Papers

We report simulations of the dissolution and diffusion of Nb's surface oxide layer in vacuum. While this chemical doping process is important for the surface preparation of Nb superconducting radio frequency (SRF) cavities - common components of particle accelerators - quantitatively linking the resulting oxygen distributions to superconducting performance remains challenging. In this work, we simulate the reaction-diffusion process numerically for treatment temperatures $T = 50^{\circ}$C to $200^{\circ}$C and times $t = 0.5$ h to $120$ h, and calculate the effect of the spatially inhomogeneous oxygen doping on Nb's superconducting properties. We find that oxygen doping redistributes the Meissner screening current, reducing its value at the surface and shifting its maximum several nanometres into the material. These results provide a microscopic link between oxygen diffusion profiles and the electromagnetic response of Nb relevant for SRF cavity operation. This work provides a quantitative framework linking oxygen diffusion profiles to superconducting performance and establishes a foundation for future studies involving time-dependent and multi-step heat treatment protocols.

The spin of a quasiparticle trapped in a quantum dot Josephson junction forms the basis of an Andreev spin qubit (ASQ): a semiconductor-superconductor device where the interplay between a localized spin degree of freedom and superconductivity leads to a spin-resolved Josephson potential. In this work, we show that shunting an ASQ with a linear inductor enhances its relaxation time by separating the spin-qubit states into distinct potential wells in phase space, nearly eliminating wavefunction overlap. The resulting inductively protected Andreev (IPA) spin qubit is equivalent to two fluxoniums in the heavy regime, one for each spin. Thus, the IPA qubit combines the long coherence times, low-frequency ground-state manifold, and large anharmonicity of a protected superconducting qubit with the operational advantages of a spin degree of freedom.

Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate atomistic simulation. Classical density functional theory offers a reusable variational description, but its central excess free-energy functional is generally unknown, and learned approximations have largely remained restricted to planar or lower-dimensional settings. Here we show that this functional can be learned directly from fully three-dimensional equilibrium density fields while preserving spatial symmetry and variational consistency, without free-energy or chemical-potential labels. A single learned functional transfers across temperatures, system sizes and statistical ensembles, and recovers structure factors, the equation of state, liquid–vapor coexistence and interfacial broadening, none of which are used as training targets. Applied to complex three-dimensional geometries, it predicts the non-monotonic force associated with formation and rupture of a solvent-depleted bridge between colloids and adsorption in an interconnected gyroid pore. These results demonstrate that equilibrium density data can be converted into a transferable thermodynamic generator connecting microscopic liquid structure to response, phase behavior and collective phenomena.

Complex multipath environments are usually avoided in wave-based information processing because repeated scattering creates many interfering propagation paths, obscuring controllability and generating extreme sensitivity to perturbations. The addition of nonlinear mechanisms fundamentally alters the wave-control landscape by breaking the superposition principle that underpins most wave-management strategies. Here, we show that these two apparent impediments – multipath complexity and nonlinearity – can instead be harnessed as key resources for physical optimization. We demonstrate an in-situ adjoint optimization protocol in a wave-chaotic platform incorporating a single localized nonlinear defect, in which the system itself performs both the forward and the adjoint propagations required for gradient evaluation. Recurrent multipath returns repeatedly expose the wave to the defect, producing from a minimal hardware a rich nonlinear input-output map with many pathway-mediated degrees of freedom. At the same time, a suitable adjoint excitation enables direct extraction of the sensitivities from measurements alone, without a digital twin or conventional numerical backpropagation. We experimentally validate the protocol on a minimal nonlinear multipath platform composed of incommensurate coaxial cables connected via T-junctions, one of which hosts a diode-loaded cavity. Our approach opens a route to adaptive wireless communications, imaging and analog intelligence in complex, partially unknown environments where conventional modeling is impractical.

We describe a mechanism of affine symmetry emergence in the maximal density regime of the prototypical model of self-organized criticality when the inverse square of the mesh of the underlying lattice is much larger than the number of random perturbation points distributed according to a prescribed probability measure supported in the interior of the ambient convex domain. Moreover, an appropriate scaling limit of the toppling function (aka odometer), which counts the number of operations per site, is a solution to a non-linear partial differential equation well known in the context of optimal transport and differential geometry, making it possible to accurately estimate the deviation of the density from its maximal value in any macroscopic window. The mechanism for the affine symmetry emergence is due to the novel empirical fact, supported in addition by inductive arguments that have recently being upgraded to a rigorous proof, that the scaling limit of the toppling function is the unique concave solution of the Monge-Ampère equation with Dirichlet boundary condition on the convex domain with the potential given by the probability measure used above as the infinite-perturbation profile.

Thin-film Mn$_5$Si$_3$ is a prominent altermagnetic candidate owing to its metallicity, demonstrated anisotropic anomalous transport, and proposed $d$-wave exchange splitting that could enable spin-polarized transport. Its postulated altermagnetic structure has zero propagation vector, unlike the bulk collinear antiferromagnetic AFM2 phase, which orders at the $M$ star. We analyze both phases using Landau theory, first-principles paramagnetic-instability calculations, and Monte Carlo simulations. AFM2 appears in Landau theory as a symmetry-protected inversion-even, permutation-odd mode at a single arm of the $M$ star. At $\Gamma$, the same intracell pattern belongs to the collinear sector of an $E_{2g}$ order parameter; both phases require higher-order terms for their selection. Disordered-local-moment calculations identify the leading exchange instability at $M$, and the resulting classical Heisenberg model orders at the bulk experimental temperature scale into the orthogonal $3M$ phase favored by single-site entropy. Magnetostructural coupling to $E_{2g}$ deformations favors single-$M$ AFM2 ordering. The estimated homogeneous-strain contribution to the relevant quartic coefficient is about a quarter of the competing entropic term near the bulk transition, while internal $E_{2g}$ relaxations are expected to provide additional stabilization. The $\Gamma$-point $E_{2g}$ mode, whose Landau theory contains the altermagnetic sector, is substantially weaker and further suppressed by epitaxial strain comparable to that reported for Mn$_5$Si$_3$/Si(111) films exhibiting anomalous transport. The same strain reduces the leading magnetic exchange scale. These results explain the bulk $M$-point instability but strongly disfavor relocating the propagation vector from $M$ to $\Gamma$ in a moderately strained bulklike Mn$_5$Si$_3$ film, leaving the identity of the Hall-active phase unresolved.

Combined with numerical analytic continuation techniques, quantum Monte Carlo (QMC) methods enable the extraction of real-frequency dynamical properties from imaginary-time correlation functions. However, the efficient computation of imaginary-time correlation functions by QMC simulations can (depending on the particular model used) be challenging, particularly for operators that are off-diagonal in the computational basis. In this work, we present an efficient and general algorithm within the stochastic series expansion (SSE) framework for evaluating imaginary-time correlation functions of both diagonal and off-diagonal operators. The algorithm builds on a discrete imaginary-time slicing of the SSE operator string, which provides correlation functions on a grid of well-defined imaginary-time points with no discretization error. For off-diagonal operators, we derive estimators that integrate directly into the existing SSE directed-loop or cluster updating schemes, introducing only minimal computational overhead. We benchmark the method on the one-dimensional transverse-field Ising model (sampling with cluster updates) and XXZ spin chain (using directed-loop sampling), demonstrating excellent agreement (with only statistical errors) with exact diagonalization of small systems. We also study larger systems to demonstrate efficiency.

Dyson universality typically manifests itself in local eigenvalue statistics. Here we show that its signature survives a nonlinear inverse-spectral reconstruction and reappears in the matrix geometry of the reconstructed operator. Using a dressing transformation, we map each unfolded spectrum to a deformation $f(x)$ of a fixed harmonic oscillator and represent it in the common oscillator basis by $F_{mn}=\bra m|f|n\ket$. We resolve the matrix-element weight into shells of fixed distance $d=|m-n|$, corresponding to the energy-transfer channels of the reference oscillator, and characterize the resulting distribution by distance-shell moments. Independently calibrated on Gaussian $\beta$-ensembles, these moments vary smoothly with $\beta$ and distinguish the GOE, GUE, and GSE. With this calibration fixed, applying the same diagnostic to the nontrivial zeros of the Riemann zeta function places the reconstructed operators in the GUE sector. Thus, the GUE character of the zeros is recovered not through direct statistics of the input levels, but from the distance-resolved geometry of the reconstructed operator.

We identify a general mechanism for the finite-time thermalization of macroscopic observables, such as coarse-grained charge densities, in terms of elementary forms of the quantum dynamics of initial states: (1) aperiodicity, which provides a computable measure of (2) a dynamical partially ergodic exploration of the Hilbert space. Specifically, this mechanism predicts the equilibration of all (concentrated) macroscopic observables, in almost all states in an initial ensemble and almost all times within finite and longer intervals, given only the observable-independent information that the return probability of the ensemble of initial states is small over a finite time range. As a special case, it also accesses standard results on equilibration over infinitely long times in terms of (stronger versions of) the effective dimension of initial state delocalization in the energy eigenbasis. Our results incorporate macroscopic thermalization into the domain of operational quantum statistical mechanics, recently developed to provide finitely computable criteria for microscopic thermalization. We discuss an overall characterization of this approach as establishing connections between (1) the decay of a (theoretically or experimentally) computable probe indicating memorylessness, (2) a fundamental invariant mechanism in terms of the alignment of observables or states in the Hilbert space, and (3) predicting different natural forms of (classical and) quantum thermalization, most of which rigorously recover conventional eigenstate-based descriptions of infinite-time thermalization as a special case but provide stronger accessible predictions over finite observation times in the thermodynamic limit.

Transition metal borides are attracting increasing interest due to their unique properties. They are not only characterised by very high hardness, but also considerable chemical and thermal stability. Silver, on the other hand, is a good material for increasing electrical and thermal conductivity, wear resistance and has antibacterial properties due to its biological characteristics. Combining these two materials can provide superhard bilayers with increased functional properties. In this study, it was decided to synthesise Ag/WB2.5, Ag/W0.76Ti0.24B2.5 coatings and compare their properties to the individual components. The silver coating was produced by pulsed laser deposition (PLD), while the WB2.5 and W0.76Ti0.24B2.5 coatings were formed by high-power pulsed magnetron sputtering (HiPIMS). To determine the mechanical properties, nanoindentation tests, adhesion of the coatings by scratch -test and wear resistance by abrasion in reciprocating motion were tested. In all cases, the silver film contributed to an increase in the wear resistance of the materials without major changes in the hardness results of the materials. In addition, the Ag/W0.76Ti0.24B2.5 film showed very good adhesion to the substrate. Human hand wiping simulator was also carried out using - Tribotouch. After 36 000 cycles Ag/W0.76Ti0.24B2.5 coating was slightly deformed, which was not visible macroscopically. This result is more than three times greater than for the pure silver film. It was also decided to carry out corrosion tests in an environment of 0.9% NaCl. The Ag/W0.76Ti0.24B2.5 bilayer has very good corrosion resistance, similar to pure silver.

It has been known for a long time that the equation of motion for dissipative linear dynamical systems with constant coefficients cannot be derived from the classical principle of stationary action because the term proportional to velocity in the equation of motion leads to the time derivative of order one half in the Lagrangian. Thus, approaches utilising fractional calculus have been used; however, they suffer from deficiencies both from mathematical and physical points of view. We here present our version of such fractional calculus based approach that provides correct Euler-Lagrange and, ultimately, the Hamilton equations, energy change of the moving body, and an attempt for a geometric interpretation of how energy dissipates.

Mesoscale self-assembly provides a route toward the design of programmable microsystems. Here, we construct flexible chains of floating monomers whose curved branches impose upward or downward deformations of the liquid interface, corresponding to effective positive or negative capillary charges. These geometrically encoded deformations generate local attractive or repulsive interactions along the chain. By tuning the capillary sequence, we obtain distinct folded configurations, including straight lines, zigzag patterns, and loops. For short chains, folding is largely governed by nearest-neighbor interactions and leads to well-defined structures. As the chain length increases and non-neighboring segments come into proximity and interact, however, the folding landscape becomes increasingly complex, with multiple metastable states whose number grows exponentially with chain length. We map these landscapes numerically and demonstrate experimentally that mechanical agitation allows the chains to transition between metastable configurations. Beyond encoding a target geometry, the capillary sequence therefore controls the complexity of the folding landscape as well as the degeneracy and mutational robustness of folded structures. These results establish capillary chains as a controllable mesoscale platform for investigating how local interaction rules give rise to collective folding and complex sequence-to-structure relationships reminiscent of those encountered in biomolecular systems.

Thermal transport in dielectric, non-magnetic crystals is mediated by quantized lattice vibrations, which drift and interact when driven out of equilibrium by a temperature gradient. This phenomenon can be described at multiple theoretical levels, ranging from fully quantum descriptions to semiclassical and mesoscopic continuum approaches. This review rigorously discusses the theoretical steps and approximations connecting these levels, bridging quantum phonon Dyson and Kadanoff-Baym equations and semiclassical Boltzmann transport formalism, and discussing the coarse-graining procedures that yield mesoscopic viscous heat equations for non-diffusive, hydrodynamic heat transport in devices. We show how the Guyer-Krumhansl and dual-phase-lag equations emerge as special linear-isotropic-band and inviscid limits of the viscous heat equations, respectively; most importantly, we demonstrate that these equations predict not only Poiseuille flow and second sound, but also more exotic effects such as negative thermal resistance, steady-state thermal backflow and vortices. We highlight how combining these frameworks with first-principles simulations connects microscopic phonon physics to observable non-diffusive heat-transport phenomena and guides their detection, amplification, and control. We recast the viscous heat equations in terms of Helmholtz and biharmonic equations solved analytically, and use this to discuss similarities and differences between the macroscopic behavior of the phonon fluid and other hydrodynamic systems, such as classical and electron fluids, focusing on compressibility, vorticity, and their influence on phonon hydrodynamics. We conclude with a roadmap to generalize the tools used to describe phonon hydrodynamics to other quasiparticles, motivating future advances in collective quantum transport phenomena in solids.

Phenomenological models of non-Fourier heat conduction often lack a strict microstructural foundation, leading to ambiguities when modeling complex heterogeneous materials. In this study, we derive a continuum heat equation beyond Fourier's law using spatial volume averaging for a two-component system. We analytically prove that the experimentally observed static and dynamic thermal diffusivity arise directly from the distinct material properties, concluding that heterogeneous media are inherently over-diffusive. The resulting heat equation is thermodynamically compatible, and the microstructural origin allows the calculation of non-Fourier transport coefficients. Furthermore, we demonstrate that finite-sample boundaries introduce higher-order spatial non-localities, thereby explaining the size dependence of over-diffusion. We validate the model against experimental data across metal and carbon foams, rocks, and metal-organic frameworks.

Neural networks trained by gradient descent on a smooth cost function can nevertheless learn in steps: the cost holds on long plateaus and then drops abruptly. Meanwhile, training losses instead follow smooth power laws. Variants of both behaviors occur in architectures with very different microscopic structures, which is the signature of a few relevant collective variables. We show that a symmetry fixes what those variables are: a network layer is a sum over interchangeable units, so relabeling the units leaves it unchanged; given smoothness and the condition that a unit's gradient vanish at the origin, symmetry then enforces a universal leading form for the expansion about the near-zero weights present at the start of training, the quadratic $\Tr[WW^{\top}A(x)]$, in which every architectural detail is confined to a single “structure matrix" $A(x)$ that we compute for each architecture. Perceptrons, attention layers, mixtures of experts, and convolutions become one model at different $A$. Its training dynamics then close on the “order parameter" $M=WW^{\top}$ and, whenever the data matrices share an eigenbasis, reduce to a Lotka–Volterra equation whose modes switch on one after another. The smaller the initial weights, the further apart the switch-on times, and the plateaus appear as a singular limit of a smooth flow; when many modes are unresolved the same events merge into a power law in training time whose exponent the theory predicts. We confirm both numerically across training methods and architectures.

We theoretically investigate the possibility of engineering topological superconductivity in doubly coupled nanowires integrating proximity-induced superconductivity and altermagnetism. By tuning experimentally accessible parameters, we find four distinct topological superconducting phases: class D, class BDI, and two phases hosting two Majorana zero modes per end, protected respectively by spin-group and magnetic point-group symmetries. Beyond inheriting the advantages of alternating?magnetism induced topological superconductivity, our system provides multiple tuning knobs (e.g., superconducting phase difference and inter-wire coupling) to control topological properties.

In theoretical physics, finding connections between problems that appear in distinct contexts is an important way to leapfrog progresses, often by illuminating deep aspects that may otherwise seem obscure. In this paper, we consider in 2d CFTs the phenomenon of forbidden singularities in auto-correlation functions – a key signature of eigenstate thermalization. We show that they correspond to phase transitions in the context of eigenstates. The connection is made explicit by utilizing the AGT correspondence, which relates eigenstate auto-correlations to the Nekrasov partition functions describing an instanton gas of the $\mathcal{N}=2$ SUSY gauge theories. We show that by taking the counter-part of the heavy-light limit, two phases emerge for the instanton gas. They are dominated by configurations represented by string-like Young tableaux with distinct structures and thermodynamic properties, which bare resemblance to the confined and the deconfined phases. A phase transition occurs as instantons proliferate from one side, in a manner that mimics the Lee-Yang theory. We work out the critical fugacity and find it corresponding exactly to the forbidden singularity.

A main theme in modern condensed matter physics is the emergence of fractionalized excitations and gauge structures from quantum spin systems. However, understanding how these exotic degrees of freedom reconfine into new phases of matter remains a fundamental challenge. In this work, we demonstrate that Polyakov's confinement mechanism-a foundation of compact gauge theory-can serve as a dynamical engine to transform a Dirac spin liquid into a symmetry-protected topological (SPT) state. Starting with a Dirac spin liquid with spin-dependent gauge fluxes, we show that the single-occupancy constraint inherent to the physical Hilbert space triggers monopole condensation, dynamically confining bulk spinons while preserving gapless edge modes-realizing a spinon analog of the quantum spin Hall effect. Using a large-scale variational Monte Carlo simulation on a triangular antiferromagnet with an additional Dzyaloshinskii-Moriya interaction, we provide microscopic evidence for this confined SPT phase, including a characteristic area law for the Wilson loop and vanishing topological entanglement entropy. Furthermore, we identify a measurable spin-pump response under magnetic fields, which directly encodes the Berry curvature of the parent Dirac cones. Our results reveal a previously unexplored pathway to SPT physics, bridging the fields of gauge theory, quantum magnetism, and topological matter.

Bicontinuous interfacially jammed emulsion gels (bijels) facilitate efficient mass transport across multiple length scales due to their interwoven structure of particle-stabilised liquid channels. This unique morphology imparts considerable potential for applications in separation and catalysis, particularly when fabricated via solvent-transfer-induced phase separation (STrIPS). STrIPS enables the continuous, large-scale production of nanostructured bijel films on solid substrates, yet the influence of the substrate properties on the formation dynamics and final morphology remains insufficiently understood. In this study, this relationship is elucidated by preparing STrIPS bijel films on silane-functionalised glass substrates with selectively controlled wettability and analysing the resulting structure with confocal microscopy. The results showed the presence of notable wetting layers at the bijel-substrate interface, whose thicknesses could be tuned through the nanoparticle weight fraction. In line with numerical simulations, increasing the substrate hydrophobicity drove a transition from a laminar, water-rich surface layer to a patch-like, progressively oil-rich structure. These findings provide crucial insight into the structure-directing role of substrates in supported bijel films, which aids their application as functional materials.

Many traditional models of electoral dynamics emphasize opinion change, social influence and persuasion mechanisms. In contrast, contemporary polarized societies often exhibit relatively stable partisan blocs coexisting with an electorally relevant mobile population. We propose a minimal dynamical model in which electoral competition is governed not by direct persuasion between opposing blocs, but by the dynamics and allocation of a mobile electoral interface. The electorate is partitioned into two stable partisan blocs and a mobile fraction, while direct transitions between the polarized blocs are strongly suppressed. Mobile voters are allocated between the competing blocs through a Fermi-like probabilistic rule governed by rejection asymmetries. Analytical calculations show that the electoral susceptibility to political shocks is proportional to the stationary size of the mobile electoral interface, identifying electoral mobility as the key quantity controlling the macroscopic response of polarized systems to external perturbations. The model naturally predicts a continuum of mobility regimes, ranging from frozen polarization to highly responsive electoral states characterized by a broad mobile interface. These results suggest that, in strongly polarized elections, aggregate electoral changes may be governed primarily by fluctuations at the mobile electoral interface rather than by large-scale ideological conversion.

As phase transitions in isolated quantum systems remain elusive, here we show how a thermodynamic-like phase transition, falling into the Lee-Yang paradigm, can arise in systems displaying eigenstate thermalization. Specifically, we show that in holographic conformal field theories, the eigenstate expectation of the auto-correlation function can be mapped to the partition function ${\cal Z}_{{gauge}}(z)$ of a virtual interacting instanton gas, with the conformal mapping of the imaginary time: $z=1-e^{-\tau}$ and the central charge $c$ mimicking the instanton fugacity and volume, respectively. We find that akin to the Lee-Yang paradigm, for $c\to\infty$ a pair of complex conjugate zeros of ${\cal Z}_{{gauge}}(z)$ move to the real axis located at the famous forbidden singularity. Passing through the singularity the system transits from the low- to high-fugacity phase, accompanied by dramatic changes in scaling behaviors of the free energy and dominant microscopic configurations. Our findings indicate that physics of phase transitions from eigenstate thermalization is very rich.

Including higher order diagrammatic corrections to the strong-coupling expansion is mainly limited by the evaluation of high-dimensional, time-ordered integrals. In this work we present and compare four different parametrizations of the integrands in order to obtain a low-rank (quantics) tensor-train representation using tensor cross interpolation. Particular emphasis is placed on a quantics time-difference formulation in which the required retarded convolutions are performed directly in quantics tensor-train form. Using controlled Gaussian benchmarks, we analyze the accuracy, bond dimensions, and computational scaling of the different approaches. We then validate the most promising formulations in self-consistent equilibrium and nonequilibrium DMFT calculations and demonstrate calculations up to the third order in the strong-coupling expansion. Finally, we extend the solver to impurity models with retarded density-density interactions and apply it within nonequilibrium extended DMFT. Our results show that tensor cross interpolation substantially reduces the cost of evaluating higher-order diagrams and provides a controlled, systematically improvable framework for nonequilibrium quantum impurity calculations.

Inverse problems are ubiquitous in physics, chemistry, and engineering, arising when reconstructing hidden quantities from indirect measurements. A key example is the analytic continuation of imaginary-time correlation functions (iTCFs) to the real-frequency domain. This process requires an inverse Laplace transform, which is inherently ill-posed and highly sensitive to small input variations. Recent neural network (NN)-based methods have shown promising results by learning mappings from imaginary-time to real-frequency spectra, often outperforming traditional techniques such as maximum entropy. However, because the problem is ill-posed, many spectra fit the same iTCF. Regression-based approaches output a single solution, which approximates an average over the true solution space, and therefore fail to capture the full distribution of plausible power spectra. To address this issue, we introduce a diffusion-based framework for analytic continuation that learns the distribution of spectra consistent with a given iTCF. It offers two key advantages. First, it quantifies uncertainty directly from the learned distribution. Second, by analyzing the spread and structure of this distribution, we can quantitatively assess the intrinsic hardness of each inversion problem. We measure this hardness with a new metric, the uncertainty pseudo-volume. Applying the framework to an iTCF from a path-integral molecular dynamics simulation of liquid parahydrogen, we obtain the self-diffusion coefficient with an error bar and flag a secondary high-frequency peak as a possible spurious artifact. In contrast to previous attempts at uncertainty quantification, our generative approach rests on a concrete probabilistic basis, providing a more theoretically grounded measure of confidence in the reconstructed power spectra.

We report the magnetic properties of a series of lithium-rich antiperovskites (Li$_2M$)$Ch$O ($M$ = Fe, Co, Mn and $Ch$ = Se, S) where transition metal and lithium ions are randomly distributed on the X-sites of the X$_3$BA structure, thereby forming a strongly diluted magnetic sublattice. Our study hence enables us to investigate the evolution of magnetic order at fixed 1/3-filling – which is in the vicinity but slightly above the percolation threshold – upon variation of the spin size, the magnetic anisotropy, and the orbital configuration. The data imply the absence of a distinct Curie-Weiss behavior up to 350~K but show rather large and weakly temperature-dependent magnetic susceptibility. We observe clear signatures of long-range antiferromagnetic order evolving in the 1/3-filled and strongly diluted magnetic X-site lattice with increasing Néel temperatures from $T_{\rm{N}}\simeq 30$~K in (Li$_2$Mn)$Ch$O to $\simeq 50$~K in (Li$_2$Fe)$Ch$O and $70-90$~K in (Li$_2$Co)$Ch$O. Except for $M$ = Co, the chalcogenide has no sizable effect on $T_{\rm N}$. We conclude significant magnetic coupling and short-range magnetic correlations at well above $T_{\rm N}$ which is in line with the observation of a broad electron spin resonance signal at room temperature. The actual ordering temperatures are strongly diminished by magnetic dilution. While structural parameters such as the tolerance factor and bonding angles do not strongly affect $T_{\rm N}$, a key parameter is the magnetic anisotropy of the transition metals.

Physical neural networks perform learning through the intrinsic nonlinear dynamics of matter. Optimising their design presents a considerable challenge: complex many-body physics can provide powerful computation, but are expensive to simulate and large experimental optimisation runs are impractical to fabricate. Hence, the high-dimensional space of possible network topologies cannot be effectively directly searched. Here, we show that this search can be efficiently performed in an abstract graph space that is vastly cheaper to explore. Using random lasing networks – composed of interconnected nanoscale waveguides and hosting strongly coupled lasing modes – as an exemplar physical vision system, we establish a quantitative three-layer link: simple graph-theoretic metrics predict the nonlinear lasing physics, which in turn predicts vision performance. After validating this relationship using physical simulations, we exploit it to drive an evolutionary algorithm using graph metrics, producing network topologies that outperform random designs at a fraction of the computational cost (3000$\times$ speed-up compared to physical simulation). On simulated image-classification tasks, graph-optimised networks substantially improve classification accuracy. As our framework operates on network topology rather than substrate-specific physics, we anticipate it can transfer to other network-based physical learning systems, providing an efficient route for the directed design and optimisation of complex, strongly-interacting physical neural networks.

Room-temperature spin-optoelectronic devices require a combination of robust ferromagnetism and giant exciton binding, a pairing mutually exclusive in conventional semiconductors due to magnetic localization that screens excitons. Cluster-assembled V4S9X4 (X = F, Cl, Br and I) monolayers overcome this bottleneck via a hierarchical design, that is, intra-cluster localized states host both local magnetic moments and strong electron-hole interactions, while inter-cluster coupling mediates long-range ferromagnetism. Remarkably, these two-dimensional semiconductors exhibit intrinsic ferromagnetism with Curie temperature up to 507.6 K. As a prototype, V4S9Br4 monolayer possesses a giant exciton binding energy of 1.85 eV. Its lowest exciton is a dark state (DI) with a radiative lifetime of 1.20 ns, whereas the first bright exciton (BI) exhibits an ultrafast radiative decay of 86.87 ps. This stark lifetime contrast enables simultaneous ultrafast optical response and long-lived spin information storage. Most notably, switching between ferromagnetic and antiferromagnetic order allows for wide-range tuning of exciton lifetime, with the giant binding energy remaining nearly intact. Our findings establish cluster assembly as a powerful paradigm for designing next-generation spin-photonic and quantum information devices operating at room temperature.

Using patch-matrix-product methods, we study two reversible three-state interaction-round-a-face cellular automata introduced by Klobas and Prosen [J. Phys. A 55, 094003 (2022)] - the species-preserving and species-flipping rules - and a coherent quantum deformation interpolating between them. Within an explicit translationally invariant ansatz, the two classical rules share a one-parameter family of quasi-local conservation laws with an auxiliary-dimension-three realization. Every regular member is also the first centered logarithmic derivative of an analytic auxiliary-dimension-two family passing through the identity observable. On the closed span of this family and the three elementary local charges, the only nonzero Euler velocities are $\pm\sqrt{3/23}$. Consequently, $\sqrt{3/23}$ is a rigorous lower bound on the maximal Euler speed in any larger conserved sector. For the species-flipping rule, this value agrees with the reported extrapolated value $0.361$ of Klobas and Prosen, strongly supporting completeness of the known sound-active charges. The species-preserving rule admits, in addition, a staggered generating family whose logarithmic derivatives form an infinite tower of strictly local charges. In the quantum deformation, the same algebraic structures yield exact low-bond-dimension invariant states (scar candidates), exponentially long-lived quasi-local quasimodes, and diagonal quasi-local charges that produce a nonzero Mazur bound after projection away from the three elementary local charges.

Energy and charge transfer in condensed matter systems are often described in terms of quasiparticles. The order of accuracy of the results in terms of the electron-to-nucleus mass ratio $\epsilon$, an adiabatic small parameter, is usually not known. To quantify energy transfer with controlled accuracy in $\epsilon$, we use adiabatic perturbation theory to derive a formula for the rate of change of the nuclear kinetic energy. Applying a sequence of near-identity unitary transformations decouples the electronic and nuclear degrees of freedom to higher and higher order and produces an effective Hamiltonian. In the special case that the nuclei are treated classically, the energy transfer formula reduces to the usual formula for the rate of work done on a classical particle but the mass of the particle is enhanced by the factor $M_0^{-1}(M_0+\epsilon M_1)$, where $M_1$ is a nonadiabatic correction to the bare nuclear mass tensor $M_0$. Additional quantum geometric corrections appear at higher orders. Adiabatic perturbation theory is further used to incorporate, order-by-order in $\epsilon$, nonadiabatic transitions into linear response calculations. The nonadiabatic frequency-dependent conductivity and Drude weight of an electron-ion system are calculated.

The gapped Kondo effect describes the screening of an $S=\frac12$ impurity spin locally coupled via an antiferromagnetic exchange interaction to a conduction-electron system exhibiting a finite hard gap. Using a combination of a Lanczos transformation and a self-consistent configuration-interaction scheme, we numerically investigate the local phase diagram. Furthermore, we show that the different phases can be characterized by several topological invariants: the conventional momentum-space Chern number of the underlying two-dimensional host system, corresponding to a Chern insulator; the B-space Chern number, defined by coupling the impurity spin to a fictitious local magnetic field $\boldsymbol B$, in the limit $B \to 0$; and the S-space Chern number, defined for a classical impurity spin, i.e., a vector of fixed length. The classical-spin limit is obtained for $B \to \infty$. By varying the field strength, we can therefore continuously interpolate between quantum-impurity-spin and classical-impurity-spin Hamiltonians and investigate whether the corresponding phase diagrams are likewise continuously connected. The gapped underscreened Kondo effect is studied for an impurity spin $S>\frac12$ as well as in the classical-spin limit approached via $B \to \infty$ or $S \to \infty$. Different variants of scattering theory are employed to interpret the resulting phases. Finally, the gapped two-channel overscreened Kondo effect, realized by coupling a quantum impurity spin equally to the local electron spins of both orbitals within a unit cell, is shown to be characterized by spontaneous particle-hole symmetry breaking. This leads to a highly nontrivial quantum-classical phase diagram.

The study of magnetic chaos has traditionally focused on macroscopic variables under external driving. Here we demonstrate a new type of magnetic chaos, termed moiré magnetic chaos, associated with mesoscopic magnetic domain variables in twisted bilayer CrI3 without external driving. The domains are stabilized by a characteristic interlayer exchange frustration, which supplies the multiple dynamical degrees of freedom required for autonomous chaos. Through micromagnetic simulations, we show that relaxation toward moiré magnetic textures is extremely sensitive to minute local perturbations of the initial state, characterized by substantial finite-time Lyapunov exponents and a final-state sensitivity that persists over five decades of perturbation amplitude. Statistical analysis further reveals that the resulting domain configurations are stochastic and pairwise uncorrelated. Our results identify a form of microscopic, undriven chaos in twisted magnets that extends nonlinear magnetism beyond the conventional driven regime.