Papers

Nodal line semimetals (NLSMs) host distinctive topological properties and unconventional collective excitations. While plasmons in linear NLSMs are well studied, those of higher-order dispersive NLSMs remain poorly understood. We investigate plasmon modes in 3D quadratic and cubic NLSMs within the random phase approximation (RPA), valid for $r_s\ll1$ but breaking down in the small-doping cubic regime where the density of states diverges. Deriving the one-loop polarizability and evaluating its coefficients numerically from the full 3D Lindhard integral, we find distinct carrier-density scaling laws: quadratic NLSMs exhibit $\omega_p\sim n^{1/2}$, while cubic NLSMs show $\omega_p\sim n^{2/3}$ at large doping crossing over to $\omega_p\sim n^{3/4}$ at small doping. The $n^{3/4}$ law is not quantitatively reliable: the diverging density of states drives $r_s>1$ already at $n\sim10^{18}$ cm$^{-3}$, limiting the observable window to at most $10^{18}\lesssim n\lesssim10^{19}$ cm$^{-3}$; below this, beyond-RPA correlations are essential. These scalings originate from the power-law density of states and intraband (Drude) response, and are analogous in phase-space power counting to bilayer and trilayer graphene, respectively. The long-wavelength intraband polarization dominates the plasmon frequency, while interband contributions are subleading (two to three orders of magnitude smaller for $\Omega\ll\mu$). Both systems share a plasmon anisotropy $\Omega_p^z/\Omega_p^\perp\to\sqrt{2}$ in the thin-ring limit, a generic consequence of the torus Fermi-surface geometry. This $\sqrt{2}$ doublet, though observable by HREELS, cannot distinguish quadratic from cubic dispersion; the density scaling exponent is the true distinguishing signature. Our results provide a framework for the collective dynamics of higher-order dispersive NLSMs and suggest experimentally testable signatures accessible by HREELS.

The recent experimental realization of the half-filled three-color Fermi-Hubbard model on a square optical lattice provides a novel platform for exploring exotic states of matter beyond conventional SU(2) systems. In this Letter, we investigate the three-color Fermi-Hubbard model with color-dependent attractive interactions using determinant quantum Monte Carlo simulations. We find that, at quantum degenerate temperatures, a lattice Fermi supersolid state emerges from the interplay between a moderately-to-strongly interacting two-color subsystem and a weakly coupled third-color environment. This supersolid state, characterized by the coexistence of charge-density-wave and color-superfluid orders, is highly promising for experimental detection with current techniques. Our results demonstrate that the attractive three-color Fermi-Hubbard model on a square optical lattice offers an experimentally accessible system for exploring the supersolidity of ultracold lattice fermions, requiring only a simple lattice geometry and easily tunable onsite interactions.

Obtaining accurate electron densities is important for the fundamental description of molecular and condensed matter systems, as well as for the development of next-generation density functionals. Diffusion Monte Carlo (DMC), in particular, is known to produce benchmark-quality data; however, the predicted real-space electron densities contain substantial amounts of statistical noise. In this work, we study denoising approaches for DMC densities, judged on the basis of the information-theoretic Jensen-Shannon divergence. The denoising is facilitated by an approximate heteroscedastic to homoscedastic transformation leveraging the density functional theory density as a physical prior. We systematically compare a range of denoising techniques-including Fourier transform, regression, and 3D UNETs-on materials showing a wide range of density variations: carbon diamond, blue phosphorus, and rutile VO2. Our results indicate that simple flattened machine learning models and 2D image-based models introduce line artifacts and struggle to capture the full spatial correlation. In contrast, when using variance stabilization, regression methods outperform all others in both the high and low- noise limits across all materials considered. The best denoisers reduce the required cost of density-generating DMC simulations by 10-100x, providing a promising route forward for application in noise-sensitive tasks such as DFT functional inversion.

We construct a neural classical density functional that acts directly on unrestricted three-dimensional density profiles. As a computationally tractable test bed, we consider parallel hard cubes of side length three on a simple cubic lattice. A fully convolutional network learns the one-body direct-correlation functional $c^{(1)}[\rho]$ from data obtained with grand-canonical Monte Carlo simulations in randomized external potentials. Complete profiles are used during both training and inference; a stochastic Bernoulli mask on the output sites makes full-profile training effective without explicitly extracting and storing overlapping local density windows. Averaging the first-layer kernels over all 48 rotations and reflections of the cubic point group additionally imposes exact cubic equivariance without data augmentation. We compare the learned functional with independent simulation data and with the lattice fundamental-measure functional of Lafuente and Cuesta. The neural functional markedly improves the homogeneous equation of state and the density profile at a planar hard wall. For the anisotropic pair distribution around a fixed particle, both functionals reproduce the principal packing shells, with their relative accuracy depending on crystallographic direction. These results demonstrate neural density-functional calculations on complete three-dimensional profiles while also identifying accurate full-dimensional training data, thermodynamic consistency, and structural correlations as the central challenges for extensions to continuum fluids.

Frustrated quantum matter can host intertwined orders rooted in symmetry-related low-energy landscapes, yet static order parameters alone do not reveal how fluctuations are organized among competing configurations. Here we measure mode-resolved shot-to-shot population fluctuations in a $p$-orbital triangular-lattice superfluid with a tunable bias among three valleys. We observe a bias-tuned evolution from enhanced, anticorrelated fluctuations of two minority valleys toward strong confinement of relative-population fluctuations in a selected two-valley stripe phase. The dominant fluctuation structure is captured by an effective canonical model that includes interactions among the condensed modes, supporting a quasi-equilibrium description of the coherent three-valley condensate. Together, the data and model reveal a quantum–thermal regime shaped by pair-tunneling-induced number–phase dynamics, in which relative-phase scrambling softens effective barriers in the minority-valley regime, while phase rigidity gives rise to macroscopic harmonic confinement in the stripe phase. Our results establish mode-resolved fluctuation measurements as a probe of hidden number–phase back-action in frustrated quantum fluids.

Since their first realization more than a decade ago spin-orbit-coupled Bose-Einstein condensates have been the subject of intense theoretical and experimental investigations. Spin-orbit coupling deeply modifies the equilibrium properties of the condensate, giving rise to novel configurations such as a supersolid stripe phase and a phase-separated plane-wave state. At the level of dynamics, both the frequency and the nature of the collective modes are significantly affected by the coupling with the spin degree of freedom. Here we review some of the most relevant advances in the field and provide our perspective on possible future research directions.

In well-established theories of Josephson junctions, the superconducting critical current \( I_\mathrm{c} \) increases as the normal state conductance \( \mathcal{G} \) increases. However, in a recent experiment in twisted bilayer graphene (TBG) based Josephson junctions, unexpectedly, it was observed that the increase of the critical current is accompanied by a decrease of the normal state conductance. We call this phenomenon the critical current anomaly. In this work, we point out that in the TBG-based Josephson junction, due to the suppression of the conventional Josephson current by the flatness of the band and the quantum metric enabled Josephson current (QMJC), the critical current anomaly can occur. The QMJC appears if the quantum metric length is comparable or longer than the junction length. We show that both \( \mathcal{G} \) and \( I_\mathrm{c} \) have the conventional and the quantum metric contributions, and there are parameter regimes in which \( I_\mathrm{c} \) increases even when \( \mathcal{G} \) decreases. We first demonstrate the critical current anomaly by a simple modified Lieb-lattice model both analytically and numerically. The incredible consistency with the experimental results is demonstrated using a realistic six-band model of twisted bilayer graphene. Therefore, we suggest that the critical current anomaly observed in the experiment provide strong evidence of QMJC which were ignored in well-established theories of Josephson junctions.

In aircraft engines, functionally graded abradable coatings are used to control blade-tip clearance, but their durability is governed by effects that are often treated separately in existing models, including temperature-dependent viscoelastic softening, progressive damage, deposition-induced microstructural modulation, and geometric tolerances. This study integrates these effects within a unified multiphysics–probabilistic framework. The solved domain is a local through-thickness coating column driven by prescribed strain and temperature histories that include thermal eigenstrain and coating–substrate expansion mismatch. The results show that periodic property modulation increases end-of-cycle damage relative to the monotonic gradient and concentrates the maximum stress within a localized modulation crest. Propagating symmetric geometric tolerances through the coupled solver produces a strongly right-skewed damage distribution and a higher classification-level exceedance probability for the modulated gradient than for the monotonic gradient. This exceedance behavior cannot be obtained from a deterministic analysis performed at nominal geometry. The implementation is verified through spatial and temporal refinement, closed-form unit tests, nested Monte Carlo convergence with Wilson confidence intervals, and a hold-out-validated polynomial response surface. Numerical verification is clearly distinguished from experimental validation, and the simulated case is presented as a generic benchmark rather than an identified material model. The framework links deposition parameters and tolerance bands to damage-exceedance risk, supporting reliability-oriented screening of graded and multilayer coating systems.

A composite excitation need not inherit the localization behavior of its constituents. We show that an interacting non-Hermitian quasiperiodic ladder realizes a controllable and reversible localization inversion between composite and unbound excitations, where internal configuration, rather than only the external potential, becomes a control parameter for localization. Opposite complex potentials on the two legs cancel at first order for a same-rung pair but act directly on separated particles, allowing extended composite states to persist while the unpaired sector becomes localized. A strong-coupling theory identifies the composite state as an emergent weakly modulated non-Hermitian quasicrystal generated by virtual unpaired configurations. Breaking the potential antisymmetry restores a direct modulation of the composite band and reverses the localization hierarchy. Engineering configuration-space pathways further stabilizes an extended composite band embedded within a localized continuum, the inverse of the conventional bound-state-in-the-continuum scenario. Our results establish internal configuration as a reversible control parameter for localization.

Recent advances in time-resolved spectroscopies have enabled direct access to the momentum-selective nonequilibrium dynamics of correlated quantum materials, revealing a strongly momentum-dependent response of electrons and collective excitations. Interpreting these observations requires a real-time theoretical framework that consistently captures the interplay between strong local electronic correlations and nonlocal collective fluctuations, a capability that remains beyond state-of-the-art nonequilibrium approaches. Using a recently developed real-time many-body framework, we resolve the momentum-selective ultrafast dynamics of a photoexcited correlated electron system. We predict a transient nodal-antinodal anisotropy in electronic heating, providing a microscopic explanation for the momentum-dependent response debated in time-resolved photoemission and Raman experiments, and identify the nonthermal spectral-weight transfer responsible for the transient antinodal in-gap states observed in ultrafast photoemission. We further uncover a momentum-selective magnetic response, in which antiferromagnetic fluctuations undergo a strongly nonthermal, quench-like excitation far above the electronic temperature while preserving their correlation length, before relaxing through a momentum-space magnon cascade toward lower-momentum modes. Finally, by tracking the real-time local spin susceptibility, we identify a dynamical, experimentally accessible signature of local-moment formation and its photoinduced melting. Our results establish a unified microscopic picture of ultrafast electronic and magnetic dynamics, providing a framework for interpreting momentum-resolved pump-probe experiments.

Cerium (Ce), the most abundant lanthanide, offers significant potential for addressing shortages in high-performance magnetic materials, particularly through the discovery of compounds suitable for gap magnets. However, predicting Ce-based ferromagnets with uniaxial magnetic anisotropy remains challenging because their magnetic behavior depends strongly on crystal structure, exchange geometry, and electronic interactions. Here, we present a physics-guided computational framework to screen known Ce-based crystal structures and identify promising Ising ferromagnets for future synthesis. A Random Forest classifier uses seven structural and SOAP descriptors, including unit-cell volume, density, atomic sites, space group, atomic density, Ce SOAP overlap, and transition-metal SOAP overlap, to prioritize candidate compounds. Selected crystallographic structures are then analyzed using Ising-model Monte Carlo simulations to characterize phase behavior and critical properties. Critical exponents extracted from simulated phase transitions provide quantitative insight into magnetic regimes and anisotropy-related effects. We further employ autoencoders trained on affinity-based features from simulated spin configurations to identify latent signatures of phase evolution and transition behavior. Together, this framework integrates structural screening, statistical-mechanical simulation, and machine learning to accelerate the identification of promising Ce-based magnetic materials and provide candidates for experimental synthesis and validation.

Motivated by recent reports of enhanced superconducting performance in several families of iron-based superconductors (IBS) processed by high-pressure (HP) synthesis, we investigate the influence of high gas pressure and high-temperature synthesis (HP-HTS) process on the structural, microstructural, electrical transport, and magnetic properties of Pr-based oxypnictide PrFeAsO1-xFx (Pr1111) using the processing conditions of 0.5 GPa for 1 h previously optimized for other IBS families. Representative underdoped (x = 0.2), optimal doped (x = 0.3), and overdoped (x = 0.5) compositions from the ambient-pressure electronic phase diagram of Pr1111 are selected to evaluate its composition-dependent effects of HP-HTS. The results demonstrate that HP-HTS enhances fluorine incorporation, improves phase formation, and produces a denser microstructure with improved grain connectivity in the underdoped and optimal doped compositions. Magnetic measurements reveal increased in the superconducting transition temperature (Tc) of ~1 K for x = 0.2 and ~6 K for x = 0.3, whereas only a marginal improvement in the critical current density is observed. Electrical resistivity measurements of the underdoped composition show a slight increase in Tc accompanied by a broader resistive transition, indicating residual structural inhomogeneity. In contrast, the overdoped composition exhibits increased impurity phase segregation, accompanied by suppression of superconductivity. These results demonstrate that the effectiveness of HP-HTS in Pr1111 is strongly composition dependent and governed by the interplay among fluorine incorporation, phase stability, and microstructural evolution, highlighting the need for further optimization of the HP-HTS processing conditions.

Slow internal states reshape both the mean throughput and the temporal organization of a driven lattice gas. Exit-counting statistics reveal this effect in a finite open totally asymmetric simple exclusion process whose particles reversibly switch between active and paused states. Increasing pausing lowers the mean current smoothly, whereas the long-window Fano factor is strongly nonmonotonic. At the reference boundary rates, the maximum remains near a measured mean paused population \(N_p=L\rho_{\rm paused}\simeq1.5\)–\(2\) across lattice lengths L=50-500, while the corresponding pausing rate scales as \(k_p^{\rm max}\propto L^{-1}\). A minimal constant-birth, linear-death approximation translates an order-one collective crossover into this finite-size displacement and gives \(N_p^\star\simeq1.50\) in the independent-pause, strong-blocking limit. The simulations delimit this approximation: the pause number is overdispersed, and at fixed \(N_p\), slower unpausing increases both the correlation time and the noise amplitude. Residence-time and structural analyses further separate the relevant slow variables. The pause-free versus pause-containing residence-time scale tracks the fitted output-correlation time, whereas the noise amplitude follows fluctuations, rather than the mean size, of the largest particle cluster. Low-frequency output noise therefore identifies an intermittent finite-size regime shaped jointly by slow-defect kinetics and traffic-jam reorganization.

This study proposes a method for predicting three-dimensional (3D) local strain distribution under compressive deformation of as-cast Mg/LPSO two-phase alloys from 3D microstructure images. The 3D local strain distribution was obtained by applying the digital volume correlation method to X-ray CT images before and after compression tests. Three microstructure descriptors were extracted from the 3D microstructure images around each strain measurement point: volume fractions of the phases, persistent diagrams that can express the connectivity of the phases, and two-phase spatial correlation that can express the spatial distribution of the phases. A deep learning model was then constructed to predict local strain from the three microstructure descriptors. Since two types of descriptors were used in this study, numerical data and image data, multimodal deep learning was employed to make predictions. Thus, the use of multiple microstructure descriptors enabled predictions to be made with higher accuracy than when predictions were made from a single descriptor. Feature importance of the descriptors was assessed through correlation analysis and occlusion sensitivity analysis. The results revealed that high strain tended to occur in the region where the hard phase, LPSO phase, had a large elongated phase oriented at a 45{\deg} direction to the loading direction. This result is consistent with other previous studies and indicates that the proposed method is effective in elucidating the relationship between the microstructure and the deformation behavior of the material.

Aqueous electrocatalysis generates oxygenated intermediates at catalyst surfaces. While intermediate species on single-crystal catalysts have been observed, the nature and evolution of surface oxygenated species on industrially relevant nanoparticle (NP) catalysts remain largely unknown. Here, using in situ Raman spectroscopy, we tracked the formation and potential-dependent evolution of oxygenated adsorbates in alkaline media on NP catalysts with an active platinum (Pt) surface. By comparing spectroscopic features in Ar- vs O2-saturated electrolytes, we determined three key intermediates produced by the oxygen reduction reaction (ORR): adsorbed OOH, OH, and O2. In contrast to the conventional wisdom that intermediates exist only during catalytic reactions, we found these oxygenated adsorbates to be highly long-lived and hysteretic, and to persist even after the termination of ORR. This adsorbate-retention effect exhibits a modest dependence on the surface oxidation state and the electrolyte cations (K+ vs Li+), and is likely facilitated by the heterogeneous nature of the catalyst surface. The results highlight the complexity of surface adsorption structures on realistic catalysts, which often extends beyond that captured by measurements or simulations on model single-crystal surfaces.

We propose that the localization length in an isolated, ideally flat Chern band is set by quantum geometry. We explore the corresponding localization transition and its critical scaling by applying transfer matrix calculations in the maximally localized hybrid Wannier basis, whose spatial spread is exactly characterized by a quantum geometric length. Remarkably, upon tuning the quantum metric of the Chern band, we observe a crossover from a universal regime controlled by the Dirac fixed point to a non-universal regime with continuously varying critical exponents. Within the universal regime, the localization length exhibits a pronounced linear dependence on the quantum geometric length, supporting its quantum geometric nature. These findings provide a novel quantum geometric perspective on the localization in quantum Hall systems such as twisted moiré superlattices, and shed new light on the long-standing controversy over the criticality of the integer quantum Hall transition.

Etching yields of Si, SiO2, and Si3N4 have been determined for silicon ion (Si+), halogen ions (F+, Cl+, and Br+) and silicon halide ions (SiF+, SiF3+, SiCl+, SiCl3+, SiBr+, and SiBr3+) irradiation in 300 to 1000 eV using a mass-selected ion beam apparatus that can irradiate a single species ion to sample surfaces under an ultra-high vacuum condition. Si+ irradiation below 1000eV deposits silicon atoms on Si, SiO2, and Si3N4 surfaces. The etching yields of silicon tri-halide ions (SiF3+, SiCl3+, and SiBr3+) above 1000 eV are larger than those of halogen ions, respectively, and these etching yields depend on the incident ion energy and the etching material (especially Si3N4). At low incident energy, silicon mono-halide ions (SiF+, SiCl+, and SiBr+) deposit silicon on substrates, and the etching threshold energy depends on the halogen species. This information contributes to a database of experimental values needed to increase the precision of an etching process and a profile simulator.

In rocksalt-derived oxide cathodes, the local Li-migration environment around an O$_4$ tetrahedral intermediate is commonly classified by the number of face-sharing transition-metal (TM) neighbors. In LiMnO$_2$, the TM species is Mn, and 0-TM denotes the absence of face-sharing Mn neighbors. However, migration and delithiation may also depend on higher-shell coordination and tetrahedral connectivity. Using the recently reported low-energy $\varepsilon$-LiMnO$_2$ polymorph as a model, we examine these factors through bond-valence site-energy and bond-valence pathway analyses combined with first-principles calculations. The resulting migration maps and tetrahedral statistics reveal distinct topologies across four LiMnO$_2$ polymorphs. Although the $\varepsilon$ phase and the lithiated-spinel phase Li$_2$Mn$_2$O$_4$ (hereafter spinel) have identical tetrahedral-type fractions, their 0-TM motifs form quasi-one-dimensional chains and a three-dimensional network, respectively. Climbing-image nudged elastic band calculations yield $\varepsilon$-phase barriers of 0.35–0.36~eV, compared with 0.41–0.53~eV in spinel, a difference that may be associated with distinct next-nearest corner-sharing shells. Ab initio molecular dynamics yields an apparent activation energy of 0.32~eV, while direction-resolved mean-squared displacements show preferential Li migration along $c$, supporting low-barrier quasi-one-dimensional diffusion. Delithiation calculations further show that differences in 0-TM connectivity and Li–Li separation between the $\varepsilon$ phase and spinel are associated with Li-site evolution and calculated voltage steps. These results link local environments and the spatial connectivity of 0-TM motifs to Li migration and delithiation, providing a structural perspective for metastable cathode design.

First-principles defect calculations are often limited by the cost of the large supercells required to suppress image interactions. Machine-learning interatomic potentials (MLIPs) provide another alternative, but training defect MLIPs typically requires thousands of structures and weeks of data generation. Since charge density is the key to density-functional-theory (DFT), we propose a machine-learning charge density (MLCD) route for predicting defect formation energies with higher data efficiency. We optimize the training set by integrating small supercells of varying sizes for better extrapolation, allocating their proportions based on spatial charge-density analysis. With only 96 supercells containing 16–96 atoms as the dataset, MLCD accurately predicts the formation energies of four intrinsic defects in 360-atom supercells, with defect-wise mean absolute error below 0.05 eV. In contrast, MLIPs trained on the same dataset can err by more than 1 eV. These results show that charge-density learning enables more robust cross-size transfer than direct energy-force fitting and that mixed-size data design can substantially reduce the cost of defect prediction.

The realization of Dirac criticality beyond the conventional Gross-Neveu-Yukawa (GNY) paradigm has become a major frontier in condensed matter physics. In this work, we introduce an $\mathrm{SO}(3)$-symmetric bilayer SLAC fermion model with tunable inter-layer interactions that exhibits a rich quantum phase diagram. As the interaction strength increases, the system undergoes two distinct phase transitions. The primary transition is a continuous boundary separating a Dirac semimetal (DSM) from an $\mathrm{SO}(3)$-broken ordered phase. Crucially, this transition evades the standard GNY universality class because the emergent order only gaps out a subset of the itinerant fermions. Using large-scale quantum Monte Carlo (QMC) simulations, we establish that this transition belongs to the Gross-Neveu-$\mathrm{SO}(3)$ universality class with $N=6$ irreducible Dirac cones and precisely extract the corresponding critical exponents. At stronger couplings, a second transition drives the system into an inter-layer $\mathrm{SO}(3)$-symmetric superconducting (SC) state. We provide strong numerical evidence that this transition is first-order. Our study provides new insights into the exploration of Dirac criticality beyond the standard GNY universality class, and also offers a distinct platform for investigating $\mathrm{SO}(3)$-symmetric superconductivity.

Molecular motion within living cells provides a window into the physical principles underlying intracellular organization and transport. Yet a fundamental limitation remains: measured trajectories are often short, noisy, and randomly oriented, rendering spatial anisotropies inaccessible to conventional analyses. We show that turning-angle statistics provide a robust approach for uncovering anisotropies in anomalous diffusion. Using a two-dimensional anisotropic fractional Brownian motion model with random orientations, we show theoretically and through simulations that turning-angle distributions preserve signatures of anisotropy. We apply this approach to single-particle tracking data, including quantum dots in the cytoplasm of HeLa cells and membrane proteins in hippocampal neurons. Turning angles reveal hidden anisotropies in the motion of quantum dots and Nav 1.6 channels, and in specific dynamical states of glycoprotein CD4. These results establish turning-angle analysis as a powerful strategy for detecting organization in complex environments and reveal that anisotropic anomalous diffusion is an overlooked feature of intracellular dynamics.

Ferroelectric polymers combine switchable polarization with the processability of soft materials, but their development has been dominated by poly(vinylidene fluoride) and related fluoropolymers, whose crystalline polar phases restrict mechanical compliance and domain design with spatial precision. Here we establish a generic design principle for creating intrinsically flexible ferroelectric liquid-crystal polymers through reactive polar mesogenic self-assembly. The approach creates polyfluoroalkyl-free polymer films in which robust ferroelectric order arises from liquid-crystalline molecular organization rather than crystalline phase formation. By transferring ferroelectric order from fluid mesogenic states into polymer networks, the resulting materials combine mechanical adaptability with programmable polar architectures. Especially, the photoalignment technology enables these polar states to be organized into pixelated domain architectures. This work establishes a design space towards soft ferroelectric polymers that integrate molecularly programmed polar order, mechanical tunability and environmentally conscious chemistry, expanding the design space of adaptive materials for flexible electronics, wearable systems and soft robotics.

Electrical control of single spin qubits is a major frontier for nanoscale, high-speed, and scalable quantum devices. Yet, extending it to highly shielded rare-earth 4f electrons remains an experimental challenge across solid-state platforms. Here we demonstrate all-electrical coherent control of a single Er electron spin, which is exchange-coupled to a nearby Ti atom. Scanning tunneling microscopy-based electron spin resonance with three-dimensional magnetic-field control enables comprehensive mapping of the resonance and Rabi frequencies, revealing pronounced anisotropies in both the Er g-tensor and the Er-Ti exchange interaction. The electrical modulation of the anisotropic Er-Ti coupling results in an efficient drive of the Er spin, allowing us to achieve near-gigahertz Rabi frequencies - a ten-fold improvement over the present record for rare-earth spin qubits. By establishing anisotropic exchange as a general resource for electrically accessing shielded rare-earth spins, our results open a new route to ultrafast and local control of rare-earth spins in solid-state quantum devices.

The Landau criterion, a cornerstone of quantum fluid dynamics, dictates that dissipation is forbidden for uniform motion below a critical velocity. Yet, the fundamental question of how acceleration reshapes the principles of quantum friction has remained open since Landau and Pitaevskii's seminal works. Here, we establish a theoretical framework for the quantum tribology of non-inertial motion, describing a probe particle undergoing composite translation and rotation within a weakly interacting Bose condensate. Using the nonlinear Gross-Pitaevskii equation, we show that centripetal acceleration fundamentally modifies the energy-momentum constraints on elementary excitations. This leads to a finite drag force in the subsonic regime of the probe particle motion, and a characteristic quantum stick-slip behaviour in the deeply supersonic regime – a direct generalization of the classical Landau-Pitaevskii picture. Beyond this dissipative response, we uncover a fundamentally distinct mechanism: the nonlinearity of the quantum fluid, combined with the broken symmetry of the trajectory, gives rise to a non-dissipative anomalous transverse force. This quantum Magnus-like response, emerging from the second-order density perturbation, performs no work and is rooted in the geometric asymmetry of the dynamically induced flow. Our findings lay the foundation for a universal program in quantum tribology of accelerated motion, establishing a direct and experimentally testable connection among non-inertial dynamics, nonlinear response, and topological symmetry breaking across platforms ranging from ultracold atoms and exciton-polariton condensates to cosmological analog systems.

Achieving effective electrostatic control of carrier transport in semimetals remains challenging due to strong screening and multiband effects. We report efficient low voltage top gated control of electronic transport in epitaxial Bi (111) thin films grown on GaAs (111) substrates using a printable hexagonal boron nitride ionogel. Magnetotransport measurements reveal pronounced nonlinear Hall conductivities arising from multiband electron and hole contributions. Remarkably, the application of a small gate voltage (less than 0.4 V in magnitude) leads to a systematic evolution of the low field Hall conductivity slope and the electron-hole compensation point. The response to gate voltage depends upon thickness and temperature. The observed behavior cannot be explained by a conventional Fermi level shift with rigid bands and instead indicates a non rigid band response associated with multiband effects and a gat tunable Rashba spin orbit coupling. Our results establish printable ionogel gating as a powerful approach to tune multiband transport in topological semimetals.

Using density functional theory, we predict that ultrathin (110) sheets of Rh, Pd, Ag, and Ir tend to transform into their (100) counterparts via lattice contraction. An approximately linear relationship between the transformation barrier and the energy difference between (110) and (100) sheets is revealed, demonstrating that the Bell-Evans-Polanyi (BEP) principle dominates. Furthermore, the critical thicknesses for these metals are also described by the BEP principle: below these thicknesses, the (110) sheets are no longer metastable and undergo spontaneous structural transformation.

Non-Hermitian dynamical quantum phase transitions (DQPTs) are intrinsically sensitive to the choice of inner product under nonunitary time evolution. Although the biorthogonal formulation based on associated states provides a normalized Loschmidt echo with a probabilistic interpretation, previous studies have found biorthogonal and self-normal DQPTs to occur in the same parameter regimes, suggesting that the two forms of dynamical criticality are concomitant. Here we demonstrate that this is not the case. In an exactly solvable periodically driven non-Hermitian Su-Schrieffer-Heeger chain, we uncover a finite biorthogonal-only Floquet DQPT regime, where the biorthogonal Loschmidt rate becomes nonanalytic while the self-normal Loschmidt rate remains smooth. The critical conditions are obtained analytically, showing that the onset of biorthogonal Floquet DQPTs is locked to the exceptional lines of the effective Floquet Hamiltonian, whereas self-normal criticality has no corresponding spectral boundary. Moreover, for each critical momentum, the biorthogonal DQPT exhibits a pair of critical times within every driving period, whereas the self-normal DQPT exhibits only one. Our results establish a fundamental distinction between biorthogonal and self-normal DQPTs, thereby opening a route toward new nonequilibrium quantum phenomena in non-Hermitian systems.

Electrides are an unusual class of ionic materials in which electrons localized in non-nuclear, interstitial regions act as anions within the crystal lattice. Here, we employ first-principles quantum mechanical calculations to investigate the structural, electronic, magnetic, and electride characteristics of elemental manganese (Mn) at an ambient pressure (0 GPa), focusing on its three crystalline phases: cubic ($\alpha$)-Mn ($I\bar{4}3m,no.217$), cubic ($\beta$)-Mn ($P4_132, no.213$), and hexagonal ($hex$)-Mn ($P6_3/mmc, no.194$). Our calculations reveal pronounced interstitial-electron character in all three phases, accompanied by spin-selective electron localization function (ELF), establishing elemental Mn as a non-van der Waals electride system. Bader charge analysis indicates substantial electron redistribution from the Mn host framework toward the interstitial anionic-electron (IAE) regions, with an effective charge transfer of approximately $-1.645e$, $-1.477e$, and $-1.083e$ per interstitial basin in $\alpha$-Mn, $\beta$-Mn, and $hex$-Mn, respectively. The electride character is further supported by the electronic density of states, where the IAE-associated states exhibit finite contributions near the Fermi level ($E_F$) and coexist with Mn-derived states, demonstrating their direct participation in the low-energy electronic structure. The combined electron localization function (ELF), effective charge transfer, and electron population due to IAE at $E_F$ therefore provide consistent evidence for interstitial anionic electrons in elemental Mn. To the best of our knowledge, this work provides the first systematic identification of spin-selective electride character in elemental Mn at ambient pressure, highlighting the possibility of exploiting its interstitial-electron states for unconventional electronic and magnetic functionalities.

Domain walls are carriers of spin transport whose controlled manipulation underlies a wide range of spintronic and information-processing technologies. Here we demonstrate tunable domain-wall transport in a weakly interacting nondegenerate ultracold Bose gas. We initialize a three-domain pseudo-spin-1/2 texture and observe spontaneous propagation of long-lived domain walls driven by exchange-mediated spin currents. By varying the orientation of the spin domains, we control the balance of spin currents across the walls and thereby tune their trajectories, including reversals of the initial direction of motion. Measurements reveal a crossover from an exchange-stabilized regime, in which coherent spin-exchange collisions suppress wall motion, to a diffusion-dominated regime characterized by rapid transport at thermal velocities. Numerical solutions of a quantum Boltzmann equation reproduce the observed dynamics and identify transverse phase gradients as an important control parameter governing domain-wall propagation. These results establish coherence and phase engineering as tools for programming spin transport in ultracold gases and provide a route toward controllable domain-wall dynamics in atomtronic systems.

In this study, we investigated the slow dynamic behavior of concrete with varying compressive strengths ($f'_c = 32\sim56$ MPa) and alkali-silica reaction (ASR) damage. Concrete prisms were conditioned by compressive loading and recovery was monitored by coda wave interferometry (CWI). A self-referencing temperature correction technique was used to minimize the effect of ambient temperature changes, demonstrating the importance of careful temperature control and correction. For intact specimens, both the recovery rate, $m_v$, and velocity drop magnitude, $|c|$, generally increased with compressive strength. Recovery times were calculated from the fit parameters, and higher strength specimens were found to recover faster (9.9 h for $f'_c = 32$ MPa, 6.6 h for $f'_c = 56$ MPa). When the same stress was applied, ASR-damaged specimens had increased softening and faster recovery rates, but longer recovery times (up to 458 h). These findings demonstrate the potential of slow dynamics in concrete characterization, through its ability to evaluate the strength of intact samples and its sensitivity to microcracking in damaged samples.