Papers

We investigate domain-wall physics in a non-Hermitian Su-Schrieffer-Heeger model featuring the non-Hermitian skin effect and its higher-dimensional extensions, focusing on analytical eigenstate solutions under open boundary conditions. By gluing two Su-Schrieffer-Heeger chains with inverted coupling ratios together, we identify two distinct interface geometries, and derive closed-form quantization conditions for the complex wave number and wave functions using symmetry-based ansatzes. Besides conventional skin modes and topological zero modes, the domain walls generate additional localized states at finite energy that detach from the bulk open-boundary-condition continuum. We validate the analytical results by exact diagonalization, and further generalize the interface construction to a two-dimensional Lieb lattice, where competing nonreciprocities produce tunable funneling regimes towards codimension-one and codimension-two interfaces.

Loading multiple quantum states in parallel into a quantum machine learning (QML) model can unlock learning tasks where key information resides in the relations between states rather than in individual states. We introduce an adaptive relational learning framework for such multi-instance quantum data that accesses pairwise and higher-order relations. Our model combines global measurements via SWAP or CYCLE tests for evaluating an $n$-state Bargmann invariant with shallow trainable transformations applied locally to each input state. We demonstrate the approach for continuous-variable (CV) photonic systems, which naturally provide access to quantum data and necessary computing operations. We solve tasks involving hidden relationship detection, geometric phase classification, and sensing in the presence of an unknown shared nuisance interaction. We benchmark the adaptive model against a non-adaptive “measure-first” approach based on continuous-variable classical shadows, and show that the cost of shadow estimation grows rapidly with $n$, while our model avoids this dependence. Already for $n=2$, we achieve perfect test accuracy $A=1.0$ with $500$ inference shots, improving average test accuracy over the shadow-based method by $\Delta A=0.15$ while using $100$ times fewer shots per data point. Our work opens routes to sensing and quantum-data applications where adaptive photonic QML can access relational features that are costly to recover with non-adaptive, measure-first models.

Living systems compute with chemistry by mapping environmental signals onto specific internal chemical states. Despite recent advances in molecular programming, it remains unclear which physicochemical features control the computational expressivity of chemical systems. Here we inverse-design thermodynamically consistent chemical reaction networks whose steady-state response to an environmental input computes a target nonlinear function. Using implicit differentiation we train the free-energy landscape directly: standard chemical potentials, transition-state energies and thermodynamic drives. Increasingly large networks generated by elementary ligation and cleavage steps fit increasingly complex nonmonotonic polynomial functions, with expressivity scaling logarithmically with network size, predicted primarily by the number of reactions. Training individual energetic parameter classes reveals that internal thermodynamic drives, capable of breaking detailed balance, dominate trainability, with comparable performances achieved only by pairs of parameter classes. These results identify nonequilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.

As advanced physical cloning and cyber-extraction techniques proliferate, achieving absolute hardware-level information security has become a paramount global challenge. Conventional architectures systems are fundamentally vulnerable because deterministic memory and cryptographic hardware are physically decoupled, exposing sensitive data to interception. While physical unclonable functions (PUFs) offer robust authentication, integrating them with high-density data writing within a unified nanoscale medium remains elusive. Here, we present an intrinsically secure all-in-one architecture that inextricably fuses high-dimensional data encoding with multiscale physical encryption within single nanodiamonds (NDs). Through precise focused-electron-beam irradiation, we achieve single-nanoparticle defect engineering, enabling deterministic base-36 data writing within individual NDs. Crucially, this customized memory layer is permanently locked within the medium's inherent stochasticity, combining macroscopic spatial PUFs with strictly irreproducible atomic-scale quantum fingerprints extracted via optically detected magnetic resonance (ODMR). By seamlessly embedding deterministic memory into an unclonable physical environment, our approach intrinsically eliminates the vulnerabilities of separated hardware, providing an unconditionally secure solid-state foundation for tamper-proof memory chips, zero-trust networks and next-generation data vaults.

Exceptional points are topological defects of complex band structures that are stable against weak perturbations, yet in two-band systems with bounded non-Hermitian couplings they are ultimately removed by a sufficiently strong Hermitian field. Here we demonstrate a striking exception in a two-dimensional subwavelength atomic array. Along a continuous square-to-triangular deformation at fixed magnetic field, the bulk passes between line-gapped topological regions with \textcolor{black}{band Chern numbers \( (C_1,C_2)=(2,-2) \)} through a gapless exceptional region. Within a finite interval of lattice deformation, increasing the magnetic field merely drives the exceptional points toward the light cone because of the singular radiative dipolar couplings. We further show that skin localization toward open boundaries responds non-monotonically to the same field. An intermediate field drives a bipolar skin localization, with bulk modes from different spectral branches accumulating at opposite boundaries, whereas stronger fields suppress boundary localization. Our results establish the interplay of lattice geometry, light-cone singularity, and Hermitian magnetic field as a route to engineering Chern and exceptional topology beyond the conventional strong-field limit.

By consuming multiple copies of an unknown qubit state, one can modify its purity while preserving the direction of its Bloch vector. We determine the maximum linear rate at which qubit states of different purities can be interconverted, allowing a nonzero error, quantified, for instance, by the trace distance, provided that it vanishes in the limit of infinitely many copies. Interestingly, the optimal conversion rate is determined by the two eigenvalues of the complex right-logarithmic-derivative (RLD) Fisher information matrix associated with $\mathrm{SU}(2)$ rotations of the qubit state. When the output qubits have higher purity, corresponding to concentration, the optimal rate is given by the ratio of the maximum eigenvalues of the input and output RLD matrices. In contrast, when the output qubits have lower purity, corresponding to dilution, the optimal rate is given by the ratio of their minimum eigenvalues. Remarkably, both concentration and dilution can be implemented using SWAP tests as the only nontrivial two-qubit measurement primitive, together with ancillary qubits initially prepared in maximally mixed states, without requiring any additional two-qubit gates. Our work thus provides a novel operational interpretation of the full complex RLD Fisher information matrix. Crucially, its antisymmetric, purely imaginary part encodes geometric information beyond the statistical distance between density operators and plays an essential role in determining the optimal state-conversion rates.

Graphene nanoribbons (GNRs) obtained by on-surface synthesis combine a width-controlled band gap with atomic precision, making chemical decoration of their edges and interiors a practical route to designed electronic and optical behavior. Ullmann coupling of a phenazine-bearing precursor yields a nine-atom-wide armchair ribbon that is periodically perforated and carries two two-coordinated nitrogen atoms at every pore rim. Here we characterize this ribbon across the properties relevant to device operation, alongside the pristine and undoped porous ribbon, disentangling the roles of pore formation and nitrogen incorporation. Perforation primarily governs mechanical and thermal response, whereas nitrogen substitution predominantly controls electronic and optical properties. Perforation removes the atomic row carrying frontier states, opening the gap into the range measured by tunneling spectroscopy while substantially reducing stiffness and thermal conductivity. Substitution leaves the mechanical response essentially unchanged and contributes a secondary conductivity reduction through phonon spectrum reweighting. Its primary effect is electronic: it selectively narrows the conduction band, binds the lowest exciton by 414 meV, and displaces the absorption edge into the red. The effective phonon mean free path we obtain, 9.9 nm, matches the length to which the synthesis presently limits these ribbons, placing reported samples at the ballistic-to-diffusive crossover. Adsorption on Au(111), Ag(111), and Cu(111) is energetically nearly indistinguishable and remains van der Waals in character, accounting for the substrate tolerance observed experimentally. Finally, graphitic rather than pyrazinic nitrogen would make the ribbon metallic, except when two atoms occupy pore-rim sites, yielding a ferromagnetic semiconducting state having a magnetic moment of 2 mu_B per unit cell.

In spatially resolved X-ray diffraction experiments using narrow beams, diffraction patterns from polycrystalline materials often fall between two well-served regimes. Fine-grained, weakly textured microstructures produce smooth Debye-Scherrer rings suited to powder- and tensor-tomography methods, whereas coarse, weakly deformed grains produce isolated spots that can be indexed grain by grain. Many important polycrystalline materials, including plastically deformed metals, martensitic and ferroelastic materials containing complex twin microstructures, and geological aggregates with strong texture or heterogeneous grain size, produce spotty diffraction rings with broadened and overlapping peaks between these limits. Texture tomography addresses this regime by reconstructing spatial orientation distributions from diffraction data. Here, conventional texture tomography lacks the angular resolution needed for sharp distributions, while grain-by-grain indexing can introduce boundary artifacts and underestimate intragranular misorientation. This work introduces basis-adaptive texture tomography. Candidate orientations obtained from peak indexing replace the uniform orientation grid, achieving higher angular resolution. Like conventional texture tomography, it benefits from reconstructing a full orientation distribution function in each voxel, allowing voxels to retain contributions from multiple grains, subgrains or domains rather than being forced into a single orientation. Simulated aluminum polycrystals show improved delineation of grain and sub-grain boundaries accompanied by lower intragranular orientation errors compared with uniform-basis texture tomography and point-by-point scanning 3DXRD. An experimental demonstration on tensile-deformed aluminum shows that bulk grain and subgrain structures with orientation spreads of several degrees can be mapped.

Several alkali chlorides and alkaline-earth oxides adopt the B1 structure at ambient conditions and transform to B2 under compression, allowing to change coordination number and cation-anion distance independently across chemically distinct hosts. The change in anion polarizability under high pressure dominates the optical response of these prototypical ionic solids, yet its dependence on local structure and cation chemistry remains experimentally unconstrained at high pressure. Here we report refractive indices of NaCl, KCl, CaO, and SrO to ~100, ~100, ~120, and ~60 GPa, each in both the B1 and B2 structures, complemented by published MgO-B1 indices to 140 GPa and DFT calculations. Lorentz-Lorenz analysis yields pressure-independent strain polarizability parameters $\Lambda$ ranging from 0.34$\pm$0.05 for KCl-B1 to 1.57$\pm$0.02 for MgO-B2. Our results establish coordination number and cation-anion distance as separable predictors of anion polarizability in both B1 and B2 structures. Increasing anion coordination from six to eight at fixed cation-anion distance lowers the polarizability of both $Cl^{-}$ and $O^{2-}$ by ~0.34-0.40 $\mathring{A}^3$ and ~0.38-0.47 $\mathring{A}^3$ respectively. Our results suggest that the polarizability of $O^{2-}$ is transferable to within ~0.06 $\mathring{A}^3$ between MgO-B1 and CaO-B1 at fixed cation-anion distance, and that this transferability possibly extends to SrO-B1 and the B2 oxides. In contrast, the polarizability of $Cl^{-}$ is not transferable between NaCl-B2 and KCl-B2: the difference between them varies with cation-anion distance, from ~0.2 $\mathring{A}^3$ at 2.65 $\mathring{A}$ to 0 at ~2.5 $\mathring{A}$. The results show that the change of the strain polarizability parameters across the B1-B2 transition is governed by the decreasing and increasing sensitivities of $O^{2-}$ and $Cl^{-}$ polarizabilities to densification, respectively.

Two-dimensional tungsten-based transition metal dichalcogenides exhibit a rich emission spectrum with distinct lines of exciton and biexciton states, neutral and charged, which are typically considered as independent entities. Here, we present a study of the emission kinetics of a WS$_2$ monolayer, demonstrating that these states together form a single interacting ensemble, with their population and depletion mutually coordinated with temperature. Dark trions make a long-lived contribution to the bright trion on a time scale of 200-300 picoseconds, and replenishment from dark states promotes intense luminescence of charged biexcitons. The developed self-consistent rate equation model reproduces the temperature dependence of the kinetics well, opening the way to controlling exciton states in such materials.

We present a comprehensive ab initio study of the effect of higher-order anharmonicity and polymorphism on the lattice thermal conductivity, ${\bm \kappa}$, of single-layer transition metal dichalcogenides. We show that four-phonon processes are responsible for an additional suppression of ${\bm \kappa}$ ranging from 15% to 35% in the ground-state 2H phase. Conversely, their effect is almost negligible on the thermodynamically competitive \1Tp\ phase. The ${\bm \kappa}$ difference between the 2H higher-conductivity phase and the \1Tp\ lower-conductivity phase can be exploited for the design of thermal switches that may find applications in thermal management and in the development of phonon-based logic elements, particularly in the case of tellurides, where field-controlled ultrafast and reversible phase transitions have already been demonstrated experimentally.

When a classical Brownian particle is subjected to a temperature gradient it can undergo thermophoresis, i.e., the particle gets transported towards the colder regions of the environment, which bears relevant consequences to nonequilibrium self-organization. Here, we investigate how the quantum Brownian motion is affected by a temperature gradient. We employ a recently proposed generalized system-plus-reservoir model, where one assumes a continuum field of thermal baths. As our main result, we obtain the quantum influence functional, within the Feynmann-Vernon path integral approach. To test convergence, we derive an effective Langevin equation for the center of mass of the quantum wavepacket, and show that it coincides with the classical Langevin equation at high temperatures. Both the fully quantum (low temperatures) and the semiclassical (high temperature) limits show signatures of quantum thermophoresis in the quantum Brownian motion, a so far unexplored effect.

We present a novel multifunctional sample-holder concept for compact research-scale vacuum systems. The sample holder is mounted directly on the transfer rod and remains mechanically connected to it throughout loading, transfer, deposition, and ion-beam milling. In contrast to conventional layouts, no handover to a separate substrate stage is required. By combining axial transfer motion, rotation around the transfer-rod axis, and independent substrate rotation, the holder itself defines the relevant process geometries and enables standard deposition, oblique deposition at freely selectable incidence angles, substrate rotation, and co-sputtering from adjacent sources. A shielding plate integrated into the sample holder acts as both a substrate shutter and a source shield. This reduces the need for separate shutter mechanisms and allows a compact radial arrangement of four 2-inch magnetron sputter sources without source-tilting mechanisms. The concept was implemented in a modular vacuum system assembled from standard DN100CF components, combining a load lock, magnetron sputtering, and ion-beam milling. The system reaches a base pressure of $5 \cdot 10^{-8}\,\mathrm{mbar}$ without bakeout. The functionality of the sample-holder concept is validated by oblique deposition of Ta/CoFeB bilayers, which exhibit pronounced uniaxial in-plane magnetic anisotropy, and by co-sputtering of an Au–Ag composition series, whose resistivity follows the expected Nordheim-type composition dependence.

Inferring directed effective interactions from neuronal spike trains is a central inverse problem in statistical physics and computational neuroscience. Kinetic Ising models provide a tractable framework for this task, but their application to neural data typically requires binning spike trains into binary activity variables, discarding within-bin timing and conflating collective network dynamics with single-neuron history effects. We introduce SpiKIsing, a latent-variable model that separates these two levels of description. A discrete-time asymmetric kinetic Ising model describes collective network activity, while continuous-time, history-dependent point processes generate the observed spikes conditional on the latent states, accounting explicitly for refractoriness and post-spike recovery. We derive a variational mean-field expectation-maximization scheme in which the point-process likelihood enters as an effective observation field, enabling joint inference of latent activity, network couplings, and emission parameters. The framework extends naturally to maximum-a-posteriori inference with structured priors, including sparsity and a hierarchical extension favouring Dale-consistent outgoing interactions. We validate parameter recovery on matched synthetic data and test the method on spike trains generated by a recurrent conductance-based leaky integrate-and-fire network. In this setting, SpiKIsing recovers sparse connection structure and correctly classifies all excitatory and inhibitory neurons despite the substantial mismatch between the data-generating dynamics and the SpiKIsing model.

We investigate the quantum Mpemba effect in a graphene nanotorus qubit and show that its strength depends strongly on how the initial state is prepared. We compare two protocols that share the same final Hamiltonian, thermal bath, and Markovian Liouvillian: bare Gibbs preparation, in which the coherent drive is switched on only at the final quench, and driven steady-state preparation, in which the initial states are stationary states of the driven dynamics. Using the distance to the final stationary state and an integrated Mpemba parameter, we find broad regions of strong anomalous relaxation for bare Gibbs preparation, with $M_B$ approaching unity, whereas the driven steady-state protocol almost completely suppresses the effect. A Liouvillian-mode analysis reveals the mechanism: the bare Gibbs protocol typically yields a smaller hot-state weight in the slow active sector than the cold state, $R_s<1$, while the driven steady-state protocol predominantly gives $R_s>1$. Since the final Liouvillian spectrum is identical for both protocols at corresponding parameter points, the contrast originates from preparation-dependent modal weights rather than from changes in the decay-rate hierarchy. These results identify state preparation as a control parameter for anomalous quantum relaxation in a curved graphene qubit.

Using the two-dimensional Ashkin-Teller (AT) model, we compare criticality on three non-periodic lattices: the Smith-hat aperiodic tiling, Voronoi-Delaunay (VD) random triangulations, and uncorrelated diluted square lattices. The decay of the block-averaged coordination fluctuation $\sigma_Q$ with exponent $\alpha$ is used to describe the connectivity disorder. The first two lattices share the same exponent $\alpha$, which differs from that of the third. We consider the regime where the correlation-length exponent $\nu<1$, where randomness is relevant according to the Harris criterion $d \nu \le 2$, but should be irrelevant in cases of the Smith-hat tiling and VD triangulations, where $\alpha \nu >1$, according to the Harris–Barghathi–Vojta (HBV) criterion. For the diluted lattice, we indeed find that the clean universality behavior breaks down along the entire critical line, indicating a crossover to a fixed line dominated by disorder, in line with both the Harris criterion and the HBV criterion. In contrast, both VD and Smith-hat lattices display critical exponents consistent with the clean AT universality class, as validated by a Coulomb-gas self-consistency check, violating the Harris criterion while conforming to the HBV criterion.

Thermal optimization of 2D CFET inverters requires testing structural proposals against their electrical costs. We examine these research tasks using an AI agent workflow within a supplied electrothermal model. At 12 nm, Astra selects a redistributed source-interconnect geometry, while a coordinating agent proposes a substrate-directed heat-removal path. The combined design reduces peak temperature rise by 1.67 K at fixed metal volume and 20 {\mu}W. A subsequent metal-resistance sensitivity gives about 0.6-K inverter cooling alongside a 2% nFET on-current loss. Effective contact-length scaling further shows that lower temperature can accompany higher thermal resistance when current falls. Reproduction identifies agreeing implementations and retains a 104.95-K failure for diagnosis. These results show that an AI scientist workflow can propose thermal structures, test them under common constraints, and quantify their electrical cost.

Stable ternary compounds lie below every competing phase on a conventional convex hull, but this does not reveal whether their stability is inherited from already favorable binaries or created by bringing three elements into one crystal. We define the emergent ternary stabilization energy, delta E3, as the energy by which a stable ternary lies below the complete unary-binary hull at the same composition. Applied to 19,209 Materials Project compounds calculated within the GGA framework, delta E3 has a median of 63.3 meV/atom and spans from nearly binary-degenerate phases to compounds stabilized by several hundred meV/atom. The distribution separates recognizable chemical limits: intermetallics have the smallest median stabilization, single-anion compounds occupy a broad intermediate regime, and polyanion compounds receive the largest gains. Within single anion families, large delta E3 is associated with cation electronegativity contrast, bandgap opening, and reconstruction of coordination, bond geometry, and local neighbour chemistry relative to the actual binary decomposition products. Experimentally linked compounds obtain a median 4.92% of their formation energy from ternary stabilization, compared with 3.53% for theoretical compounds, with the clearest separation in sulphides and selenides. delta E3 therefore provides a compact measure of the thermodynamic value of compositional complexity: low values identify phases whose stability is largely reproducible by binaries, while high values identify compounds that gain substantially from ternary specific electronic and structural organization.

This course on two-dimensional quantum gases, prepared for the São Paulo ICTP-SAIFR IVth School on Light and Cold Atoms, is organized into three lectures. The first introductory lecture addresses Bose-Einstein condensation in an ideal gas, the effect of interactions in the weakly interacting limit, Gross-Pitaevskii equation and its hydrodynamic formulation, as well as superfluidity. The second lecture is devoted to the main features of two-dimensional quantum gases: importance of the interactions, enhanced phase fluctuations and the emergence of a quasi long-range order, superfluidity through the Berezinskii-Kosterlitz-Thouless mechanism, scaling symmetry. The third lecture has taken the form of a seminar on fast rotating quasi two-dimensional Bose gases and thermal melting of the vortex lattice. The slides accompanying these notes can be found on the school website: https://www.ictp-saifr.org/slca2025/.

We address three-dimensional (3D) solitons maintained by spin-orbit coupling (SOC) in the binary self-interacting Bose-Einstein condensate (BEC) held in the 3D optical lattice (OL). The analysis reveals that the SOC-OL interplay results in the formation of stable full-vortex (FV) soli-tons, built as sets of four density peaks residing in neighboring wells of the lattice potential, with the superimposed global vortical phase, and site-centered semi-vortices (SV), in which the vorticity is present in only one component. The full-vortex solitons, with their specific phase textures, do not exist in a uniform BEC with SOC. Full-vortex and semi-vortex states in the binary self-attractive BEC are stable despite the possibility of the supercritical collapse in the 3D system. Such states also exist, as gap solitons, in the self-repulsive 3D system. In terms of the chemical potential and number of particles, the stability regions of the 3D full-vortex solitons and semi-vortices expand with the increase of the SOC strength and OL depth. The results open the route to the creation of 3D complexes of vorticity-carrying condensates that can be realized with existing experimental techniques.

CeRh$_2$As$_2$ has been receiving considerable attention due to its unusual two-phase superconductivity. The superconducting (SC) phase appears at $T_{\mathrm{c}}$ = 0.35 K in an ordered state (phase I) of the Ce-4$f$ moments, which develops below $T_{\mathrm{0}}$ = 0.55 K. The microscopic nature of phase I has not been fully established yet. We report a single-crystal study of the effect of La substitution on these low-temperature phases in Ce$_{1-\textit{x}}$La$_\textit{x}$Rh$_2$As$_2$ up to $x$ = 0.1. The lattice parameters increase monotonically with $x$, corresponding to an effective negative pressure of approximately -0.3 GPa for $x$ = 0.1. While the Ce$^{3+}$ local valence state and the non-Fermi-liquid behavior are preserved, the resistivity coherence maximum $T^{*}_{\mathrm{max}}$ $\approx$ 45 K in the pristine sample shifts to lower temperatures with increasing $x$, indicating a suppression of the Kondo energy scale upon lattice expansion. On the other hand, both $T_{\mathrm{c}}$ and $T_{\mathrm{0}}$ are rapidly suppressed to below 0.1 K for $x$ $>$ 0.05. Notably, such a rapid decrease of $T_{\mathrm{c}}$ under moderate negative pressure is unexpected, but is rather consistent with substitution-induced disorder effect which leads to strong pair breaking in unconventional superconductors. The observed fragility of phase I under both pressure and disorder may imply its itinerant origin.

Recent studies of the superconducting proximity effect in quasicrystalline and topological systems have opened up a new research direction for exploring how quasiperiodicity and topology influence proximity induced superconductivity. In this work, we investigate spatial variation of the proximity induced pairing amplitude in a hybrid ring composed of a spin singlet superconductor and a normal region described by three different lattice models using the self-consistent Bogoliubov-de Gennes formalism. We first consider the normal region described by the diagonal Aubry-André-Harper (AAH) model. Increasing the quasiperiodic potential enhances spatial fluctuations in the induced order parameter, while progressively suppressing its magnitude in the normal region. Beyond the localization transition, proximity-induced pairing is strongly diminished due to the localized nature of the underlying electronic states. The normal region is then modeled by the Su-Schrieffer-Heeger (SSH) chain to investigate the effect of hopping dimerization. Weak dimerization introduces oscillatory modulations in the induced pairing that extend deep into the normal region, whereas strong dimerization confines these oscillations and significantly reduces the penetration of superconducting correlations. Finally, we study the combined SSH-AAH model to explore the interplay between the dimerized hopping and quasiperiodicity. The results show that the SSH dimerization determines the oscillatory behavior and penetration of the induced pairing, while the AAH potential enhances spatial inhomogeneity and further reduces its magnitude. Together, these two effects provide a versatile means of controlling proximity-induced superconductivity in quasiperiodic hybrid systems.

Muon-spin rotation/relaxation is one of the most powerful microscopic probes for understanding magnetic order, spin dynamics, superconductivity, and complex magnetic phases in emerging quantum materials. Quantitative interpretation depends critically on the accurate identification of the muon stopping site, a problem traditionally addressed using density functional theory-based structural relaxation. However, conventional DFT approaches are computationally demanding, time-consuming, and require extensive calculations. Here, we develop MEOWN (Muon Engine for Optimized Weighted Networks), a machine-learning-based, physics-informed computational framework that combines a Polarizable Unperturbed Electrostatic Potential (P-UEP) model with machine-learning-guided optimization and symmetry-driven relaxation to predict energetically favorable muon stopping sites in crystalline solids. MEOWN explicitly incorporates electrostatic interactions, electronic screening, polarization effects, and zero-point motion into the learning workflow, providing physically interpretable predictions with substantially reduced computational cost while maintaining physical consistency and scientific rigor. To validate the framework, we investigated several well-established benchmark materials, including MnSi, CoF2, CaF2, LiF, and NaF. The predicted muon stopping sites and dipolar fields are in close agreement with previously reported DFT+mu results, demonstrating the reliability and transferability of the approach across chemically diverse systems. Notably, MEOWN predicts the equilibrium muon stopping site within a few minutes, offering a significant speedup over conventional DFT-based calculations. This manuscript presents the theoretical foundations, computational methodology, and validation of MEOWN as a general-purpose software for rapid and reliable muon stopping-site prediction in crystalline materials.

The mechanical response of a moving probe in an active bath reflects both the dynamics of the bath and how the probe couples to it. For a probe moving at velocity $\mathbf V$ and interacting weakly with an ideal active bath, we show that the trajectory statistics of a free bath particle determine a density response from which the bath force on the probe follows. The probe-particle interaction determines how this response is weighted over wave vector $\mathbf q$, while the probe motion selects frequencies $\omega_{\mathbf q}=\mathbf q\cdot \mathbf V$. This trajectory-response relation separates the bath dynamics from the wave-vector weighting, so neither the stationary state nor force correlations need to be recalculated for each probe interaction and velocity. Applying this relation to active-particle models, we prove that positive power transfer is excluded for active Ornstein–Uhlenbeck particles in all dimensions and for complete-reset run-and-tumble particles in $d\geq2$, while one-dimensional run-and-tumble particles and two-dimensional active Brownian particles possess modes that can transfer positive mechanical power. For a given probe, this response predicts drag reversal, spontaneous probe motion, and motion-induced density distortions, all in quantitative agreement with simulations. Varying the probe geometry changes how the same bath response is weighted, thereby selecting different moving states. Beyond the leading weak-probe, ideal-bath limit, higher orders in probe strength involve multi-interval trajectory statistics, while finite-density corrections involve interacting-particle dynamics. Our results connect the trajectory statistics of active bath particles to the mechanical response of a passive probe, providing a route to predict probe response from single-particle trajectory statistics measured in the absence of the probe.

Companion to arXiv:2609.10614, which built a physics judge that classifies the gap symmetry of a superconductor candidate from its structure and showed that generation in the wire families re-finds the known canon. Here the judge is applied end to end to the single compound that survived its own screen. From 58 iron-pnictide compositions in the electronic window of BaFe$_2$As$_2$ absent from the Materials Project, a zero-cost literature-and-stability screen leaves one with no bulk synthesis and no band-structure, magnetic or pairing calculation: NaFeP (111-type, P4/nmm, Fe$^{2+}$ d$^6$, arsenic-free). The judge, refined with an explicit Stoner gate and a frame-independent symmetry reader, returns a paramagnetic DFT ground state and an s$\pm$ gap: a clean sign reversal between the hole cylinders at Gamma and the electron sheets at the zone corner, at nesting vector (1/2,1/2,1/2), in every basis and cell tried. Whether the gap is nodeless is where the instrument is tested. The phosphorus height (1.16 Å) lies below the empirical threshold of about 1.33 Åseparating nodal from nodeless pnictides, so the structural rule calls NaFeP nodal. The sealed verdict on a metal-only basis said nodeless; a d+p basis that passes the same gates puts the inner hole cylinder at the node threshold (min|g|/max|g| = 0.07-0.10), and a scan of the phosphorus height reproduces, inside one compound, the ingredients of the height rule: a third hole pocket, a switch of the nesting vector to (1/2,1/2,0), a monotonic approach to the Stoner instability, and near-degeneracy of s$\pm$ and d channels beyond 1.33 Å. The prediction we stand behind is the d+p reading: an s$\pm$ superconductor with a near-nodal inner hole sheet, on the LiFeP side of the rule, with the disagreement between bases reported, not resolved by choice. No Tc is asserted; the discriminating measurement, nodal or nodeless, is stated in advance.

Odd slip at solid-liquid interfaces, characterized by a velocity component perpendicular to the applied shear stress, introduces a novel class of odd transport phenomena with transverse momentum transfer at boundaries, distinct from odd viscosity, odd elasticity, or odd diffusivity. This effect arises on chiral active surfaces with persistent energy input and defined handedness, such as carpets of rotating biological or synthetic cilia with flexible anchoring. Although odd slip itself is dissipationless, its maintenance comes at an energetic cost, which we estimate from the microscopic model. With respect to its orientation dependence, odd slip can be classified as pseudo-scalar or pseudo-vectorial. We use a perturbative expansion to determine the resistance tensors of problems with different levels of broken symmetries. They reveal how the surface chirality and body asymmetry together violate the Onsager symmetry, but uphold the Onsager-Casimir reciprocity. Strikingly, suspensions of particles with odd slip embedded in a conventional Newtonian bulk fluid exhibit odd viscosity, providing a new minimal mechanism for its emergence beyond current microscopic models.

Low-dimensional Dirac systems offer a versatile platform for quantum-geometric non-linear transport. Hexagonal warping in topological insulator (TI) surface states generates rich momentum-space geometric textures. However, mirror and time-reversal symmetries strictly constrain both the Berry curvature and quantum metric dipoles to zero. Here, we show that proximity-coupling a hexagonally warped TI to a d-wave altermagnet lifts spatial mirror symmetry via the interplay of C_{3v} warping and d-wave spin-splitting. This symmetry breaking enables finite Berry curvature dipole components, which, however, remain heavily suppressed near charge neutrality, establishing the intrinsic quantum metric dipole as the sole driver of the non-linear Hall response. This scattering-independent response vanishes at \mu = 0 and exhibits odd parity under chemical potential inversion. Importantly, rotating the altermagnetic orientation angle \phi continuously tunes quantum metric dipole and induces a sign reversal, peaking near \phi=0 and vanishing at \phi = \pi/4. Our findings highlight TI-altermagnet interfaces as ideal candidates for gate- and orientation-tunable quantum metric spintronics.

The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that microstructure evolves along an arbitrary thermal trajectory, however, remains a challenging problem. Conventional continuous-cooling-transformation diagrams, for instance, provide only a static description, and they do not exist for all materials, especially emerging and novel ones. Deep-learning approaches address this issue, but they treat image-based characterization, temporal prediction, and microstructure forecasting as separate tasks. Here, we introduce a probabilistic deep-learning framework that unifies these tasks for in situ high-temperature confocal laser scanning microscopy data. The initial surface image is compressed with a beta-variational autoencoder into a low-dimensional latent representation, which, together with the cooling rate and the full temperature history, is passed to a temporal fusion transformer that produces a quantile forecast of the Widmanstatten ferrite ratio. In parallel, a Gaussian process predicts the end-state Widmanstatten ferrite ratio and corrects the forecast equilibrium level, yielding probabilistic prediction intervals. We demonstrate the efficacy of our method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes and showcase significant accuracy. This verifies the relevance of our framework and paves the way for a streamlined and highly accelerated investigation of material evolution under thermal treatment.

Nonlocal magic quantifies the minimum nonstabilizerness attainable under independent local unitary transformations on the two subsystems. Here, we use min-relative nonlocal magic (NLM) to characterize the crossover from ergodicity to many-body localization (MBL) in the random-field XXZ chain. Unlike entanglement entropy, NLM probes how entanglement is organized through the distance of the Schmidt spectrum from dyadic-flat stabilizer spectra. From weak to intermediate disorder, NLM evolves from an $O(1)$ Haar-like value into a size-enhanced dome, while entanglement remains volume-law, revealing a spectral reorganization not visible in the entropy. Deep in the MBL regime, a two-level cut-hybridization model captures the nearly binary Schmidt spectrum and explains why the mean and median NLM decay approximately as $W^{-1}$ and $W^{-2}$, respectively. Following a product-state quench, NLM overshoots and relaxes in the ergodic regime, whereas at strong disorder it grows slowly and approximately logarithmically. These results show that NLM resolves Schmidt-spectrum structure not visible in the entanglement entropy and provides a complementary probe of ergodicity breaking.

The interaction between magnetic and elastic degrees of freedom is increasingly central to advanced spintronics technology. In magnetostrictive materials, a moving domain wall carries a localized elastic deformation. Here, we investigate these elastic deformations and their back-action on field-driven domain wall dynamics using a fully coupled micromagnetic-elastodynamic framework and an analytical collective coordinates model. We show that mechanical damping acts as a non-magnetic dissipation channel, extracting energy from the magnetic texture and sustaining translational domain wall motion even in the complete absence of intrinsic Gilbert damping ($\alpha=0$). Specifically, elastic damping impacts domain wall dynamics in a manner equivalent to magnetic losses, allowing us to map it onto an effective magnetoelastic contribution to the Gilbert damping parameter, $\alpha_\text{me}$. We calculate the localized stress tensor profiles co-moving with the domain wall and show that mechanical dissipation breaks their spatial symmetry. Using our collective coordinates model, we demonstrate that these asymmetric stress profiles-specifically the localized stress gradients across the wall core-exert direct propelling forces that balance field-induced magnetic precession, enabling steady-state motion. Overall, these findings reveal a fundamental energy transfer and relaxation mechanism between magnetic and elastic subsystems, providing valuable insights for controlling magnetic texture dynamics via strain engineering and mechanical damping design in acoustic spintronic devices.