Papers

Ferroelectric materials have colossal optical nonlinearities, but their integration into quantum photonic chips is made challenging by the additional loss mechanisms that they introduce. Here we present a perturbative theory that expresses non-absorptive (elastic) photon scattering-induced loss as a functional of a general spectral density for spatial fluctuations of electric permittivity. We apply the theory to calculations of attenuation coefficients $\alpha$ in slab waveguides in order to compare two distinct loss mechanisms: Interface roughness and ferroelectric domain disorder. Our theory can account for realistic roughness without special symmetry considerations, and it demonstrates how to use electron microscopy images of ferroelectric domains to obtain explicit numerical predictions for $\alpha$. Loss is maximum when the mean domain length is comparable to the wavelength of light (Mie regime), indicating that, for telecom wavelengths, sub-micron domains (Rayleigh regime) or single domain waveguides provide equivalent strategies for reducing loss.

A highly exotic phenomenon in solid-state physics is the observation of magnetic quantum oscillations in insulators. For instance, in the Kondo insulator YbB$_{12}$ various groups reported the observation of such oscillations seemingly originating from Fermi surfaces, though this contradicts the concept of an insulator having no charged quasiparticles. In this study, we searched for quantum oscillations in YbB$_{12}$ by using bulk-sensitive ultrasonic experiments in high magnetic fields up to 65 T and down to 485 mK. For that, we utilized an YbB$_{12}$ single crystal that, in previous experiments, revealed oscillations in the magnetoresistance in the insulating state. We confirmed oscillation-like behavior of the magnetoresistance as well as field-dependent oscillations in the magnetocaloric effect. However, we could not observe magnetoacoustic quantum oscillations in the insulating state, only in the field-induced metallic state. In the insulating state, we found some anomalies in our ultrasound data, the origin of which remains elusive. Our findings provide further information on the puzzling behavior of the insulating state of YbB$_{12}$.

We numerically examine a binary system of particles with repulsive interactions, where one species is driven by a rotating drive and the other is subjected either to a constant drive in a fixed direction or to a rotating drive that is out of phase with the first species. As a function of rotation frequency, we find a variety of order-disorder transitions and pattern forming states, including density-modulated stripes, partially jammed states, phase separated fluids, and mixed fluids. When one species has a constant drive and the drive on the other species is rotated at low frequencies, the system switches between different pattern forming phase-separated lanes including density-modulated stripes and partially jammed states, similar to what is observed for oppositely driven colloids. The lanes tend to align with the net direction of rotation, resulting in a series of order-disorder switching transitions. The transport curves show abrupt jumps up or down at the transitions, which also correspond with changes in the topological order. We find similar switching transitions when both species rotate out of phase with each other. For intermediate driving frequencies, the system becomes increasingly fluid-like and the laning behavior is lost. At high frequencies, however, the system can again exhibit patterned flow when the rotation orbits become smaller than the average spacing between particles. The switching is reduced when a finite temperature is included, but even for temperatures at which the uniform equilibrium bulk system is liquid, the partially jammed state can generate local density enhancements that lead to recrystallization. We demonstrate the pattern switching behavior for systems with different screened repulsive interaction potentials.

Molecular-scale interactions between solvated macromolecules and solid surfaces govern a large number of processes, from biology to engineering. Yet, despite extensive characterization at the macroscopic level, our molecular understanding of polymer/surface interactions remains limited, particularly under out-of-equilibrium conditions. Here, we combine wide-field single-molecule microscopy with microfluidic transport to directly track the nanoscale dynamics of individual fluorescently tagged macromolecular PEG adsorbates, and investigate their subtle couplings with interfacial hydrodynamic flows. At equilibrium, we evidence marked surface dependence, with macromolecular dynamics switching from heterogeneous non-Brownian diffusion on hydrophilic glass to bidimensional Brownian-like transport in an interfacial physisorbed state on hydrophobic self-assembled monolayers. While for hydrophilic glass, the effect of the flow is restricted to an advective contribution during solvent-mediated flights, we uncover for the hydrophobic surfaces a peculiar regime of mixed macromolecular friction, whereby the adsorbed chain rubs on the solid wall while being continuously dragged by the near-surface hydrodynamic flow through interfacial slippage. Through joint analysis of equilibrium and out-of-equilibrium transport, we finely disentangle these molecular level frictional interactions with both the solid surface and the interfacial liquid. Beyond population-averaged dynamics, we further unveil a broad distribution of friction coefficients associated to individual chains, which we attribute conformational heterogeneities with sluggish reorganization timescale. By enabling direct observations of molecular-scale interfacial dynamics, our approach provides a novel molecular picture of macromolecular friction and adsorbate/surface interactions at flowing solid/liquid interfaces.

Hydrogen is a promising candidate for the light element in terrestrial planetary cores. Its incorporation into iron causes significant volume expansion, leading to a substantial density deficit. Although extensive studies have been conducted on iron hydride (FeH$_{x}$) with the fcc structure, the thermoelastic properties on FeHx with hcp structure (hcp-FeH$_{x}$) remain unconstrained because of the experimental difficulties to control hydrogen content. Here, we synthesized hcp-FeH$_{x}$ with controlled hydrogen contents under high-pressure and high-temperature conditions. We carried out in situ X-ray diffraction measurements on hcp-FeH$_{x}$ at 10–25~GPa and 300–900~K using a Kawai-type mutilanvil apparatus and constructed their equations of state. By combining our results with previously reported equations of state for hcp-Fe and experimental determinations of hydrogen content in hcp-FeH$_{x}$, we demonstrated that the discrepancies in the hydrogen-induced volume expansion coefficient can be clearly explained by its pressure and temperature dependence. Our results revealed that the hydrogen-induced volume expansion of hcp-Fe exhibits a strong temperature dependence at low pressures, but its temperature effect significantly weakens with increasing pressure. We also showed that the density reduction of Fe by hydrogenation depends on its crystal structure. These findings demonstrate that estimates of hydrogen content in iron at planetary interior conditions based on hydrogen-induced volume expansion need to be revised by properly accounting for its $PT$-dependence and crystal structure.

Optically active solid-state spin defects are promising candidates for quantum applications, however a unified theoretical framework to predict their spin dynamics at high temperatures is not yet available. Here, using Kubo linear–response theory, we derive expressions of spin-lattice and decoherence times \(T_1\) and \(T_2\) in terms of correlation functions of spin–lattice couplings. We then evaluate \(T_1\) and \(T_2\) from molecular dynamics and spin–lattice interaction time–series generated by state–of–the–art machine learning models trained on ab–initio data. Finally we measure \(T_1\) times for the NV center in diamond and compare experimental and theoretical results, showing excellent agreement.

In a recent article [J. G. Wang, U. Dhumal, M. E. Zakhari, and R. N. Zia, AIChE Journal 72, e70275 (2026).], the authors discuss the absence of simulations of monodisperse hard spheres in which a metastable fluid spontaneously nucleates into a stable fluid-crystal coexistence. Here, we show that such a simulation can be readily accomplished with standard simulation methods.

Ensembles of negatively charged boron vacancy (V$_{\text{B}}^-$) centers in hexagonal boron nitride (hBN) have emerged as a two-dimensional spin qubit system interfaced with optics to advance nanoscale quantum sensing. However, a comprehensive description of its optically detected magnetic resonance (ODMR) signal remains challenging due to the strongly correlated nature of the excited electronic states involved in its optical cycle. In this work, we model the energetics, structural relaxation, and transition rates of the V$_{\text{B}}^-$ center using a high-level wave-function-based electron correlation method (CASSCF-NEVPT2). We provide a thorough analysis of the excited state fine structure and pseudo Jahn-Teller effects, singlet-triplet quasi-degeneracies, photoluminescence parameters, intersystem crossing pathways, and stress-dependence of the fine structure and decay parameters. Our findings not only clarify the fundamental behavior of the V$_{\text{B}}^-$ center in hBN but also establish the theoretical foundation for advancing the V$_{\text{B}}^-$ center's readout for integrated 2D quantum sensors.

One dramatic feature of network liquids is the emergence at low temperatures and high pressures of polyamorphism, where multiple distinct liquid phases are accessed in a single material. Polyamorphism can arise from the competition between distinct local inherent structures corresponding to bonded and nonbonded ordering. Thermal bond breaking thus can lead to a phase transition often accompanied by thermodynamic anomalies away from the transition itself, such as the familiar density maximum in water at atmospheric pressure and $4^\circ$ C. Water exhibits network interactions in the form of hydrogen bonding between water molecules. The polyamorphic transition in water, however, is difficult to study due to the rapid crystallization of supercooled water and due to glassy effects at low temperatures. In the present work, we propose a simple microscopic model where the glassy and thermodynamic properties are both calculated directly from the microscopic potentials. The model contains a liquid-liquid phase transition, which, after tuning the microscopic parameters, may be located either above, near, or below the glass transition. By applying the Random First Order Transition theory of the glass transition to this simple microscopic model, we shine light on the interplay of polyamorphism and glassy properties in network liquids. We show the connection between the thermodynamic water-like anomalies and corresponding anomalies in the glassy kinetics. The analysis unveils key details on the way glassy dynamics modifies the phase transition kinetics. When the parameters of the model are tuned to produce a phase diagram resembling that of water, the liquid-liquid phase transformation near $T_g$ occurs via “nanonucleation”, resulting in extremely small domains sizes and nonclassical nucleation kinetics which are predicted from the RFOT theory.

In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction, including auto-encoders, principal component analysis and Artificial Neural Networks, and time-series analysis, including LSTM and Gated Recurrent Unit (GRU).

Tracer diffusion in crowded environments is central to many biological and soft matter systems, but quantitative frameworks for linking tracer motion to environmental structure remain limited, and the co-dependence among geometric variables that facilitate or hinder tracer transport is not yet well understood. Here, we study the transport of rigid tracers in suspensions of soft particles and within living cells. Experiments reveal a transition from diffusive to confined motion as the matrix area fraction increases. The observed ensemble-level statistics, including the mean-squared displacements (MSDs), can be reproduced using a minimal simulation. Using simulation outputs, we train a parallel partial Gaussian process (PPGP) model that rapidly predicts MSDs from matrix geometric variables, including area fraction, particle size, and polydispersity. Analysis reveals that tracer transport is primarily governed by accessible pore sizes and that distinct global structures can produce indistinguishable MSDs. While MSDs do not uniquely encode system geometrical parameters, we nevertheless find correspondence between pore size distribution and the ensemble MSDs. By modeling matrix self-diffusivity, the minimal model can also phenomenologically describe MSDs of internalized tracer particles in cells. The framework enables rapid inference of structural properties in crowded environments, including transport in the intracellular environment.

Quantum state preparation can be strikingly counterintuitive: the fastest route to a target state need not start from the apparently closest initial condition. We uncover such a quantum Mpemba effect in the dynamical generation of quantum magic (nonstabilizerness), quantified by the stabilizer Rényi entropy, in $\mathrm{U(1)}$-symmetric random circuits initialized from tilted product states. States with lower initial magic can generate magic faster than states with higher initial magic. The acceleration is not determined solely by the conserved-charge distribution. Two initial-state families with identical initial magic and identical charge distribution exhibit qualitatively different magic-growth dynamics, depending also on the spatial structure of the initial state within each charge sector. Analogous magic Mpemba effects in $\mathrm{SU(2)}$-symmetric circuits and under nonintegrable Hamiltonian dynamics further show that the phenomenon is tied neither to Abelian symmetry nor to random-circuit dynamics, establishing quantum magic as a distinct arena for Mpemba physics.

High-rate bivariate bicycle (BB) codes are promising low-overhead quantum memories, but their stabilizer checks typically have weight $6$ or higher, making syndrome extraction challenging. We introduce subsystem bivariate bicycle (SBB) codes, a translation-invariant CSS subsystem construction that realizes BB-code logical structure using local weight-$4$ gauge measurements. Their stabilizer syndromes are inferred by multiplying the corresponding gauge outcomes. We further show that nonlocal stabilizers in translation-invariant CSS subsystem codes can be detected using a determinantal-ideal criterion based on the gauge-operator commutation matrix. When this criterion excludes nonlocal stabilizers, a finite-depth Clifford circuit decouples gauge qubits and identifies the protected subsystem with a corresponding BB stabilizer code. An SBB code is topological, meaning that it has no nontrivial local logical operators, if and only if the corresponding BB code is topological. A finite search yields low-overhead examples including $[[27,6,3]]$, $[[75,10,5]]$, and $[[108,12,6]]$; the latter encodes six times more logical qubits than a subsystem surface code at the same block length and distance. These results show how gauge degrees of freedom can make high-rate BB logical structure compatible with local weight-$4$ syndrome extraction.

We study the complexity of approximately contracting translation-invariant tensor networks. The computational cost of row-by-row tensor network contraction, which defines a discrete time evolution governed by a fixed transfer matrix, is associated with the entanglement of the state of a row. By analyzing a family of tensor networks whose transfer matrices interpolate between chaotic Floquet and strongly non-unitary limits, we uncover a transition between volume- and area-law entanglement in states evolved under the transfer matrix. We show that deep in the volume-law phase the spectrum of the transfer matrix in the complex plane consists of a dense ring with a sharp outer edge, reminiscent of behavior identified for non-unitary random matrices. At late times an evolving row state therefore has significant contributions from many eigenvectors with nearly degenerate eigenvalue magnitudes. In the area-law phase, there is instead a distinct leading eigenvalue. Our results establish connections between contraction complexity, spectral properties of the transfer matrix, and purification under non-unitary dynamics.

Nonlinear hydrodynamic interactions can change the relaxation of fluctuations from exponential to power-law decay at late times. Schwinger-Keldysh effective field theory provides a standard framework for describing such fluctuation effects, where the nonlinear late-time behavior is encoded in loop corrections. Extracting this behavior requires identifying the singularities of loop integrals, whose structure becomes increasingly intricate beyond simple models. We apply Landau singularity analysis to two-point functions in effective field theories and determine the singularities induced by nonlinear interactions without performing the loop integrations explicitly. From these frequency-space singularities, we extract nonlinear relaxation modes that control the late-time behavior. When gapless modes are present, these modes produce power-law decay at late times. Our results give a systematic singularity-based description of nonlinear late-time relaxation in a broad class of macroscopic effective theories.

The study of disorder effects in electronic systems is one of the central themes in physics. It is well established that in the Anderson localization regime, the localization length of electrons decreases monotonically as the disorder strength increases. Here, we demonstrate that the conventional Anderson localization paradigm fails completely in describing an isolated band with quantum metric, where the quantum metric of the band defines a length scale called the quantum metric length. For an isolated band with a finite bandwidth separated from other bands by a band gap $\Delta$, weak disorder results in conventional Anderson localization behavior. However, as the disorder increases, the localization length ceases to decrease and becomes pinned at a value proportional to the quantum metric length, forming a localization length plateau. We term the regime within this localization length plateau as the quantum metric localization regime. Remarkably, the localization length does not deviate from the plateau until the disorder strength far exceeds $\Delta$. We refer to this strong protection against disorder, characterized by the quantum metric length, as quantum metric protection. In this work, we first numerically demonstrate quantum metric localization using a 1D Lieb lattice. We then provide a simple physical picture based on the properties of Wannier functions to explain the origin of the localization length plateau. A supersymmetric field theory approach explains why the localization length is proportional to the quantum metric length and captures the crossover from Anderson localization to quantum metric localization. Our conclusions are broadly applicable to disordered electronic, photonic, and acoustic systems.

Metallic amorphous ferrimagnets of 3d transition metals (TM) and rare earths (RE) with 4f electrons exhibit rich magneto transport behavior due to the interplay between the 3d and 4f magnetic sublattices and their interaction with mobile charges. Tuning the TM and RE concentrations in the alloy can effectively modulate the compensation temperature, where the moments of the two sublattices point in opposite direction leading to a net zero magnetization. Despite extensive magnetotransport studies in Gd and Tb based 3d 4f systems, Ho based alloys remain comparatively underexplored, even though Ho possesses the largest orbital angular momentum (OAM) among the lanthanides. This unquenched OAM can strongly impact magnetic anisotropy and magnetotransport in ferrimagnetic heterostructures. Here, we have investigated the anomalous Hall resistivity , dc magnetization, and spin Hall magnetoresistance (SHMR) of Co78Ho22.Al film and a Pt.Co78Ho22.Al heterostructure deposited using multitarget magnetron sputtering. The Hall resistivity of both systems shows a distinct sign reversal and prominent wing-shaped hysteresis loops in the vicinity of the compensation temperature (Tcomp), which is accompanied by the minimum saturation magnetization near Tcomp. Furthermore, the SHMR in Pt.Co78Ho22.Al film is enhanced due to the Pt layer. These HM interface-induced prominent features of magneto-transport are addressed in the light of the existing theories of spin flop transitions, spin orbit torque, and microscopic phase separation, which may lead to the formation of 3d and 4f magnetic clusters in the film.

Phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) has emerged as a promising electronic structure method for correlated electronic systems. However, the quality of its predictions depends critically on the choice of trial wavefunction, and it is not obvious how to make an optimal choice especially for strongly correlated states of large systems. Mean-field wavefunctions are compelling trial wavefunction candidates as they map directly to chemical concepts and can be obtained with $O(N^4)$ cost. Yet in the strongly correlated regime one faces a symmetry dilemma and the existence of multiple nearly-degenerate solutions. In this work we investigate active space models of [2Fe-2S]$^{2+}$, mixed-valent [4Fe-4S]$^{2+}$, and [4Fe-4S]$^{4+}$ and explore the sensitivity of ph-AFQMC to the choice of unrestricted Hartree-Fock trial wavefunction. We find that chemical properties and physical symmetries, rather than the variational energy, ought to guide the choice of mean-field trial for ph-AFQMC (or reference state for coupled cluster models), and show that surprisingly accurate ground-state energies for these systems can be obtained. However, in all cases we find a rapidly vanishing overlap between the stochastic wavefunction and the UHF trial, indicating that the trials are suboptimal importance functions. By analogy to a similar situation in the stretched helium dimer cation, we show how this sampling bias pushes ph-AFQMC towards artificially negative energies, which evidently can be compensated for by the phaseless bias in certain cases.

Venture capital outcomes are dominated by a small number of extreme successes, making it difficult to distinguish investor skill from favorable realizations in a highly skewed return distribution. We study this question by comparing empirical VC portfolios to a constrained random benchmark that preserves key portfolio characteristics, including timing, geography, sector composition, and portfolio size, while randomizing individual company selection. Across funding stages, empirical portfolio distributions appear remarkably close to their random benchmarks. We find no evidence that portfolio construction increases the probability of high-multiple outcomes: the right tail remains statistically indistinguishable from random allocation. Deviations in the lower part of the distribution are small and sensitive to the interpretation of zero outcomes, suggesting at most weak evidence of downside improvement. We further introduce a rank-based benchmark distribution to evaluate outperformance at each position in the cross-section. This analysis shows that even the best-performing portfolios do not exceed the outcomes expected for their rank under random sampling. Our results suggest that VC portfolio outcomes are largely consistent with constrained random allocation, highlighting the difficulty of identifying aggregate skill in heavy-tailed investment environments. A similar conclusion holds for the performance of financial analysts in predicting future earnings.

Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective descriptions of strongly correlated phases and engineered dynamics. We introduce a gauge-invariant graph neural network (GNN) architecture for Abelian lattice gauge models, in which symmetry is enforced explicitly through local gauge-invariant inputs, such as Wilson loops, and preserved throughout message passing, eliminating redundant gauge degrees of freedom while retaining expressive power. We benchmark the approach on both $\mathbb{Z}_2$ and $\mathrm{U}(1)$ lattice gauge models, achieving accurate predictions of global observables and spatially resolved quantities despite the nonlocal correlations induced by gauge-matter coupling. We further demonstrate that the learned model serves as an efficient surrogate for semiclassical dynamics in $\mathrm{U}(1)$ quantum link models, enabling stable and scalable time evolution without repeated fermionic diagonalization, while faithfully reproducing both local dynamics and statistical correlations. These results establish gauge-invariant message passing as a compact and physically grounded framework for learning and simulating Abelian lattice gauge systems.

Within the toric-code phase, we study parameterized families of topologically ordered states. We construct $1$- and $2$-parameter families of local Hamiltonians and confirm their non-triviality via topological pumping. For the $1$-parameter family, we show that the $em$-exchange defect is pumped into the bond Hilbert space of a tensor-network representation. For the $2$-parameter case, we construct a “pump of a pump” that transports an $S^1$-family of a system in one lower spatial dimension. Using similar methods, we also present a $1$-parameter family with a higher-order anyon pump that produces corner-localized anyon modes. These constructions provide explicit lattice realizations and concrete diagnostics of family-level topology. We use recently developed boundary algebra methods to study the non-triviality of these families.

Understanding how cities transition from free-flowing to congested traffic remains a central open problem in urban science. Here we show that city-scale congestion undergoes a reproducible nonlinear transition analogous to an order-disorder phase transition in statistical mechanics, in which aggregate mobility acts as a control parameter and jam extent as a collective order parameter. Crucially, this analogy is not merely formal: we derive and empirically identify an effective thermodynamic temperature with concrete physical meaning, quantifying infrastructural heterogeneity and how broadly a city explores congestion configurations as demand increases. Low-temperature cities are congestion-fragile: small mobility increases trigger sharp, system-wide jam transitions. This framework further reveals that the macroscopic fundamental diagram is an incomplete description of the traffic state: it emerges as a projection of a richer free-energy landscape governed by entropy-capacity trade-offs. Validated across 46 cities in Latin America and the Caribbean and independently confirmed with loop-detector data from 8 cities on three continents, these results establish a physics-based foundation for comparing urban traffic resilience and anticipating congestion regime shifts under changing mobility demand.

Recent experiments have demonstrated that measurements of the entropy change associated with the addition of electrons to semiconductor- and graphene-based quantum dots accurately quantify the spin and orbital degeneracy of the states into which they are added. However, measuring more exotic entropies requires probing the entropy change of an entire system in response to an added particle. Here, we demonstrate that Maxwell relation-based measurements probe not only the entropy change associated with the added electron but also that of the surrounding system as it responds to that electron. Using a pair of capacitively coupled GaAs quantum dots, we show that charge measurements on one dot reveal entropy changes associated with the entire two-dot system, both at weak dot–reservoir coupling where microstate counting applies and at stronger coupling where numerical renormalization group calculations are required.

We solve an infrared effective holographic model of a non-Fermi liquid at finite temperature that satisfies Luttinger's theorem and incorporates long-range Coulomb interactions. Motivated by the absence of a Luttinger-counting Fermi surface in standard Reissner-Nordstrom holographic metals, we consider a Maxwell-Chern-Simons theory in a static anti-de Sitter-Schwarzschild background, coupled to an LU(1) gauge field rather than a conventional U(1) gauge field. By an appropriate choice of boundary conditions, we obtain a damped collective plasmon mode whose plasma frequency scales as predicted by Luttinger's theorem. We further analyze the density-density correlator in the absence of long-range Coulomb interactions and identify a contribution consistent with a Lindhard-like continuum.

Zero-energy flat bands within the superconducting gap can give rise to competing ordered phases. We investigate such phases in topological superconductors based on the magnetic adatom platform hosting a flat band of Majorana edge states. Our self-consistent calculations of the superconducting order parameter show the emergence of both a pair density wave with edge-localized amplitude modulations and a phase crystal characterized by edge-localized phase modulations. These two phases lower the free energy of the system by gapping out the Majorana flat band, as dictated by winding numbers, which are primarily tuned by the chemical potential. In fact, at zero temperature the uniform superconducting solution with Majorana flat band never survives and the phase diagram features a pair density wave, while the order parameter transitions into a phase crystal when amplitude modulations are insufficient to hybridize all the Majorana states. A broad intermediate region connects these two phases with comparable modulations in both amplitude and phase. At finite temperatures, the pair density wave survives up to around 80% of the bulk superconducting transition temperature, while the phase crystal only appears at lower temperatures and the intermediate region is strongly suppressed. Our findings establish the ubiquity of emergent nonuniform superconducting phases and their temperature-dependent behavior in topological superconductors.

We describe the development of 1x1 mm2 InAs thermoradiative diodes grown by molecular beam epitaxy with emphasis on their reverse saturation current and break-down voltage. P-i-n diode structures grown at 450 C, with As2 flux around 3 times stoichiometry and an In effusion cell tip temperature 150 C higher than the base temperature, exhibit the best results with breakdown voltages above 0.3 V and reverse saturation current densities 200 times the radiative limit.

It is expected that the statistical fluctuations of local observables in large quantum systems obey the central limit theorem, and approximate a normal distribution as their size grows. Here, we prove a version of the Berry-Esseen theorem for quantum lattice systems, which strengthens that central limit theorem by providing a rigorous convergence estimate towards the normal distribution for large but finite system size. Given a local quantum Hamiltonian on $N$ particles and a quantum state with a finite correlation length, the result states that the measurement of local observables such as the energy follows a normal distribution, up to an error scaling as $\mathcal{O}\left(N^{-\frac{1}{2}} \text{polylog}(N)\right)$, which is optimal up to logarithmic factors.

We study quantum charge transport in two-dimensional networks in the presence of a magnetic field and spin-orbit interaction. The interplay of the corresponding Abelian and non-Abelian gauge fields leads to an intricate behavior of the conductance, which has different periodicities in the diffusive and ballistic regimes. We classify all configurations of magnetic and spin-orbit fields where a logarithmically divergent weak-(anti)localization correction appears in the diffusive regime. The conductivity of topologically distinct configurations is the same in the diffusive regime but different in the ballistic regime. The proposed setup provides a feasible realization of quantum graphs with non-Abelian gauge fields.

We demonstrate machine learning (ML) based reconstruction of terahertz transmission spectra using an electrically tunable grating-gate AlGaN/GaN plasmonic-crystal analyzer. The analyzer encodes the transmission spectrum into a voltage-dependent intensity, which is then inverted by an ML algorithm. A feedforward neural network trained on a synthetic dataset is validated experimentally on four samples in standard Fourier Transform Infrared (FTIR) mode and in direct (fixed-mirror) acquisition mode. The network achieves a mean square error (MSE) of the reconstruction of 0.015 in FTIR mode and 0.038 in direct mode, correctly identifying six out of seven ground-truth resonances in each mode. Against a first-difference Tikhonov regularization baseline, the mean reconstruction error is reduced 3.6 times in FTIR mode and 1.55 times in direct mode, with fewer spurious peaks and lower peak-position errors. Voltage-tunable plasmonic filtering combined with neural-network inversion establishes an interferometer-free architecture for THz spectral reconstruction.

Ising spin-orbit coupling (ISOC) can strongly protect superconductivity against exchange-field-induced depairing, typically leading to critical fields far exceeding the Pauli limit and continuous (second-order) phase transitions. Here, using a free-energy approach, we demonstrate that first-order transitions can emerge in superconductors with weak ISOC under large exchange fields. In this regime, conventional theoretical approaches based on the gap equation fail to determine the thermodynamic critical field and instead yield only the supercooling field. Moreover, we identify two pronounced in-gap coherence peaks in the quasiparticle spectra, which represent the weak-ISOC manifestation of the previously reported mirage-gap states. Our results establish the importance of free-energy analysis in describing the first-order phase transitions in Ising superconductors and reveal distinct spectroscopic signatures of the weak-ISOC regime.