Large System Sizes Research Articles

Managing singular kernels and logarithmic corrections in the staggered six-vertex model

In this paper, we investigate the spectral properties of the staggered six-vertex model with Z2 symmetry for arbitrary system sizes L using non-linear integral equations (NLIEs). Our study is motivated by two key questions: what is the accuracy of results based on the ODE/IQFT correspondence in the asymptotic regime of large system sizes, and what is the optimal approach based on NLIE for analyzing the staggered six-vertex model?We demonstrate that the quantization conditions for low-lying primary and descendant states, derived from the ODE/IQFT approach in the scaling limit, are impressively accurate even for relatively small system sizes. Specifically, in the anisotropy parameter range π/4 < γ < π/2, the difference between NLIE and ODE/IQFT results for energy and quasi-momentum eigenvalues is of order OL−2.Furthermore, we present a unifying framework for NLIEs, distinguishing between versions with singular and regular kernels. We provide a compact derivation of NLIE with a singular kernel, followed by an equivalent set with a regular kernel. We address the stability issues in numerical treatments and offer solutions to achieve high-accuracy results, validating our approach for system sizes ranging from L = 2 to L = 1024.Our findings not only validate the ODE/IQFT approach for finite system sizes but also enhance the understanding of NLIEs in the context of the staggered six-vertex model. We hope the insights gained from this study have significant implications for resolving the spectral problem of other lattice systems with emergent non-compact degrees of freedom and provide a foundation for future research in this domain.

Incorporating static intersite correlation effects in vanadium dioxide through DFT+V

We analyze the effects on the structural and electronic properties of vanadium dioxide (VO2) due to adding an empirical interatomic potential within the density-functional theory+V (DFT+V) framework. We use the DFT+V machinery founded on the extended Hubbard model to apply an empirical self-energy correction between nearest-neighbor vanadium atoms in both rutile and monoclinic phases, and for a set of structures interpolating between these two cases. We observe that imposing an explicit intersite interaction V along the vanadium–vanadium chains enhances the characteristic bonding-antibonding splitting of the relevant bands in the monoclinic phase, thus favoring electronic dimerization and the formation of a bandgap. We then explore the effect of V on the structural properties and the relative energies of the two phases, finding an insulating global energy minimum for the monoclinic phase, consistent with experimental observations. With increasing V, this minimum becomes deeper relative to the rutile structure, and the transition from the metallic to the insulating state becomes sharper. We also analyze the effect of applying the +V correction either to all or only to selected vanadium–vanadium pairs and both in the monoclinic as well as the metallic rutile phase. Our results suggest that DFT+V can indeed serve as a computationally inexpensive unbiased way of modeling VO2 which is well suited for studies that, e.g., require large system sizes. Published by the American Physical Society 2024

Modeling martensitic transformation temperatures in Zirconia–Ceria solid solutions using machine learning interatomic potentials

Abstract Shape memory ceramics (SMCs), while exhibiting high strength, sizeable recoverable strain, and substantial energy damping, tend to shatter under load and have low reversibility. Recent developments in SMCs have shown significant promise in enhancing the reversibility of the shape memory phase transformation by tuning the lattice parameters and transformation temperatures through alloying. While first-principles methods, such as density functional theory (DFT), can predict the lattice parameters and enthalpy at zero Kelvin, calculating the transformation temperature from free energy at high temperatures is impractical. Empirical potentials can calculate transformation temperatures efficiently for large system sizes but lack compositional transferability. In this work, we develop a model to predict transformation temperatures and lattice parameters for the Zirconia–Ceria solid solutions. We construct a machine learning inter-atomic potential (MLIAP) using an initial dataset of DFT simulations, which is then iteratively expanded using active learning. We utilize reversible scaling to compute the free energy as a function of composition and temperature, from which the transformation temperatures are determined. These transformation temperatures match experimental trends and accurately predict the phase boundary. Finally, we compare other relevant design parameters (e.g. transformation volume change) to demonstrate the applicability of MLIAPs in designing SMCs.

Polarization-selective dual-band infrared metamaterial perfect absorber

Abstract Metamaterial absorbers have significant effects and great potential for applications in a variety of important sciences and technologies, including infrared imaging, thermal emitters, electromagnetic shielding, and photodetectors. However, conventional absorbers with a single function are difficult to meet the growing demand of devices, especially for the situations where the absorption and polarization are simultaneously required in infrared detection. If the polarization absorption function is realized by integrating absorption and polarization elements, it will lead to a large overall system size and slow response time. To solve this problem, we propose a dual-band polarization-selective metamaterial perfect absorber, whose maximum absorptions at resonant wavelengths of λ 1 = 2.824 μ m and λ 2 = 7.622 μ m are 99.99% and 99.88%, respectively. The absorber exhibits strong polarization sensitivity, absorbing TM-polarized incident light and reflecting TE-polarized incident light at the two resonant wavelengths. The extinction ratios of the absorber at the two referred resonant wavelengths are 90.9 and 142.7, respectively. The proposed absorber would be beneficial for infrared polarization detection and imaging.

Dynamical signatures of discontinuous phase transitions: How phase coexistence determines exponential versus power-law scaling.

There are conflicting reports in the literature regarding the finite-size scaling of the Liouvillian gap and dynamical fluctuations at discontinuous phase transitions, with various studies reporting either exponential or power-law behavior. We clarify this issue by employing large deviation theory. We distinguish two distinct classes of discontinuous phase transitions that have different dynamical properties. The first class is associated with phase coexistence, i.e., the presence of multiple stable attractors of the system dynamics (e.g., local minima of the free-energy functional) in a finite phase diagram region around the phase transition point. In that case, one observes asymptotic exponential scaling related to stochastic switching between attractors (though the onset of exponential scaling may sometimes occur for very large system sizes). In the second class, there is no phase coexistence away from the phase transition point, while at the phase transition point itself there are infinitely many attractors. In that case, one observes power-law scaling related to the diffusive nature of the system relaxation to the stationary state.

Modeling of excitation dynamics in large-size molecular systems: Hierarchical equations with compartmentalization.

We describe the new method that can be useful for calculation of the excitation dynamics in large molecular arrays that can be split into compartments with weak exciton coupling between them. In this method, the dynamics within each compartment is evaluated nonperturbatively using hierarchical equations of motion (HEOM), whereas transfers between the exciton states belonging to different compartments are treated by the generalized Förster (gF) theory. In a combined HEOM-gF approach, the number of equations increases linearly when adding new compartments as opposed to pure HEOM, where a depth of hierarchy exhibits strong non-linear grows when scaling the total number of molecules. Comparing the combined HEOM-gF method with an exact HEOM solution enabled us to estimate the parameters corresponding to a validity range of the proposed theory. The possibility of using the method for modeling of energy transfers in photosynthetic antenna supercomplexes is discussed.

Renal Cell Carcinoma with Venous Tumor Thrombus: 15 Years of Experience in an Oncology Center.

Background: The purpose of this study is to report the experience of a single Portuguese oncology center in the management of patients with renal cell carcinoma (RCC) and venous tumor thrombus (VTT). Methods: This is a retrospective analysis of all patients with RCC and VTT surgically treated in our center between 2008 and 2023. Only patients with VTT up to level III (Mayo Clinic classification) were included. Patient, tumor characteristics and peri-operative outcome data were registered. Administration of systemic therapy was performed upon progression. Survival analysis was conducted with the collected data. Results: A total of 64 patients (n = 16 women) were included in this study. The mean age at diagnosis was 66.3 ± 10.7 years old. The VTT level was 0, I, II and III in 40 (62.5%), 12 (18.7%), 6 (9.4%) and 6 (9.4%) patients, respectively. Nine patients (14.1%) had distant metastasis at diagnosis. No peri-operative deaths occurred, and the major complication rate was 3.1%. Histology revealed 98.4% of clear cell RCC, with sarcomatoid differentiation present in 12.5% of the cases. A negative margin status was achieved in 54 (84.4%) patients. Systemic therapy was administered in 24 (37.5%) patients during follow-up. The median progression-free (PFS), cancer-specific (CSS) and overall (OS) survival were 23, 60 and 48 months, respectively. In multivariable analysis, significant predictors of CSS were tumor size, sarcomatoid differentiation and collecting system invasion. Conclusions: Radical nephrectomy with VTT excision up to level III is a feasible and safe procedure. Patients with large tumor size, sarcomatoid differentiation and collecting system invasion are at the highest risk and should be closely monitored.

Self-powered flexible sensor network for continuous monitoring of crop micro-environment and growth states

Monitoring crops’ micro-environment and growth states is significant for developing smart agriculture. However, the traditional rigid and large-sized sensor systems cannot meet the demands of precision agriculture for multi-parameter and high-throughput monitoring. Here, we integrate a flexible sensor system with low-power communication techniques, designing a two-layer star topology sensor network based on BLE and NB-IoT protocol. The sensors are arranged on flexible substrates, allowing them to closely adhere to various parts of crops, such as stems and leaves. To ensure the accuracy of the sensors, they are evaluated and calibrated in the laboratory. A solar energy harvesting system is designed for the sensor network to make it self-powered and successfully measure temperature, humidity, illumination, and leaf angles stably, demonstrating the system’s feasibility. The deployment cost of the sensor network is significantly lower than that of traditional automatic weather stations, showcasing its potential for widespread application in farmlands.

Stochastic Resolution of Identity to CC2 for Large Systems: Ground State and Triplet Excitation Energy Calculations.

An implementation of stochastic resolution of identity to the CC2 (sRI-CC2) ground state energy followed by triplet excitation energy calculations is presented. A set of stochastic orbitals is introduced to further decouple the expensive 4-index electron repulsion integrals on the basis of RI approximation. A Laplace transformation of the orbital energy difference denominators into numerical summations is adopted to obtain a third-order overall scaling. We select a series of hydrogen dimer chains with nearly thousands of electrons, as well as some other molecules, for sRI-CC2 energies and test the accuracy and time consumption in comparison with those of RI-CC2. Our sRI-CC2 results reproduce a modest agreement with the RI-CC2 in Q-Chem program package and it allows a steep scaling reduction from O(N5) to O(N3). Besides, the unrestricted sRI-CC2 calculations also fit well with the restricted results. Thus, our sRI-CC2 implementation of ground state energy and triplet excitation energy provides a cost-efficient alternative approach, especially for some large-sized systems.

Flow patterns and energy spectra in forced quasi-two-dimensional turbulence: effect of system size and damping rate

An experimental investigation is conducted to study the flow patterns, spectral properties and energy fluxes in thin-layer turbulence with varying system sizes and damping rates. It is found that although a system-size vortex (an indicator of spectral condensation) occurs for small system sizes and does not for large ones, the spectra for different system sizes consistently exhibit a scaling close to $k^{-3}$ in inverse cascade (another indicator of spectral condensation). On the other hand, under a fixed system size larger than the friction-dominated length scale, the energy spectrum in the inverse cascade range changes from $k^{-3}$ to $k^{-5/3}$ as the damping rate increases, suggesting that the friction-dominated length scale may not be a suitable parameter for predicting spectral transition. At lower damping rates and large system sizes, turbulent structures grow larger via inverse cascade, manifesting as long streamers, and the small-scale vortices are suppressed. This suppression leads to a reduction of energy flux at intermediate scales and a change in the spectral shape. The dimensionless Taylor microscale is found to exhibit a monotonic dependence on the damping rate. With the reduction in the damping rate, the Taylor microscale increases to become comparable with the forcing scale, and the spectrum in inverse cascade transits to a steeper scaling, $k^{-3}$ , indicating that the dimensionless Taylor microscale may be used as a diagnostic parameter for spectral transition.

Log-Based Fault Localization with Unsupervised Log Segmentation

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared with the traditional timestamp approach. We demonstrate that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces.

Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate-metal interactions. Next, for the water-water and water-adsorbate interactions, we used established classical force fields. Finally, for the water-Ru surface interaction, for which no reliable force fields have been published, we used Behler-Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Melting and freezing of a skyrmion lattice

We report comprehensive Monte-Carlo studies of the melting of skyrmion lattices (SkL) in systems of small, medium, and large sizes with the number of skyrmions ranging from 103to over 105. Large systems exhibit hysteresis similar to that observed in real experiments on the melting of SkLs. For sufficiently small systems which achieve thermal equilibrium, a fully reversible sharp solid-liquid transition on temperature with no intermediate hexatic phase is observed. A similar behavior is found on changing the magnetic field that provides the control of pressure in the SkL. We find that on heating the melting transition occurs via a formation of grains with different orientations of hexagonal axes. On cooling, the fluctuating grains coalesce into larger clusters until a uniform orientation of hexagonal axes is slowly established. The observed scenario is caused by collective effects involving defects and is more complex than a simple picture of a transition driven by the unbinding and annihilation of dislocation and disclination pairs.

An accurate and transferable machine learning interatomic potential for nickel

Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a “magnetism-hidden” machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The DP-Ni model exhibits excellent transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The high accurate DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex phase transformation behavior and may serve as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

Injection Process of Pickup Ion Acceleration at an Oblique Heliospheric Termination Shock

The injection process of pickup ion acceleration at a heliospheric termination shock is investigated. Using two-dimensional fully kinetic particle-in-cell simulation, accelerated pickup ions are self-consistently reproduced by tracking long time evolution of shocks with an unprecedentedly large system size in the shock normal direction. Reflected pickup ions drive upstream large-amplitude waves through resonant instabilities. Convection of the large-amplitude waves causes shock surface reformation and alters the downstream electromagnetic structure. A part of pickup ions are accelerated to tens of upstream flow energy in the timescale of ∼100 times inverse ion gyrofrequency. The initial acceleration occurs through the shock surfing acceleration (SSA) mechanism followed by the shock drift acceleration mechanism. Large electrostatic potential accompanied by the upstream waves enables the SSA to occur.

High-Fidelity, Multiqubit Generalized Measurements with Dynamic Circuits

Generalized measurements, also called positive operator-valued measures (POVMs), can offer advantages over projective measurements in various quantum information tasks. Here, we realize a generalized measurement of one and two superconducting qubits with high fidelity and in a single experimental setting. To do so, we propose a hybrid method, the “Naimark-terminated binary tree,” based on a hybridization of Naimark’s dilation and binary tree techniques that leverages emerging hardware capabilities for midcircuit measurements and feed-forward control. Furthermore, we showcase a highly effective use of approximate compiling to enhance POVM fidelity in noisy conditions. We argue that our hybrid method scales better toward larger system sizes than its constituent methods and demonstrate its advantage by performing detector tomography of symmetric, informationally complete POVM (SIC POVM). Detector fidelity is further improved through a composite error-mitigation strategy that incorporates twirling and a newly devised conditional readout error mitigation. Looking forward, we expect improvements in approximate compilation and hardware noise for dynamic circuits to enable generalized measurements of larger multiqubit POVMs on superconducting qubits. Published by the American Physical Society 2024

Quantitative evaluation method for post-earthquake functionality loss of pedestrian flow pathways within a complex building using evacuation and rescue simulation

The complex building is a single building, characterized by its large size and complicated structural systems, such as high-rise-buildings, long-span-podium, and large underground space, as well as diverse functions. The pedestrian flow pathways within a complex building play a crucial role in urgent evacuation and rescue following seismic events. This study proposed a quantitative method for evaluating the functionality loss of pedestrian flow pathways within the complex building following seismic events. The method performed the component fragility analysis and constructed the speed reduction models that considered the variations of occupants' motions under different damage states of various nonstructural components. To evaluate the functionality loss of pedestrian flow pathways, the evacuation and rescue scenarios were established by analyzing the maximum speeds and behavioral delays of occupants at different spatial locations within the post-earthquake complex building. Based on the simulation, a quantitative index was constructed. Finally, a process accounting for uncertainties such as component damage was presented by generating the engineering demand parameters. A case study was employed to demonstrate the functionality loss of pedestrian flow pathways on each floor and in different areas within the post-earthquake complex building, which has reference significance for improving the post-earthquake complex building 's evacuation capacity as soon as possible to deal with secondary risk.

Scalable and efficient digital twins for model-based design of cyber-physical systems

ABSTRACT The optimised design, operation and management of complex, large-size Cyber Physical Systems (CPSs) – like modern manufacturing and logistic assets – calls for Digital Twins (DTs) in which dynamic modelling and simulation play a relevant role. In such simulation-based DTs computational efficiency is crucial. According to the present state of the art, the said efficiency is hindered by the way the Cyber part of a CPS is represented, which in the manufacturing case practically corresponds to representing digital controls. This paper proposes a modelling framework for an efficient and scalable representation of the Cyber part in a DT of a CPS. The presented solution is based on Object-Oriented Modelling (OOM) and IEC industrial standards. Motivating and explanatory examples are provided, and a proof-of-concept case study is discussed to support the framework potential and industrial viability.

Ultraviolet color image sensor based on CsPbBr3 inorganic perovskite nanocrystal film.

Ultraviolet and color imaging require different image sensors and optical channels, which results in large size, complex structure, and high cost of imaging systems. Here, we report a novel, to the best of our knowledge, image sensor that combines ultraviolet and color imaging functions. The fabrication of this image sensor is achieved by coating high-transparency CsPbBr3 perovskite nanocrystals in a polymer film on the color filter layer of a silicon-based detector. The film, serving as an ultraviolet photoluminescent layer, exhibits high transparency, exceeding 91.5% at wavelengths beyond the photoluminescence peak of 513 nm. During ultraviolet imaging, the film converts ultraviolet light into visible light, which passes through the green filter layer to reach the detector for imaging. During visible light imaging, red light, green light, and most of the blue light pass through the CsPbBr3 perovskite nanocrystal film and color filter layer to reach the detector for imaging. As a result, the image sensor can capture both 257 nm solar-blind ultraviolet images and color photos in the visible light.