Papers

We investigate how cyclic loading evolves the structure and deformation behaviors of a granular raft composed of particles floating at an air-oil interface. The raft has a disordered particle packing structure, and is cohesive due to capillary interactions between particles. Under uniaxial cyclic loading with a small strain amplitude, the raft's packing structure experiences an aging process characterized by logarithmically increasing packing fraction and decreasing structural heterogeneity. The observed structural change is due to particle dynamics that are organized around morphologically evolving voids in the raft. The raft is then subjected to quasi-static tension or compression tests until failure. In comparison with non-aged rafts, the rafts that experienced cyclic loading show a higher strength, higher stiffness, and lower ductility, along with qualitatively different features, such as a stress overshoot in the loading curve.

Origami, which transforms flat sheets into three-dimensional shapes through folding patterns, has inspired the emergence of deployable systems in architecture and civil realms. Most existing origami-inspired deployable systems are based on rigid or curved-crease origami types. However, they inherently lack shape stability and require additional supports to maintain their deployed shapes. These lead to a fundamental trade-off between deployability and shape stability, which remains a major challenge for large-scale origami systems. Multistable origami, in contrast, achieves energy stability across multiple configurations during deployment. This unique characteristic enables it to maintain stable shapes even under external loads. These properties allow multistable origami to achieve both shape stability and deployability, offering high potential for self-supporting deployable systems in architectural applications. However, realizing both large-scale and structurally stable systems using a single origami faces many practical constraints. To overcome these limitations, origami block assembly has emerged as an effective approach to form global systems. This approach enables flexibility in global geometry and mechanical behaviors while offering reconfigurability. These indicate that the complementary potential of multistable origami and block assemblies can provide a promising solution. This study aims to address the challenges of applying deployable origami to large-scale architectural systems by leveraging the potential of multistable origami as modular building blocks. From a geometric standpoint, we explore design methods for stable configurations of multistable origami blocks that can align and interlock with each other. From a mechanical standpoint, we explore stiffness-controllable design methods that ensure self-supporting and load-bearing capabilities through geometric parameters.

We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries, even after it has been completely evaporated. For this purpose, image analysis techniques were used to characterize patterns obtained from drying suspensions using single solvents (water, ethanol, acetone) and two-component solvents (water-ethanol mixtures at different concentrations). Frequency histograms were generated based on nine morphological features, taking into account their size, shape, geometry and orientational ordering. Subsequently, we used these histograms as input data into artificial neural network variants to determine the set of features that lead to the higher accuracy in solvent identification. We obtained an average accuracy of $96(\pm 1)\%$ considering all solvents in the analysis. The highest accuracy was obtained with sets of features that include the crack area distribution. The proposed protocol can help to determine the combination of features that optimize pattern recognition in other fields of science and engineering.

We develop a hydrodynamic framework for the interactions and collective dynamics of force dipoles embedded in a compressible fluid membrane supported by a shallow viscous subphase. Starting from the generalized two-dimensional Stokes equations with shear, dilatational, and odd (Hall) viscosities, we derive an exact real-space Green tensor using Hankel transforms. The resulting tensor is characterized by three hydrodynamic screening scales associated with shear, compressional, and odd-viscous modes, and smoothly reduces to the standard limiting cases of incompressible membranes and compressible parity-symmetric membranes, while also capturing the chiral response generated by odd viscosity. Using this Green tensor we obtain the velocity and vorticity fields generated by a force dipole and formulate the dynamical system governing interacting dipoles. The analysis reveals several distinct dynamical regimes and identifies observables that isolate the antisymmetric odd-viscous contribution to dipole interactions, including transverse drift and chiral relative motion.

Halide segregation in metal halide perovskites limits their bandgap tunability and hinders their adoption in tandem solar cells and light emitting diodes. Here, we reveal the thermodynamic driving force behind halide segregation in mixed halide (Br-I) perovskites. By performing first-principles calculations on slab models with varying bromide and iodide distributions, we demonstrate that bromide ions preferentially occupy surface sites over bulk sites. Our simulations show that the segregation tendency is higher in MAPb(Br$_x$I$_{1-x}$)$_3$ (MA=methylammonium) compared to FA$_{0.8}$Cs$_{0.2}$Pb(Br$_x$I$_{1-x}$)$_3$, highlighting the role of the A-site cation. To quantify this effect, we establish a descriptor for halide segregation: the difference in defect formation energies of Br antisite defects between the bulk and the surface, which confirms the role of the A-site cation at equimolar Br-I concentration. Furthermore, we identify the localization of photo-generated holes near iodide ions, which triggers their oxidation and accelerates the formation of iodide vacancies, thereby promoting segregation. Overall, this work establishes defect thermodynamics as a framework for understanding halide segregation and provides a structural basis for designing stable mixed halide perovskites.

Single-electron spin qubits defined in quantum dots are used as building blocks of a semiconductor-based quantum computer. Readout in a scaled-up version of such a quantum computer is expected to rely on the Pauli Spin Blockade (PSB) mechanism. A desired functionality of PSB readout is that it reveals two bits of information on the two spin qubits that are involved in the process, such that the four computational basis states can be discriminated. In this work, we propose and quantitatively analyze an experimental procedure, based on gate reflectometry, which enables this four-state discrimination in a single measurement. We provide an intuitive recipe to maximize the contrast between the quantum capacitances of the four basis states. Focusing on silicon double quantum dots equipped with a micromagnet, we quantify how amplifier noise and phonon-mediated relaxation influence readout fidelity. Our results highlight a realistic opportunity to mitigate the overhead of readout ancilla qubits in a spin-based quantum computer.

In this work, we benchmark three leading composition based Machine Learning (ML) frameworks, MODNet, CrabNet, and a random forest model based on Magpie features, predicting the properties of battery electrode materials using the Materials Project Battery Explorer dataset. We evaluate these models based on predictive accuracy, visualize numerical features using two-dimensional embeddings, and quantify performance using standard metrics. Our results demonstrate that CrabNet consistently outperforms the other models across all tests. To validate these findings, we employ bootstrap resampling and two cross-validation (CV) strategies (leave-one-cluster-out and stratified 5-fold CV), comparing each model against a control baseline, using unseen experimental data as a hold-out test. We also apply unsupervised clustering using t-SNE and DBSCAN on physically observed features extracted from matminer, revealing coherent material groupings without prior labels. The final selected model consistently improves over controls, and we believe can be used as a early stage oracle for electrode materials composition screening. Our study aims to identify the error distributions and limitations of the approach, discussing the challenges with developing robust ML models. Despite these constraints, our findings suggest the final selected model is effective for early-stage compositional screening.

Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the system is open, this task becomes considerably harder than in the purely unitary case, because coherent and dissipative mechanisms can produce similar measurement statistics and long-time data can be insensitive to coherent couplings. Here we tackle this so-called Lindbladian learning problem of open-system characterisation with maximum-likelihood on Pauli measurements at multiple experimentally friendly transient times, exploiting the richer information content of transient dynamics. To navigate the resulting non-convex likelihood loss-landscape, we augment the physical model neural differential-equation term, which is progressively removed during training to distil an interpretable Lindbladian solution. Our method reliably learns open-system dynamics across neutral-atom (with 2D connectivity) and superconducting Hamiltonians, as well as the Heisenberg XYZ, and PXP models on a spin-1/2 chain. For the dissipative part, we show robustness over phase noise, thermal noise, and their combination. Our algorithm can robustly infer these dissipative systems over noise-to-signal ratios spanning four orders of magnitude, and system sizes up to $N=6$ qubits with fewer than $5 \times 10^5$ shots.

Efficient transport of spin and orbital moments, and their electrical detection, are among the main challenges in spintronics and orbitronics. In magnetic insulators, these currents are mediated by magnons. In addition to carrying spin and orbital moment, the orbital motion of a magnon combined with its magnetic moment, generates an effective electric dipole moment. Here, we develop a theoretical framework for Seebeck- and Nernst-type transport of the magnon orbital moment (MOM) and its associated electric dipole moment (EDM). We identify a Drude-like scattering contribution and an intrinsic component governed by the generalized Berry curvatures of magnon bands. We show that a measurable transverse voltage generated by the EDM current provides a direct electrical detection scheme for magnon orbital transport. Applying our theory to an hexagonal altermagnet, we obtain an experimentally accessible voltage of approximately $0.4~\mu$V. Our results establish a concrete electrical probe of magnon orbital transport and highlight magnons as potential low-dissipation information carriers for orbitronics.

We identify a fundamentally new class of resonances in light scattering from a pair of point-like electric dipoles. These resonances arise from multiple scattering response and correspond to poles of the two-particle Green function. The realization of exact resonances requires violation of the optical theorem, which holds in equilibrium systems but can be violated under nonequilibrium conditions, without violating causality. As a concrete example, we show that a pumped two-level atom can realize the required polarizability and access the exact resonance regime. Our results for one electric and one magnetic dipole show how resonances can amplify a weak magnetic response of a single dipole to the incident field. In equilibrium systems, only approximate resonances with finite amplification are possible. Using the Drude model for metallic nanoparticles, we show that while equilibrium resonances enhance the scattering, they never exceed the optimal single-dipole response.

Spectral control of closely spaced excitonic transitions is central to valleytronic photonics, nanoscale light sources, and wavelength-encoded sensing. In monolayer molybdenum disulfide (MoS2), the A and B excitons are separated by only tens of meV, making selective excitonic emission control both fundamentally important and technologically challenging. Here, we numerically investigate plasmon-enhanced excitonic emission from monolayer MoS2 coupled to vertically oriented hollow gold nanocylindrical cavities through a dielectric spacer. Finite-difference time-domain simulations combined with a photoluminescence-rate framework enable separate evaluation of excitation enhancement, radiative decay modification, nonradiative quenching, and excitonic charge generation. By tuning the cavity aspect ratio, the localized surface plasmon resonance is selectively aligned with either the A- or B-exciton transition, while the spacer thickness and refractive index regulate near-field coupling and the local density of optical states. Under optimized conditions, the excitation rate is enhanced by up to 4.34-fold and the radiative decay rate by more than 40-fold, yielding photoluminescence enhancements of 143.85 and 87.27 for the A and B excitons, respectively. The cavity also redistributes the relative excitonic peak intensities, producing exciton-selective peak ratios up to 2.4 times higher than those of bare MoS2. These results establish hollow plasmonic nanocavities as geometry-tunable platforms for exciton-selective emission and charge-generation control in atomically thin semiconductors.

A ring polymer in a confining space may exhibit at least two phases, namely an expanded (or solvent-rich phase) if its concentration is small, or a collapsed (or polymer-rich phase) when it is concentrated and compressed. These phases are discussed in reference \cite{deG79}, and have been modelled, traditionally, in the mean field using Flory-Huggins theory \cite{Flory42,Huggins42}. In three dimensions the ring polymer may also be knotted, or linked, and have its conformational degrees of freedom constrained by its topology. In a lattice model of confined knotted ring polymers there are indications that the thermodynamic properties of the ring polymer (for example, the osmotic pressure \cite{GJvR18,JvR19}) is a function of its topology. In this paper we explore a lattice knot model of a confined ring polymer as a function of its chemical potential. We show that a well-defined phase transition occurs between solvent-rich and polymer-rich phases when the lattice knot exhibits either the unknot topology or any other fixed knot type. Furthermore, we observe small yet significant variations in the free energy near the critical point when comparing trefoil knots with other non-trivial knot types. These findings indicate that the thermodynamic properties of confined ring polymers depend on their topological entanglement characteristics (namely, their knot type).

We report the thermodynamic and magnetotransport properties of the half-Heusler antimonide ErPdSb, studied on single-crystalline samples in wide ranges of temperature and magnetic fields. The compound was found to order antiferromagnetically at 1.2 K. In the paramagnetic state, it shows semimetallic behavior with a broad hump in the temperature-dependent electrical resistivity around 70 K. The results of ab initio calculations of the electronic structure of ErPdSb indicated a bulk insulating nature. In small magnetic fields the magnetoresistance is driven by a weak antilocalization effect, while in strong fields it is negative and describable by the deGennes-Friedel formalism. The Hall effect data indicated that holes are the dominant charge carriers. At 2 K, the Hall conductivity exhibits a sizable anomalous contribution, which is obscured by multiband effects at higher temperatures. The angular magnetoresistance shows unusual features as functions of magnetic field and temperature, pointing to a possible field-induced reconstruction of the Fermi surface.

Improving the detailed understanding of the underlying properties and functions of biomolecules has recently attracted growing interest, enabled by the possibility of real-space imaging of single, intact macromolecules using Scanning Tunneling Microscopy (STM) in combination with electrospray ion beam deposition and soft landing. This combination provides key insights into biomolecular behavior, but it also imposes stringent requirements on rapid and reliable data analysis. A major limiting factor for applying machine learning to STM images is often the scarcity of training data, caused by the long acquisition times required for both experimental imaging and high-accuracy simulations. Here, we propose a framework for the rapid generation of three-dimensional structures of glycans, peptides, and glycopeptides and their corresponding STM-like image simulations, based on state-of-the-art, machine-learning-accelerated Density Functional Theory (DFT). We generate datasets for the polypeptide bradykinin and for a representative glycan molecule, and we train a conformer estimation model to predict a molecule's three-dimensional structure from an STM image. On synthetic data, our approach achieves high accuracy, with median atomic deviations below $2\,\AA{}$ for peptides and below $4\,\AA{}$ for glycans. Application to experimental data predominantly yields a precise, reliable, and visually convincing determination of the local positions of molecular subunits. The application to experimental data represents an important milestone towards a fully automated structural search pipeline for complex, biologically relevant systems imaged with STM.

High-quality single crystals of the half-Heusler compound DyNiSb were investigated for their low temperature thermodynamic and magnetotransport properties. Magnetic susceptibility, heat capacity, and electrical resistivity measurements revealed two distinct magnetic phase transitions at TN1 = 7.3 K and TN2 = 3.4 K, contrasting with previous reports on polycrystalline samples, which identified only a single transition near TN2 . Moreover, the studied samples were found to exhibit Metal like conductivity, at odds with a semiconducting behavior reported for the polycrystals. Magnetoresistance measurements performed in both transverse and longitudinal configurations revealed in small magnetic fields a weak antilocalization effect that diminishes with increasing temperature, giving way to a positive, monotonic magnetoresistance at high temperatures. Angular-dependent resistivity studies showed a crossover from fourfold to twofold symmetry with increasing magnetic-field strength, suggesting a field-induced reconstruction of the Fermi surface. Our findings highlight a complex magnetic and electrical transport behavior in DyNiSb, highly sensitive to structural disorder and easily tunable by external magnetic field.

We report the first realization of a fractional topological phase in a fully unitary, noninteracting discrete-time quantum walk implemented on finite cyclic graphs. Using a single-coin split-step cyclic quantum walk (SCSS-CQW), we uncover topological phenomena that are inaccessible within conventional cyclic quantum-walk dynamics. The protocol enables controlled engineering of quasienergy spectra, flat bands, and topological phase transitions through the step-dependency parameter and coin-rotation angle. We show that cyclic graphs with even and odd numbers of sites exhibit qualitatively different band structures, while rotational flat bands arise exclusively in $4n$-site cycles; a general analytic condition for their emergence is derived. The SCSS-CQW produces fractional winding numbers $\pm \frac{1}{2}$ (Zak phases $\pm \frac{\pi}{2}$), in sharp contrast with the integer invariants in standard cyclic quantum walks. These fractional invariants lead to an unconventional bulk-boundary correspondence and support protected {zero energy} edge states beyond the usual integer topological classification. In the step-dependent protocol, transitions between distinct fractional winding sectors generate robust edge modes pinned at zero quasienergy. Numerical simulations show that these states remain stable in the presence of both dynamic and static coin disorder as well as phase-preserving perturbations, while survival-probability analysis demonstrates their long-time persistence. Requiring only a constant number of detectors independent of the evolution time and fewer operators, the proposed scheme offers a minimal-resource and experimentally accessible platform for realizing fractional topology, flat bands, and protected zero energy edge states in small-scale synthetic quantum systems.

The recent interest in non-Hermitian (NH) systems has significantly broadened their application across condensed matter physics, offering a unique framework to explore out-of-equilibrium phenomena. Simultaneously, altermagnets have emerged as a distinct magnetic class, characterized by unconventional spin-split bands protected by crystal symmetries. In this work, we investigate the interplay between non-Hermitian dynamics and spin transport in these materials, focusing on the Edelstein effect. We demonstrate that the introduction of non-Hermiticity in $d$-wave altermagnets and $p$-wave unconventional magnets opens novel susceptibility components that are inaccessible in Hermitian counterparts. Our analysis reveals that these susceptibility channels are highly sensitive to the underlying symmetry of the order parameter. Crucially, our results show that the non-conservative nature of the system leads to the selective gain and loss of specific spin components, a phenomenon that can be tuned by the interplay between dissipation and the altermagnetic order. These components exhibit a distinct gain/loss profile that depends strictly on the Néel vector orientation, providing a new route for manipulating spin degrees of freedom through controlled non-conservative processes in emerging magnetic materials

Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries.

We consider a half-filled two-dimensional Su-Schrieffer-Heeger lattice and examine the role of the long-range Coulomb electron-hole attractive interaction. We demonstrate that, under specific conditions, a rare interplay of topological and excitonic-collective behavior emerges as a novel state of matter. A unique Bose-Einstein condensate of excitons forms, exhibiting co-presence of pseudo-spin chiral texture. The emerging complex order-parameter, a particle-hole pairing-gap, has non-zero real and imaginary parts throughout the Brillouin zone (BZ) but vanish separately on two different nodal lines, which intersect at two Weyl points. The Weyl nodes possess opposite pseudo-spin chiralities, which act as source and drain of a Berry-flux associated with the particle-hole pairing-wavefunction, and are the cause of Bogoliubov-deGennes Fermi-arc edge-states. We self-consistently calculate the full momentum-dependence of the particle-hole pairing gap throughout the entire BZ. Near the Weyl points, the pairing gap exhibits the unconventional time-reversal-symmetry breaking $p_x+ip_y$ character. Finally, we discuss general potential experimental realizations of this novel state of matter.

We present a covariant field-theoretical framework for a rank-4 tensor gauge field theory describing fractonic string-like objects. We show that the most general quadratic, parity-preserving action naturally leads to a Maxwell-like sector, with tensorial analogues of electric and magnetic fields, Maxwell-like equations, a conserved energy-momentum tensor, and a Lorentz-like force. Remarkably, the theory gives rise to fracton-like string excitations purely from symmetry principles: constraints on the motion of these extended objects appear as Gauss-like laws, without being imposed by hand. One of these laws is new and corresponds to a generalised dipole conservation for closed strings, restricting their mobility and defining a novel class of fractonic string-like excitations. Finally, we uncover a connection to linearised area-metric gravity: in a suitable limit, the theory reduces to known covariant fracton models with rank-2 gauge fields, highlighting a deep link between fractonic matter and gravity-like structures. This provides a unified perspective on higher-rank gauge fields, extended excitations, and emergent gravitational features.

We study the scalar curvature of the Fisher information metric on the microscopic coupling-parameter manifold of lattice spin models at criticality. For a $d$-dimensional lattice with periodic boundary conditions and $n = L^d$ sites, the Fisher manifold has $m = d \cdot n$ dimensions (one per bond), and we find $|\mathcal{R}(J_c)| \sim n^{d_R}$ with $d_R = (d\nu + 2\eta)/(d\nu + \eta)$, where $\nu$ and $\eta$ are the correlation-length and anomalous-dimension critical exponents. For 2D Ising ($\nu = 1$, $\eta = 1/4$), this predicts $d_R = 10/9$, confirmed by exact transfer-matrix computations ($L = 6$–$9$: $d_R = 1.1115 \pm 0.0002$) and multi-seed MCMC through $L = 24$. For 3D Ising ($\nu = 0.630$, $\eta = 0.0363$), the prediction $d_R = 1.019$ is consistent with MCMC on $L^3$ tori up to $L = 10$ (power-law fit: $d_R = 1.040$). For 2D Potts $q = 3$ (predicted $33/29 \approx 1.138$), FFT-MCMC through $L = 40$ shows $d_\mathrm{eff}$ oscillating non-monotonically around $\sim 1.20$, consistent with $O(1/(\ln L)^2)$ logarithmic corrections. For $q = 4$ (predicted $22/19$), effective exponents oscillate with strong logarithmic corrections. The Ricci decomposition identity $R_3 = -R_1/2$, $R_4 = -R_2/2$ holds to 5–6 digits for all models. This exponent is distinct from Ruppeiner thermodynamic curvature and reflects the collective geometry of the growing Fisher manifold. We provide falsification criteria and predictions for additional universality classes.

Unconventional magnetism has typically been classified into two fundamental classes: even-parity magnets (EPMs) and odd-parity magnets (OPMs). These two classes exhibit identical and opposite spin splittings, respectively, under momentum inversion, while both maintain symmetry-compensated magnetization. In this Letter, we present a unified spin space group-based framework that establishes comprehensive symmetry criteria for both classes. Our framework not only yields a complete classification of EPMs and OPMs but also uncovers a wealth of new symmetry-driven mechanisms for them. Specifically, we classify both classes into three types based on their spin textures: collinear (type-I), coplanar (type-II), and noncoplanar (type-III), and we demonstrate that both classes can be realized across collinear, coplanar, and noncoplanar magnetic orders. We identify eight distinct symmetry-driven mechanisms for OPMs and seven for EPMs, among which some paradigms of unconventional magnetism, for instance, altermagnets naturally emerge as one specific mechanism of EPMs. Using these established criteria, we identify numerous candidate materials from the Magndata database, realizing some new symmetry mechanisms for OPMs and EPMs. Our work establishes a foundational symmetry framework for understanding, predicting, and designing unconventional magnetic materials.

We demonstrate that the finite-temperature critical transverse-field Ising chain exhibits quantitative signatures of black-hole physics in its dual gravitational description within the AdS/CFT correspondence. Its finite-temperature dynamics and thermodynamics are consistently captured by a mixed thermal-AdS/BTZ black hole saddle, leading to three mutually compatible observations. First, antipodal excitation transport collapses onto a universal temperature-dependent curve determined by the relative AdS and BTZ contributions to the gravitational partition function, reflecting horizon absorption. Second, in the high-temperature regime, the retarded response exhibits exponential relaxation governed by the lowest quasi-normal mode of the dual black hole. Third, the temperature derivative of the von Neumann entropy develops a pronounced minimum at a temperature consistent with the Hawking-Page transition. These results identify critical quantum spin chains as minimal and experimentally accessible platforms for probing dynamical and thermodynamic aspects of quantum black holes in controllable many-body systems.

We present a theoretical study of proximitized planar Josephson junctions (JJs) with Rashba spin-orbit coupling (SOC) subject to an in-plane magnetic field and demonstrate that the magneto-current-phase relation (magneto-CPR) provides a powerful and unified probe of their microscopic and topological properties. By analyzing the full phase and Zeeman-field dependence of the supercurrent, we show that magneto-CPR measurements allow one to reconstruct the ground-state phase that minimizes the system's free energy in the absence of phase bias. This reconstructed phase generally displays 0-pi-like transitions as a function of the Zeeman energy, and we demonstrate that the magnitudes of the associated phase jumps provide quantitative information about the Rashba SOC. We further show that the mixed phase-field response encoded in the magneto-CPR enables the extraction of the second mixed spin susceptibility, which serves as a sensitive diagnostic of gap closings and can be used to construct a superconducting topological phase diagram in terms of the relative topological gap. In addition, the magneto-CPR yields the field dependence of the forward and reverse critical currents, allowing one to characterize the Josephson diode effect and its connection to the Zeeman field, Rashba SOC, and junction transparency. Our results establish magneto-CPR measurements as a versatile spectroscopic tool that can be used to extract key system parameters and provide evidence of topological superconducting phases in planar JJs.

We study the critical behavior of the semi-infinite Gross-Neveu-Yukawa model, a quantum field theory describing Dirac fermions interacting with bosonic fields via a Yukawa coupling. We consider Neumann and Dirichlet boundary conditions for the bosonic fields, and the most general boundary conditions for the fermions that preserve unitarity, conformal invariance, and charge conjugation symmetry. We analyze the phase diagram and identify distinct fixed points corresponding to different universality classes of boundary critical behavior. The associated boundary critical exponents, which govern the scaling behavior and crossover phenomena, are computed to one-loop order. We also discuss the relevance of our results to the semi-infinite pseudoscalar Yukawa model.

We study entanglement entropy (EE) in interacting quantum field theories (QFTs) at finite density. We argue that, in the limit of large subregions, the derivative of EE with respect to the size of the entangling region approaches the thermal entropy density, independently of microscopic details. We make this relation explicit using slab-shaped subregions, where the limiting behavior can be directly identified. At finite chemical potential, we show that EE satisfies thermodynamic response relations, including a generalized Maxwell relation linking chemical potential and charge density. We provide strong nonperturbative evidence for these statements in the three-dimensional O(4) model, and conjecture that they are generic features of continuum QFTs, establishing a two-way link between entanglement and thermodynamics that opens a route toward extracting the equation-of-state information from entanglement data.

Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multi-barrier setups allow electron transmission to be controlled with high precision, but have a huge design space of possible barrier geometries. This work presents a framework to optimize electronic transport in such systems using differential evolution algorithms. First, transfer matrix methods are employed to efficiently compute quantum transport through multi-barrier structures, before optimization is applied to find barrier configurations whose transmission profiles closely match a predefined target profile. To address the trade-off between the accuracy and complexity of resulting barrier configurations, regularization techniques are incorporated into the optimization process. Our approach demonstrates the potential for highly tunable electronic transport in graphene-based systems by exploiting evolution-inspired optimization techniques.

Monte Carlo (MC) simulations are powerful computational tools for investigating thermodynamic behavior and validating analytical approaches in complex physical systems. Here we present ETHER (Efficient Tool for THermodynamics Exploration via Relaxations), an open-source MC simulation package, developed for studying temperature-dependent magnetic properties in spin systems. ETHER enables large-scale MC simulations of various spin systems to analyze phase transitions, critical behavior, and complex magnetic structures. The package constructs spin-lattice networks from standard structural input files and supports exchange interaction definitions through user-specified neighbor lists. It also provides tools for easy visualization and post-processing of simulation outputs. Our code has been benchmarked thoroughly against results reported in the literature for common representative magnetic systems. This user-friendly code offers researchers a versatile platform for exploring thermodynamic properties of complex magnetic systems.

Most spherical viruses exhibit icosahedral symmetry, yet the growth of viral shells remains poorly understood due to the short lifetimes and broad size distribution of assembly intermediates. Classical nucleation theory has been widely applied to describe this process, but it treats the boundary of a growing shell as rigid and structureless. Here, we extend classical nucleation theory by incorporating thermal fluctuations of the capsid rim using both discrete and continuum descriptions. Allowing the rim of a partially formed capsid to undergo small geometric undulations, we show that these fluctuations generate an entropic contribution that renormalizes the effective line tension. As a result, rim fluctuations can either promote or hinder capsid closure, depending on the subunit-subunit binding free energy, temperature, and fluctuation amplitude. We find that fluctuations generally lower the nucleation barrier when the binding free energy is below a threshold value, while for sufficiently strong binding, they can instead raise the barrier by stabilizing incomplete capsids through a finite-size entropy penalty associated with rim closure. By moving beyond the idealized capillarity approximation, our results provide a controlled extension of classical nucleation theory that clarifies how boundary fluctuations influence capsid nucleation and growth.

Local material inhomogeneities can strongly influence magnetization dynamics and macroscopic magnetic properties, yet detecting such defects from magnetic imaging data remains challenging when thermal fluctuations and experimental noise obscure static contrast. Here, we investigate defect detection in strongly fluctuating magnetization regimes where signatures of inhomogeneities largely average out in time-resolved measurements. Using finite-temperature micromagnetic simulations with randomly distributed defects and material parameters representative of \ce{Ni80Fe20}, we compute per-pixel temporal mean, temporal standard deviation, and latent entropy and use them as inputs for U-Net-based semantic segmentation models. We find that the most effective descriptor depends on the noise level and, importantly, that robust detection requires training data that reflect the expected noise statistics. These results provide practical guidance for designing noise-robust defect-detection workflows in magnetic imaging.