Simulation Of Systems Research Articles

Probing the range of applicability of structure- and energy-adjusted QM/MM link bonds III: QM/MM MD simulations of solid-state systems at the example of layered carbon structures.

The previously introduced workflow to achieve an energetically and structurally optimized description of frontier bonds in quantum mechanical/molecular mechanics (QM/MM)-type applications was extended into the regime of computational material sciences at the example of a layered carbon model systems. Optimized QM/MM link bond parameters at HSEsol/6-311G(d,p) and self-consistent density functional tight binding (SCC-DFTB) were derived for graphitic systems, enabling detailed investigation of specific structure motifs occurring in graphene-derived structures quantum-chemical calculations. Exemplary molecular dynamics (MD) simulations in the isochoric-isothermic (NVT) ensemble were carried out to study the intercalation of lithium and the properties of the Stone-Thrower-Wales defect. The diffusivity of lithium as well as hydrogen and proton adsorption on a defective graphene surface served as additional example. The results of the QM/MM MD simulations provide detailed insight into the applicability of the employed link-bond strategy when studying intercalation and adsorption properties of graphitic materials.

An effective Hamiltonian for the simulation of open quantum molecular systems

Abstract We discuss the derivation of an effective Hamiltonian for open quantum many-particle systems. The aim is to define an operator that can be used for (molecular) simulations where, through the exchange of energy and matter with the surrounding environment (reservoir), the number of particles, n, becomes a variable of the problem.
 The Hamiltonian is formally derived from the von Neumann equation; specifically, we derive an n-hierarchy of equations for the density matrix, rho_{n}, for near equilibrium situations. Such a hierarchy, in case of stationary equilibrium, delivers the standard grand canonical density matrix as it would be expected. We report that a similar Hamiltonian was conjectured, from empirical considerations, in the field of superconductivity. Thus our result also provide a formal basis for this long-standing hypothesis. 
Finally, an application is discussed for Path Integral simulations of molecular systems

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Design, modeling, and simulation of a PV/diesel/battery hybrid energy system for an off-grid hospital in Ethiopia

In the pursuit of sustainable energy solutions, off-grid hybrid systems have emerged as a promising avenue, catering to the electrification needs of rural areas. These systems encompass a multifaceted approach, addressing concerns of reliability, sustainability, and environmental preservation. Leveraging advanced tools such as HOMER modeling, the design and simulation of hybrid off-grid systems, alongside the evaluation of existing diesel generator (DG) power supply, have become imperative. This paper embarks on a comprehensive exploration with the overarching objective of designing, modeling, and simulating an off-grid power system tailored for the Shinshicho Primary Hospital. Nestled in the heart of Shinshicho Town within the Kembata Tembaro Zone of Ethiopia, this healthcare facility stands as a focal point for community well-being. The proposed hybrid system integrates solar PV, diesel generators, and battery storage, offering a robust and resilient energy solution. Throughout the optimization process, a primary load demand of 276 kgwatt-hours per day and a peak load of 40 kW were pivotal considerations. The financial cost of this hybrid system results in an initial capital requirement of $160,500, complemented by operational and maintenance costs amounting to $14,824. Over the projected 20-year lifespan, the total net present cost (NPC) is estimated at $216,155. With these economic parameters in mind, the cost-effectiveness of the selected hybrid system is underscored by a cost of energy (CEO) of 0.187 dollars per kilowatt-hour. This metric not only highlights the economic viability of the proposed solution but also positions it as a sustainable and financially prudent choice for meeting the energy needs of Shinshicho Primary Hospital.

Efficient learning of accurate surrogates for simulations of complex systems

Efficient learning of accurate surrogates for simulations of complex systems

Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

SummaryThis research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments

Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Dynamics and Energetics of Ion Adsorption at the Interface between a Pure Ionic Liquid and Carbon Electrodes.

Molecular dynamics simulations have been used extensively to determine equilibrium properties of the electrode-electrolyte interface in supercapacitors held at various potentials. While such studies are essential to understand and optimize the performance of such energy storage systems, investigation of the dynamics of adsorption during the charge of the supercapacitors is also necessary. Dynamical properties are especially important to get an insight into the power density of supercapacitors, one of their main assets. In this work, we propose a new method to coarse-grain simulations of all-atom systems and compute effective Lennard-Jones and Coulomb parameters, allowing subsequently to analyze the trajectories of adsorbing ions. We focus on pure 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide in contact with planar carbon electrodes. We characterize the evolution of the ion orientation and ion-electrode distance during adsorption and show that ions reorientate as they adsorb. We then determine the forces experienced by the adsorbing ions and demonstrate that Coulomb forces are dominant at a long range while van der Waals forces are dominant at a short range. We also show that there is an almost equal contribution from the two forces at an intermediate distance, explaining the peak of ion density close to the electrode surface.

Structural and Computational Insights into Dynamics and Intermediate States of Orexin 2 Receptor Signaling.

Orexin 2 receptor (OX2R) is a G protein-coupled receptor (GPCR) whose activation is crucial to regulation of the sleep-wake cycle. Recently, inactive and active state structures were determined from X-ray crystallography and cryo-electron microscopy single particle analysis, and the activation mechanisms have been discussed based on these static data. GPCRs have multiscale intermediate states during activation, and insights into these dynamics and intermediate states may aid the precise control of intracellular signaling by ligands in drug discovery. Molecular dynamics (MD) simulations are used to investigate dynamics induced in response to thermal perturbations, such as structural fluctuations of main and side chains. In this study, we proposed collective motions of the TM domain during activation by performing 30 independent microsecond-scale MD simulations for various OX2R systems and applying relaxation mode analysis. The analysis results suggested that TM3 had a vertical structural movement relative to the membrane surface during activation. In addition, we extracted three characteristic amino acid residues on TM3, i.e., Q1343.32, V1423.40, and R1523.50, which exhibited large conformational fluctuations. We quantitatively evaluated the changes in their equilibrium during activation in relation to the movement of TM3. We also discuss the regulation of ligand binding recognition and intracellular signal selectivity by changes in the equilibrium of Q1343.32 and R1523.50, respectively, according to MD simulations and GPCR database. Additionally, the OX2R-Gi signaling complex is stabilized in the conformation resembling a non-canonical (NC) state, which was previously proposed as an intermediate state during activation of neurotensin 1 receptor. Insights into the dynamics and intermediate states during activation gained from this study may be useful for developing biased agonists for OX2R.

Nanofabrication, characterisation and modelling of soft-in-hard FeCo–FePt magnetic nanocomposites

Advanced nanofabrication exploiting e-beam lithography has been used to prepare nanocomposites consisting of periodic arrays of soft magnetic FeCo-based nano-inclusions of variable dimensions, embedded in a hard magnetic FePt matrix. Nanocomposites with non-magnetic (Pt) inclusions and single phase hard magnetic FePt microstructures were prepared as reference samples. The formation of Kirkendall voids in the soft-in-hard nanocomposites, because of annealing-induced diffusion, has been identified through high resolution imaging and chemical analysis. Replication of the μm-scaled nanocomposite and hard magnetic units over mm-scaled surfaces allowed global magnetic characterisation of magnetisation reversal using standard magnetometry. Magnetic force microscopy imaging was used for spatially resolved magnetic characterisation in different remanent magnetic states. Both of these experimental techniques were used to study the influence of the size, volume content and nature of the nano-inclusions on magnetisation reversal. The well-defined geometry and nano-scaled size of the inclusions render these nanocomposites as model systems for micromagnetic simulations and very good agreement was achieved between measured and simulated magnetisation reversal curves. This accordance motivates future in-silico optimisation of such soft-in-hard nanocomposites, which may serve as model systems to guide the design of new high performance bulk magnets which are less dependent on critical elements.

A physics and data co-driven surrogate modeling method for high-dimensional rare event simulation

This paper presents a physics and data co-driven surrogate modeling method for efficient rare event simulation of civil and mechanical systems with high-dimensional input uncertainties. The method fuses interpretable low-fidelity physical models with data-driven error corrections. The hypothesis is that a well-designed and well-trained simplified physical model can preserve salient features of the original model, while data-fitting techniques can fill the remaining gaps between the surrogate and original model predictions. The coupled physics-data-driven surrogate model is adaptively trained using active learning, aiming to achieve a high correlation and small bias between the surrogate and original model responses in the critical parametric region of a rare event. A final importance sampling step is introduced to correct the surrogate model-based probability estimations. Static and dynamic problems with input uncertainties modeled by random field and stochastic process are studied to demonstrate the proposed method.

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

Coarse-grained molecular dynamics model of AB diblock copolymers composed of blocks with different Lennard-Jones parameters forming lamellar structures

In thermoplastic elastomers, the structure formation of hard and soft domains and the mechanical response to deformation remain vital areas of interest. To realize the coarse-grained molecular dynamics (CGMD) simulations of systems incorporating the difference in glass transition temperature Tg between domains, we studied AB diblock copolymers (di-BCPs) with asymmetric Lennard-Jones (LJ) parameters (the strength coefficient εLJ and cutoff length rc). For instance, hard and soft domains were realized by assigning the cutoff lengths of each segment to rc,(A,A) = 2.5 (high Tg) and rc,(B,B) = 1.5 (low Tg), respectively. To identify how the cutoff length rc,(B,B) corresponds to the strength coefficient ε(B,B) in the phase separation behavior against the fixed LJ parameters for the A block (rc,(A,A) = 2.5 and ε(A,A) = 1.0), we focused on lamellar structures in which the A and B blocks possess equal segment numbers. For the asymmetric cases with rc,(B,B) < rc,(A,A), we estimated the effective ε(B,B)' on the basis of the DA/DB ratio of the partial domain spacings. We confirmed that the scaling relations (D ∼ Δε1/6, D/N0.7 ∼ Δε, and D/N1/2 ∼ ΔεN) hold, where ε(A,A) = 1, Δε = ((1 + ε(B,B)')/2 – ε(A,B)), and N denotes the number of segments per AB di-BCP chain. Therefore, this work will provide valuable insights for guiding applied research on BCPs.

Wishful Thinking about Consciousness

We contrast three very distinct mathematical approaches to the hard problem of consciousness: quantum consciousness, integrated information theory, and the very large-scale dynamical systems simulation of a network of networks. We highlight their features and their associated hypotheses, and we discuss how they are aligned or in conflict. We suggest some challenges to these theories, in considering how they might apply to the human brain as it develops both cognitive and conscious sophistication, from infancy to adulthood. We indicate how an evolutionary perspective challenges the distinct approaches to aver performance advantages and physiological surrogates for consciousness.

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials.

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate abinitio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces.

In-flight anti-icing simulation of electrothermal ice protection systems with inhomogeneous thermal boundary condition

Conventional anti-icing computational solvers calculate the convective heat transfer coefficient using a homogeneous thermal boundary condition, assuming a constant temperature across the surface. However, this approach can lead to inaccuracies in regions with significant temperature variations. To address this limitation, the present study proposes a novel approach that utilizes an inhomogeneous thermal boundary condition, which updates the convective heat transfer coefficient based on the transient wall temperature distribution. We developed a unified finite volume framework that efficiently integrates various in-house solvers, including a compressible Navier-Stokes-Fourier (NSF) airflow solver, a Eulerian droplet impingement solver, a PDE-based ice solver, and a multilayer heat conduction solver. Thermal interaction between the different solvers was modeled using the conjugate heat transfer method. The effects of updating airflow on anti-icing results were investigated by comparing the results obtained by coupled and decoupled solvers. While the decoupled solver computes airflow once and remains unchanged, the coupled solver updates the airflow during the anti-icing simulation. Our findings show that the decoupled solver has the highest deviations from the coupled solver in evaporative anti-icing regimes, where dry regions form within the protection limits. Using coupled solvers in designing ice protection systems in evaporative regimes improves temperature prediction accuracy, enabling designers to reduce safety factors and save energy.

Carrier transport simulations in twisted bilayer and turbostratic multilayer graphene systems

The effects of inserting a twisted bilayer graphene (tBLG) between turbostratic graphene layers and a SiO2 substrate on the transport properties are investigated, to explore the possibility that using a tBLG could be an effective way to screen the potential fluctuations due to the impurities on the substrate. The Fermi velocity in a tBLG changes from that of the pristine graphene depending on the twist angle. In the present study, a parameter α is introduced, which is defined as the ratio of the Fermi velocity in the tBLG to that of pristine graphene, and the transport properties are calculated as a function of α. The self-consistent calculation of the Poisson equation with the Thomas–Fermi approximation is performed for multilayer graphene systems consisting of a tBLG and turbostratic graphene layers, and the potential profiles are incorporated into a Monte Carlo simulator to calculate the drift velocities and mobilities. It is shown that the transport properties of the whole system strongly depend on the parameter α and the number of layers in the system.

Thermal measurements of boiling granular films.

This study focused on two familiar items in our daily lives: grains and boiling water. Accurately measuring boiling dense granular beds requires specialized instruments and novel techniques. An annular vertical riser was used to boil granular films. The riser was filled with glass beads and then immersed in liquid water. The water is heated by inducing eddy currents at the base of the riser, which is composed of carbon steel. Beyond the critical electric current, Ic, the Lyapunov exponent of the time series of the measured temperature distributions within the granular films became positive. This indicates the chaotic behavior of the films. Slightly above Ic, patterned instabilities with affine fractal-like structures were observed in the boiling granular films. Chaotic boiling granular films manifested percolation and transition to turbulence. Computer simulations of the above system under the guidance of generative AI (GenAI) successfully reproduced many critical features of the experimental observations, which can be considered a significant breakthrough in the modeling and simulation of complex systems. With this success in utilizing GenAI, more accurate instruments can be developed, resulting in the world of tomorrow rapidly becoming today's world.