CyberSec Research

We have investigated the pressure-dependent electronic structure, phonon stability, and anomalous Hall response of the recently discovered altermagnet FeSb2 from density functional theory (DFT) and Wannier function analysis. From density functional perturbation theory (DFPT) calculations, we have found that FeSb2 remains dynamically stable up to 10 GPa, evidenced by positive phonon frequencies. Our spin-polarised band structure shows that the node of band crossing between spin-up and spin-down bands around the Fermi energy exactly lies at the Gamma and A-symmetry points. The Fermi crossing is mostly exhibited by band-24, band-25 and band-26. The non-relativistic spin-splitting (NRSS) along M'-Gamma-M and A-Z-A' symmetry is attributed to the broken time-reversal (PT ) symmetry. There are significant changes in the band profile under applied pressure, as one can see the shifting of the node of band-24 and band-26 towards the lower energy side. The NRSS exhibited by band-24 along M'-Gamma-M symmetry is notably small. Although the strength of NRSS of band-26 along A-Z-A' symmetry is significant but reduces under applied pressure. The anomalous Hall conductivity (AHC) values are prominent in -1 to 1 eV range. A sharp peaked and positive AHC values at ambient pressure, becomes spectrally broadened and negative at 10 GPa due to pressure-induced band crossings and redistribution of Berry curvature near the Fermi level. We have observed that the values of spin hall conductivity (SHC) are around 2-2.5 times lower as compared to AHC and prominent in between -1.0 eV to 1.0 eV. Our results establish FeSb2 as a tunable altermagnetic candidate where pressure can modulate both topological transport and dynamic stability, offering opportunities for strain-engineered Hall responses in compensated magnetic systems.

Current-induced magnetization switching, a fundamental phenomenon related to spin-transport of electrons, enables non-voltaic and fast information write, facilitating applications in low-power memory and logic devices. However, magnetization switching by spin-orbit torques is usually attributed to current flowing in the nonmagnetic metal layer of multilayers or in magnetic alloys with heavy elements. Here, we report perpendicular magnetization switching induced by current flowing in an elemental ferromagnet nickel single layer. This prototype structure demonstrates that current-induced magnetization switching is a general phenomenon of magnet. The results suggest that the current induces an effective transverse magnetic field with an out-of-plane component leading to the magnetization switching, different to the conventional spin-orbit torques. Our work opens the new insight and reveals the intrinsic mechanism of current-induced torques.

The electronic quality of two-dimensional systems is crucial when exploring quantum transport phenomena. In semiconductor heterostructures, decades of optimization have yielded record-quality two-dimensional gases with transport and quantum mobilities reaching close to 10$^8$ and 10$^6$ cm$^2$/Vs, respectively. Although the quality of graphene devices has also been improving, it remains comparatively lower. Here we report a transformative improvement in the electronic quality of graphene by employing graphite gates placed in its immediate proximity, at 1 nm separation. The resulting screening reduces charge inhomogeneity by two orders of magnitude, bringing it down to a few 10$^7$ cm$^-2$ and limiting potential fluctuations to less than 1 meV. Quantum mobilities reach 10$^7$ cm$^2$/Vs, surpassing those in the highest-quality semiconductor heterostructures by an order of magnitude, and the transport mobilities match their record. This quality enables Shubnikov-de Haas oscillations in fields as low as 1 mT and quantum Hall plateaus below 5 mT. Although proximity screening predictably suppresses electron-electron interactions, fractional quantum Hall states remain observable with their energy gaps reduced only by a factor of 3-5 compared to unscreened devices, demonstrating that many-body phenomena at spatial scales shorter than 10 nm remain robust. Our results offer a reliable route to improving electronic quality in graphene and other two-dimensional systems, which should facilitate the exploration of new physics previously obscured by disorder.

The control of material properties at the atomic scale remains a central challenge in materials science. Transition metal dichalcogenides (TMDCs) offer remarkable electronic and optical properties, but their functionality is largely dictated by their stable crystalline phases. Here we demonstrate a single-step, ligand-free strategy using femtosecond laser ablation in liquid to transform crystalline, stoichiometric palladium diselenide (PdSe$_{\mathrm{2}}$) into highly stable, amorphous, and non-stoichiometric nanoparticles (PdSe$_{\mathrm{2-x}}$, with x$\approx$1). This laser-driven amorphization creates a high density of selenium vacancies and coordinatively unsaturated sites, which unlock a range of emergent functions absent in the crystalline precursor, including plasmon-free surface-enhanced Raman scattering with an enhancement factor exceeding 10$^\mathrm{6}$, a 50-fold increase in photocatalytic activity, and near-infrared photothermal conversion efficiency reaching 83$\%$. Our findings establish laser-induced amorphization as a powerful top-down approach for defect-engineered TMDCs and advances their practical usage in optics, catalysis, and nanomedicine.

Synthetic sequence-controlled polymers promise to transform polymer science by combining the chemical versatility of synthetic polymers with the precise sequence-mediated functionality of biological proteins. However, design of these materials has proven extraordinarily challenging, because they lack the massive datasets of closely related evolved molecules that accelerate design of proteins. Here we report on a new Artifical Intelligence strategy to dramatically reduce the amount of data necessary to accelerate these materials' design. We focus on data connecting the repeat-unit-sequence of a \emph{compatibilizer} molecule to its ability to reduce the interfacial tension between distinct polymer domains. The optimal sequence of these molecules, which are essential for applications such as mixed-waste polymer recycling, depends strongly on variables such as concentration and chemical details of the polymer. With current methods, this would demand an entirely distinct dataset to enable design at each condition. Here we show that a deep neural network trained on low-fidelity data for sequence/interfacial tension relations at one set of conditions can be rapidly tuned to make higher-fidelity predictions at a distinct set of conditions, requiring far less data that would ordinarily be needed. This priming-and-tuning approach should allow a single low-fidelity parent dataset to dramatically accelerate prediction and design in an entire constellation of related systems. In the long run, it may also provide an approach to bootstrapping quantitative atomistic design with AI insights from fast, coarse simulations.

Mercury chalcogenides are a class of materials that exhibit diverse structural phases under pressure, leading to a range of exotic physical properties, including topological phases and chiral phonons. In particular, the phase diagram of mercury sulfide (HgS) remains difficult to characterize, with significant uncertainty surrounding the transition pressure between phases. Based on recent experimental results, we employ Density Functional Theory and Superconducting Density Functional Theory to investigate the pressure-induced structural phase transition in HgS and its interplay with the emergence of superconductivity as the crystal transitions from the cinnabar phase (space group P3$_1$21) to the rock salt phase (space group Fm$\bar{3}$m). Remarkably, the rocksalt phase hosts a multigap superconducting state driven by distinct Fermi surface sheets, with two dominant gaps; the unusually high critical temperature of $\sim$11 K emerges naturally within this multiband scenario, highlighting the role of interband coupling beyond isotropic models. These results place HgS among the few systems where multiband superconducting gap structures emerge under pressure.

Perovskite-type ternary nitrides with predicted exciting ferroelectricity and many other outstanding properties hold great promise to be an emerging class of advanced ferroelectrics for manufacturing diverse technologically important devices. However, such nitride ferroelectrics have not yet been experimentally identified, mainly due to the challenging sample synthesis by traditional methods at ambient pressure. Here we report the successful high-pressure synthesis of a high-quality ferroelectric nitride perovskite of CeTaN3-{\delta} with nitrogen deficiency, adopting an orthorhombic Pmn21 polar structure. This material is electrically insulating and exhibits switchable and robust electric polarization for producing ferroelectricity. Furthermore, a number of other extraordinary properties are also revealed in this nitride such as excellent mechanical properties and chemical inertness, which would make it practically useful for many device-relevant applications and fundamentally important for the study of condensed-matter 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.

We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. Specifically, we showcase the application to a two-dimensional, many-particle system, focusing on surface-step fluctuations and on the related time-dependent roughness. After the construction of a dataset based on Kinetic Monte Carlo simulations, a conditional GAN is trained to propagate stochastically the state of the system in time, allowing the generation of new sequences with a reduced computational cost. Modifications with respect to standard GANs, which facilitate convergence and increase accuracy, are discussed. The trained network is demonstrated to quantitatively reproduce equilibrium and kinetic properties, including scaling laws, with deviations of a few percent from the exact value. Extrapolation limits and future perspectives are critically discussed.

We introduce a unified machine-learning framework designed to conveniently tackle the temporal evolution of alloy microstructures under the influence of an elastic field. This approach allows for the simultaneous extraction of elastic parameters from a short trajectory and for the prediction of further microstructure evolution under their influence. This is demonstrated by focusing on spinodal decomposition in the presence of a lattice mismatch eta, and by carrying out an extensive comparison between the ground-truth evolution supplied by phase field simulations and the predictions of suitable convolutional recurrent neural network architectures. The two tasks may then be performed subsequently into a cascade framework. Under a wide spectrum of misfit conditions, the here-presented cascade model accurately predicts eta and the full corresponding microstructure evolution, also when approaching critical conditions for spinodal decomposition. Scalability to larger computational domain sizes and mild extrapolation errors in time (for time sequences five times longer than the sampled ones during training) are demonstrated. The proposed framework is general and can be applied beyond the specific, prototypical system considered here as an example. Intriguingly, experimental videos could be used to infer unknown external parameters, prior to simulating further temporal evolution.

Materials science inherently spans disciplines: experimentalists use advanced microscopy to uncover micro- and nanoscale structure, while theorists and computational scientists develop models that link processing, structure, and properties. Bridging these domains is essential for inverse material design where you start from desired performance and work backwards to optimal microstructures and manufacturing routes. Integrating high-resolution imaging with predictive simulations and data-driven optimization accelerates discovery and deepens understanding of process-structure-property relationships. The differentiable physics framework evoxels is based on a fully Pythonic, unified voxel-based approach that integrates segmented 3D microscopy data, physical simulations, inverse modeling, and machine learning.

In this study, we systematically investigate the thermal and electronic transport properties of two-dimensional PbSe/PbTe monolayer heterostructure by combining first-principles calculations, Boltzmann transport theory, and machine learning methods. The heterostructure exhibits a unique honeycomb-like corrugated and asymmetric configuration, which significantly enhances phonon scattering. Moreover, the relatively weak interatomic interactions in PbSe/PbTe lead to the formation of anti-bonding states, resulting in strong anharmonicity and ultimately yielding ultralow lattice thermal conductivity (${\kappa_{\rm L}}$). In the four-phonon scattering model, the ${\kappa_{\rm L}}$~values along the $x$ and $y$ directions are as low as 0.37 and 0.31 W/mK, respectively. Contrary to the conventional view that long mean free path acoustic phonons dominate heat transport, we find that optical phonons contribute approximately 59\% of the lattice thermal conductivity in this heterostructure. These optical phonons exhibit large Gr\"uneisen parameters, strong anharmonic scattering, and relatively high group velocities, thereby playing a crucial role in the low ${\kappa_{\rm L}}$ regime. Further analysis of thermoelectric performance shows that at a high temperature of 800 K, the heterostructure achieves an exceptional dimensionless figure of merit ($ZT$) of 5.3 along the $y$ direction, indicating outstanding thermoelectric conversion efficiency. These findings not only provide theoretical insights into the transport mechanisms of PbSe/PbTe monolayer heterostructure but also offer a practical design strategy for developing high-performance two-dimensional layered thermoelectric materials.

Nonrelativistic magnon chiral splitting in altermagnets has garnered significant recent attention. In this work, we demonstrate that nonlinear three-wave mixing -- where magnons split or coalesce -- extends this phenomenon into unprecedented relativistic regimes. Employing a bilayer antiferromagnet with Dzyaloshinskii-Moriya interactions, we identify three distinct classes of chiral splitting, each dictated by specific symmetries, such as $C_4T$, $\sigma_v T$, or their combination. This reveals a novel bosonic mechanism for symmetry-protected chiral splitting, capitalizing on the unique ability of magnons to violate particle-number conservation, a feature absent in low-energy fermionic systems. Our findings pave the way for engineering altermagnetic splitting, with potential applications in advanced magnonic devices and deeper insights into magnon dynamics in complex magnetic systems.

The negative thermal expansion (NTE) effect has been found generally combined with structural phase transitions. However, the charge and orbital freedoms of the NTE has not been well studied. This study employs angle-resolved photoemission spectroscopy and first-principles calculations to elucidate the charge and orbital kinetics of the anomalous two-step negative thermal expansion structural phase transitions in PbTa2Se4. As the temperature decreases, each transition undergoes a similar block-layer sliding, although the charge transfer behaviors differ significantly. During the first transition, charge is mainly transferred from the Pb 6pz orbital to an M-shaped band below the Fermi level, barely altering the Fermi surface. In contrast, the second transition involves modifications to both the Fermi surface and charge-transfer orbitals, with charge selectively transferred from Pb 6px/py orbitals to Ta 5dz2 orbitals and a decrease of the Fermi pockets formed by Pb 6px/py orbitals. Furthermore, a small pressure can easily tune the base structure phase among the three phases and the corresponding superconductivity. Therefore, our findings reveal that the orbital-selective charge transfer drives the unusual structure transition in PbTa2Se4, offering new insights into the NTE mechanisms and providing a unique window to study the pressure-tuned superconductivity in this metal-intercalated transition chalcogenides.

Materials with coexisting and coupled ferroelectric and magnetic orders are rare. Here we show, using density functional theory calculations, that inducing Fe$_\mathrm{La}$ antisites into non-ferroelectric and antiferromagnetic LaFeO$_3$ renders the material at the same time ferroelectric and ferrimagnetic. Even more excitingly, we observe a direct coupling between the ferroelectric and ferrimagnetic polarization, the latter being switchable by the former. While on average the magnetic moments of antisites would cancel, we envision that preparing defective LaFeO$_3$ under simultaneous electric and magnetic fields will lead to a net magnetic moment due to magnetic domain reconfiguration. Moreover, ferroelectric switching under a static magnetic field can lead to 180$^\circ$ switching of the antiferromagnetic order in LaFeO$_3$.

The role of Li-based batteries in the electrification of society cannot be understated, however their operational lifetime is often limited by the formation of dendrites, i.e. the localised deposition of Li that can cause shorts between the two electrodes leading to the failure of the battery. Nanocrystalline bimetallic current collectors can be used for anode-free Li-metal batteries, with improved Li plating and limited or suppressed formation of dendrites. Here, we demonstrate that the microstructure of an alpha-Brass current collector, Cu 63% Zn 37%, used in an anode-free Li-metal battery evolves during cycling. It initially had a nanocrystalline deformation layer approximately 80 nm in thickness after polishing. After 100 cycles, the initial deformed brass layer was partially converted to a ternary Laves phase Cu3ZnLi2 within a nanocrystalline brass matrix that grew to 200 - 250 nm in thickness. Upon Li stripping, the phase partially decomposes electrochemically, but what remains can sequester Li thus forming "dead Li" thereby contributing to capacity loss. We propose a mechanism for the microstructural evolution including dynamic recrystallization and phase formation. Since this ternary Laves phase emerges during electrochemical cycling alone, binary alloy current collectors must be assessed for metastable ternary phase formation under different cycling conditions to either stabilize and exploit such phases or electrochemically fully strip them.

Contamination of freshwater sources has been alarming due to the widespread use of toxic chemicals in various industries. Advanced oxidation processes (AOPs) such as photocatalysis are widely explored to tackle such problems. In photocatalysis, highly oxidative species such as hydroxyl radicals (*OH) are produced with the help of some semiconductor photocatalysts and light. A photocatalyst decomposes these toxic organic compounds in the presence of light. Spinel ferrite (MFe2O4, M = Co, Ni, Cu, Zn, etc.) materials are an important candidate as a photocatalyst due to their semiconducting behaviour and narrow optical bandgap. In this work, we have synthesized cobalt ferrite (CoFe2O4) nanoparticles using the sol-gel method and subsequently annealed at 500{\deg}C. The nanoparticles are characterized using X-ray diffraction, scanning electron microscopy, Raman, and Infrared spectroscopy for structural analysis. The band gap of the material is evaluated using UV-visible spectroscopy. The photocatalytic activity of the material is investigated using methyl orange and methylene blue aqueous solutions as a model dye and a low-power white LED as a light source. The material could decompose 95 % of the dye after 150 minutes of irradiation. Adding hydrogen peroxide further improves the decomposition rate, with over 90 % decomposition achieved within 90 minutes.

This investigation employed microwave whispering gallery mode (WGM) analysis to characterize the dielectric properties of a cylindrical, single-crystal sample of calcium tungstate (CaWO$_4$). Through investigation of quasi-transverse\hyp{}magnetic and quasi-transverse\hyp{}electric mode families, we can assess loss mechanisms and relative permittivity from room temperature down to cryogenic conditions. We report the biaxial permittivity values of $\epsilon_{||} = 9.029 \pm 0.09$ and $\epsilon_{\perp} = 10.761 \pm 0.11$ at $295$ K, and $\epsilon_{||} = 8.797 \pm 0.088$ and $\epsilon_{\perp} = 10.442 \pm 0.104$ at $4$ K. Components are denoted with respect to the c\hyp{}axis of the crystal unit cell. The parallel component agrees well with the published literature at MHz frequencies; however, the perpendicular component is $4.8$\% lower. The WGM technique offers greater precision, with accuracy limited primarily by the uncertainty in the crystal's dimensions. WGMs also serve as sensitive probes of lattice dynamics, enabling monitoring of temperature-dependent loss mechanisms. At room temperature, the measured loss tangents were $\tan\delta_{||}^{295,\mathrm{K}} = (4.1 \pm 1.4) \times 10^{-5}$ and $\tan\delta_{\perp}^{295,\mathrm{K}} = (3.64 \pm 0.92) \times 10^{-5}$. Upon cooling to 4 K, the loss tangents improved by approximately two orders of magnitude, reaching $\tan\delta_{||}^{4,\mathrm{K}} = (1.56 \pm 0.52) \times 10^{-7}$ and $\tan\delta_{\perp}^{4,\mathrm{K}} = (2.05 \pm 0.79) \times 10^{-7}$. These cryogenic values are higher than those reported in prior studies, likely due to a magnetic loss channel associated with an unidentified paramagnetic spin ensemble. These findings have implications for the use of CaWO$_4$ in applications such as spin-based quantum systems and cryogenic bolometry, highlighting the potential of WGMs for novel sensing applications.