CyberSec Research

We present an accurate and efficient framework for real-space Hubbard-corrected density functional theory. In particular, we obtain expressions for the energy, atomic forces, and stress tensor suitable for real-space finite-difference discretization, and develop a large-scale parallel implementation. We verify the accuracy of the formalism through comparisons with established planewave results. We demonstrate that the implementation is highly efficient and scalable, outperforming established planewave codes by more than an order of magnitude in minimum time to solution, with increasing advantages as the system size and/or number of processors is increased. We apply this framework to examine the impact of exchange-correlation inconsistency in local atomic orbital generation and introduce a scheme for optimizing the Hubbard parameter based on hybrid functionals, both while studying TiO$_2$ polymorphs.

This study explores the use of synchrotron measurements as a nanometrology tool for blazed gratings. In grazing incidence geometry, one can measure both the conical diffraction and the diffuse scattering on the grating simultaneously in a single scattering pattern. The sensitivity of scattering patterns to the structure of the blazed gratings is evaluated. The diffraction component of the pattern is shown to be sensitive to the average groove profile of the gratings. Meanwhile, the diffuse scattering depends on the roughness morphology of the reflective surface of blazed gratings. These findings are supported by numerical simulations. The simulations were performed using several rigorous solvers for the Helmholtz equations, and with a perturbation theory. The analysis relies on synchrotron data, as well as data from atomic force microscopy and scanning electron microscopy. The aim of this article is to draw the attention of the optical community to the synchrotron measurements as a nanometrology tool for the modern optical elements.

Spin pumping with superconductors has been extensively studied, particularly in double-layer systems. In this study, we investigate spin pumping in a trilayer system comprising a ferromagnetic insulator (FMI), a superconductor (SC), and a normal metal (NM). We derive the AC and DC spin currents in the NM layer induced by spin motion in the FMI under circularly polarized microwave irradiation. If we treat the spin motion as classical, the AC spin current is expressed. On the other hand, if we treat the spin motion as quantum quasiparticles, the DC spin current is derived. After these derivations, while the computational cost of evaluating the spin current is extremely high, we mitigate this using the Quantics Tensor Cross Interpolation (QTCI) method. We present numerical results showing the dependence of the spin current on temperature, microwave frequency, and superconductor layer thickness. Notably, the temperature dependence of AC and DC spin currents exhibits a coherence peak. Furthermore, we have discovered a transition structure in the dependence of the spin current on the thickness of the superconductor layer, where the dependence changes after a particular frequency.

Many problems are governed by differential equations (DEs). Artificial intelligence (AI) is a new path for solving DEs. However, data is very scarce and existing AI solvers struggle with approximation of high frequency components (AHFC). We propose an AI paradigm for solving diverse DEs, including DE-ruled first-principles data generation methodology and scale-dilation operator (SDO) AI solver. Using either prior knowledge or random fields, we generate solutions and then substitute them into the DEs to derive the sources and initial/boundary conditions through balancing DEs, thus producing arbitrarily vast amount of, first-principles-consistent training datasets at extremely low computational cost. We introduce a reversible SDO that leverages the Fourier transform of the multiscale solutions to fix AHFC, and design a spatiotemporally coupled, attention-based Transformer AI solver of DEs with SDO. An upper bound on the Hessian condition number of the loss function is proven to be proportional to the squared 2-norm of the solution gradient, revealing that SDO yields a smoother loss landscape, consequently fixing AHFC with efficient training. Extensive tests on diverse DEs demonstrate that our AI paradigm achieves consistently superior accuracy over state-of-the-art methods. This work makes AI solver of DEs to be truly usable in broad nature and engineering fields.

Metasurfaces are innovative planar optical structures capable of manipulating incident light properties. Accurate and computationally efficient modeling of such metasurfaces, particularly those with irregular geometries, remains a challenge for conventional solvers. In this work, we present a mesh-free Physics-Informed PointNet (PIPN) to model electromagnetic scattering from all-dielectric metasurfaces that feature spatially varying nanopillars. Our approach uses the PointNet architecture to directly encode spatially varying material properties into the Physics-Informed Machine Learning (PIML) framework. We demonstrate the generalization capability of our PIPN through evaluations on datasets; these datasets are generated with varying refractive indices representing common dielectric materials. Furthermore, the inclination angles are varied within each dataset, which represent expected manufacturing defects. Overall, our method provides a promising, mesh-free framework for accurate and efficient modeling of complex optical structures represented by irregular geometries.

The extensional rheology of dilute suspensions of spheres in viscoelastic or polymeric liquids is studied computationally. At low polymer concentration (c) and Deborah number (De), a wake of highly stretched polymers forms downstream of the particles due to larger local velocity gradients than the imposed flow, indicated by a positive deviation in local De. This increases the suspension's extensional viscosity with time and De for De less than 0.5. When De exceeds 0.5 (the coil-stretch transition), the fully stretched polymers from the far field collapse in regions with lower local velocity gradients around the particle's stagnation points, reducing suspension viscosity relative to the polymer-only liquid. The interaction between local flow and polymers intensifies with increasing c. Highly stretched polymers impede local flow, reducing local De, while it increases in regions with collapsed polymers. Initially, increasing c aligns local De and polymer stretch with far-field values, diminishing particle-polymer interaction effects. However, beyond a certain c, a new mechanism emerges. At low c, fluid three particle radii upstream exhibits increased local De, stretching polymers beyond their undisturbed state. As c increases, this deviation becomes negative, collapsing polymers and resulting in increasingly negative stress from particle-polymer interactions at large De and time. At high c, this negative interaction stress scales as c squared, surpassing the linear increase in polymer stress, making dilute sphere suspensions more effective at reducing the viscosity of viscoelastic liquids at larger De and c.

Understanding ion adsorption at electrified metal-electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, we systematically investigate the free energy profiles of Na$^+$, Cl$^-$, and F$^-$ ions at the Au(111)-water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that --without due care-- standard mixing rules can lead to qualitatively incorrect descriptions of ion-metal interactions. We present a systematic methodology for tuning the cross-term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms (UMA) MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

Two-dimensional sliding ferroelectrics, with their enhanced efficiencies of charge separation and tunability, constitute promising platforms for next-generation photovoltaic devices. However, recent systems predominantly exhibit dual degenerate polarization states with weak intensity, hindering the optimal manipulations of photovoltaic effects through sliding ferroelectricity. Here, we address this limitation by introducing two strengthened and distinct non-degenerate sliding ferroelectric phases (WZ' and ZB') in Janus In2S2Se, which can be achieved by Se-to-S substitution in monolayer In2Se3. First-principles calculations validate the experimental synthesis of this structure and its capability for reversible phase transitions triggered by atomic layer sliding, and a series of superior photovoltaic performances are demonstrated in such unique Janus In2S2Se, accompanied by a detailed analysis of how non-degenerate sliding ferroelectricity modulates distinct photovoltaic characteristics. The WZ' to ZB' transition can increase the carrier mobility and moderate the band gap while inducing an indirect-to-direct transition, yielding a marked red-shift and enhancement of the photocurrent peak in the infrared spectrum. Conversely, the WZ' phase, benefiting from enhanced polarization, delivers superior photoelectric conversion efficiency in the visible light region. This work establishes a phase-engineered framework of how non-degenerate sliding ferroelectricity orchestrates distinct photovoltaic behaviors, and the intrinsic physical correlations may offer novel perspectives for designing and regulating innovative photovoltaic devices.

We investigate plasmon-assisted photoelectron emission using a one-dimensional time-dependent density-functional theory (TDDFT) model. Photoelectron spectra are computed with the time-dependent surface-flux (t-SURFF) method. In addition to the expected above-threshold ionization (ATI) comb, we observe peaks that arise from long-lived plasmon oscillations and the associated electron emission occurring after the laser pulse. We further analyze the positions of these peaks and their scaling behavior with the laser intensity.

This paper presents a multiscale methodology for efficient unsteady conjugate heat transfer simulations. The solid domain is modelled by coupling a global representation of the temperature field, based on the eigenfunctions of the unsteady heat conduction equation, with a local, fine-scale-resolving solution of the heat conduction equation at the conjugate interface. To address the disparate time scales and enhance convergence, the decoupled modal equations are leveraged to enable targeted acceleration of the longest thermal time scales. One-dimensional analyses validate the properties of the scheme, while scale-resolving simulations demonstrate its practical application for steady and unsteady problems. Notably, the method achieves up to a fourfold reduction in computational time to reach steady thermal conditions compared to conventional conjugate simulations, without introducing significant computational overhead or error, offering an accurate and accelerated framework for unsteady thermal analysis.

Lattice vibrations critically shape charge and spin transport by governing carrier scattering, spin-charge interactions and spectral redistribution in nanostructures. In this study, we investigate how electron-phonon coupling (EPC) and structural configurations intertwine in magnetic and nonmagnetic $\beta_{12}$-borophene nanoribbons (BNRs). Using a tight-binding framework with site-dependent hopping parameters extracted from ab initio calculations and incorporating phonons within the Holstein model, we compute phonon-renormalized Green's functions and transport currents via the Landauer-B\"{u}ttiker formalism. We find that spin-dependent EPC enhances spin-dependent current in magnetic zigzag (ZZ) nanoribbons, driven by phonon-induced inelastic scattering and spin-selective band renormalization. Additionally, we observe an enhancement of charge transport current in the nonmagnetic configurations of $\beta_{12}$-BNRs. Structural variations further induce anisotropic EPC effects, significantly reshaping charge and spin transport. These insights establish EPC as a powerful design lever for optimizing borophene-based logic devices through tailored edge engineering.

Rhenium, Re, is used as an x-ray shield in laser-driven material property experiments, where its strength at high pressures can be a consideration in the design, modeling, and interpretation. We present a Steinberg-Guinan (SG) strength model for Re, tailored for use in high-pressure dynamic loading simulations. Parameters for the SG model were derived from recent atom-in-jellium predictions of the shear modulus under compression and experimental data on work-hardening from rolled-bar studies. The ambient shear modulus was fixed to the measured value, and the pressure-hardening parameter was fitted to the atom-in-jellium predictions up to 1 TPa. The shear modulus model was still a reasonable fit beyond 25 TPa. Thermal softening was estimated from the thermal expansivity and bulk modulus. Work-hardening parameters were extracted by fitting the model to Knoop microhardness measurements under known plastic strains. The resulting model captures the observed hardening behavior but predicts significantly lower flow stresses at high pressures than diamond anvil cell observations suggest, implying that Re may exhibit enhanced strength at megabar pressures. These results provide a basis for improved modeling of strength in Re under extreme conditions and suggest directions for further theoretical and experimental investigation.

The discovery of transition pathways to unravel distinct reaction mechanisms and, in general, rare events that occur in molecular systems is still a challenge. Recent advances have focused on analyzing the transition path ensemble using the committor probability, widely regarded as the most informative one-dimensional reaction coordinate. Consistency between transition pathways and the committor function is essential for accurate mechanistic insight. In this work, we propose an iterative framework to infer the committor and, subsequently, to identify the most relevant transition pathways. Starting from an initial guess for the transition path, we generate biased sampling from which we train a neural network to approximate the committor probability. From this learned committor, we extract dominant transition channels as discretized strings lying on isocommittor surfaces. These pathways are then used to enhance sampling and iteratively refine both the committor and the transition paths until convergence. The resulting committor enables accurate estimation of the reaction rate constant. We demonstrate the effectiveness of our approach on benchmark systems, including a two-dimensional model potential, peptide conformational transitions, and a Diels--Alder reaction.

Quantum computation offers potential exponential speedups for simulating certain physical systems, but its application to nonlinear dynamics is inherently constrained by the requirement of unitary evolution. We propose the quantum Koopman method (QKM), a data-driven framework that bridges this gap through transforming nonlinear dynamics into linear unitary evolution in higher-dimensional observable spaces. Leveraging the Koopman operator theory to achieve a global linearization, our approach maps system states into a hierarchy of Hilbert spaces using a deep autoencoder. Within the linearized embedding spaces, the state representation is decomposed into modulus and phase components, and the evolution is governed by a set of unitary Koopman operators that act exclusively on the phase. These operators are constructed from diagonal Hamiltonians with coefficients learned from data, a structure designed for efficient implementation on quantum hardware. This architecture enables direct multi-step prediction, and the operator's computational complexity scales logarithmically with the observable space dimension. The QKM is validated across diverse nonlinear systems. Its predictions maintain relative errors below 6% for reaction-diffusion systems and shear flows, and capture key statistics in 2D turbulence. This work establishes a practical pathway for quantum-accelerated simulation of nonlinear phenomena, exploring a framework built on the synergy between deep learning for global linearization and quantum algorithms for unitary dynamics evolution.

Simulations of hadronic and nuclear interactions are essential in both collider and astroparticle physics. The Chromo package provides a unified Python interface to multiple widely used hadronic event generators, including EPOS, DPMJet, Sibyll, QGSJet, and Pythia. Built on top of their original Fortran and C++ implementations, Chromo offers a zero-overhead abstraction layer suitable for use in Python scripts, Jupyter notebooks, or from the command line, while preserving the performance of direct calls to the generators. It is easy to install via precompiled binary wheels distributed through PyPI, and it integrates well with the Scientific Python ecosystem. Chromo supports event export in HepMC, ROOT, and SVG formats and provides a consistent interface for inspecting, filtering, and modifying particle collision events. This paper describes the architecture, typical use cases, and performance characteristics of Chromo and its role in contemporary astroparticle simulations, such as in the MCEq cascade solver.

The data-driven discovery of interpretable models approximating the underlying dynamics of a physical system has gained attraction in the past decade. Current approaches employ pre-specified functional forms or basis functions and often result in models that lack physical meaning and interpretability, let alone represent the true physics of the system. We propose an unsupervised parameter estimation methodology that first finds an approximate general solution, followed by a spline transformation to linearly estimate the coefficients of the governing ordinary differential equation (ODE). The approximate general solution is postulated using the same functional form as the analytical solution of a general homogeneous, linear, constant-coefficient ODE. An added advantage is its ability to produce a high-fidelity, smooth functional form even in the presence of noisy data. The spline approximation obtains gradient information from the functional form which are linearly independent and creates the basis of the gradient matrix. This gradient matrix is used in a linear system to find the coefficients of the ODEs. From the case studies, we observed that our modeling approach discovers ODEs with high accuracy and also promotes sparsity in the solution without using any regularization techniques. The methodology is also robust to noisy data and thus allows the integration of data-driven techniques into real experimental setting for data-driven learning of physical phenomena.

Physics-informed neural networks (PINNs) are numerical solvers that embed all the physical information of a system into the loss function of a neural network. In this way the learned solution accounts for data (if available), the governing differential equations, or any other constraint known of the physical problem. However, they face serious issues, notably their tendency to converge on trivial or misleading solutions. The latter occurs when, although the loss function reaches low values the model makes incorrect predictions. These difficulties become especially significant in differential equations involving multi-scale behavior, such as rapidly varying terms and solutions exhibiting strong oscillatory behavior. To address these challenges, we introduce the Dynamical Boundary Constraint (DBC) algorithm, which imposes restrictions on the loss function based on prior training of the PINN. To demonstrate its applicability, we tested this approach on examples of different areas of physics.

The "CO adsorption puzzle", a persistent failure of utilizing generalized gradient approximations (GGA) in density functional theory to replicate CO's experimental preference for top-site adsorption on transition-metal surfaces, remains a critical barrier in surface chemistry. While hybrid functionals such as HSE06 partially resolve this discrepancy, their prohibitive computational cost limits broader applications. We tackle this issue by adopting the Deep Kohn-Sham (DeePKS) method to train machine-learned exchange-correlation functionals. Principal component analysis reveals that the input descriptors for electronic structures separate distinctly across different chemical environments, enabling the DeePKS models to generalize to multi-element systems. We train system-specific DeePKS models for transition-metal surfaces Cu(111) and Rh(111). These models successfully recover experimental site preferences, yielding adsorption energy differences of about 10 meV compared to HSE06. Furthermore, a single model for the two surfaces is trained, and the model achieves comparable accuracy in predicting not only adsorption energies and site preference but also potential energy surfaces and relaxed surface adsorption structures. The above work demonstrates a promising path towards universal models, enabling catalyst exploration with hybrid functional accuracy at substantially reduced cost.