoscale mechanisms are not yet fully understood. In this work, the inhibition mechanism of the 2,6-dithiopurine (DTP) molecule was investigated using advanced characterizations and atomic simulations. We revealed that the DTP molecule undergoes a multi-layer adsorption mode and is governed by both chemisorption and physisorption processes. An organometallic layer at a thickness of ∼3.8 nm was captured using TEM, comprised of Fe-N and Fe-S bonds strongly anchored to the iron interfaces, as confirmed by the XPS analysis. Our quantum chemical calculations showed that heteroatoms such as N or S in the DTP molecule offer reactive sites that facilitate its chemisorption onto iron surfaces, to form metal-organic bonds (specifically, Fe-N and Fe-S) at the DTP-iron interfaces. We further employed molecular dynamics simulations to investigate the interaction between DTP molecule and various substrates. Our findings suggest that the hydroxyl groups on the lepidocrocite surface can significantly promote bonding between DTP and iron surfaces. Based on large-scale molecular dynamics simulations, we discovered that DTP can exhibit a strong binding affinity for aggressive ions, leading to a considerable decrease in the self-diffusion coefficient of hydronium ions of up to 70 %. This work provides a new understanding of anti-corrosion mechanisms and offers opportunities to design new corrosion inhibitors possessing multiple reactive sites and strong bonding with the substrate.