Papers

We introduce the pushy random walk, where a walker can push multiple obstacles, thereby penetrating large distances in environments with finite obstacle density. This process provides a minimal model for experimentally observed interactions of active particles with dense, deformable media. Using scaling arguments and numerical simulations, we show that in one dimension the walker carves out an obstacle-free cavity whose length grows subdiffusively with time. In two dimensions, increasing obstacle density drives a transition from free diffusion to localized behavior, where the walker is trapped within a cavity whose radius again grows subdiffusively with time. These results show how tracer-induced rearrangements qualitatively reshape transport in crowded media.

A quantum anomalous Hall system is predicted to realize topological superconductivity when proximity-coupled to an $s$-wave superconductor. A kagome magnet with chiral magnetic ordering exhibits the quantum anomalous Hall effect; however, superconducting proximity to an ordinary $s$-wave superconductor fails to induce pairing in the strong exchange coupling limit. In this work, we demonstrate that proximity coupling to a Rashba superconductor gives rise to topological superconducting phases characterized by odd Bogoliubov-de Gennes Chern numbers. We confirmed their consistency with the chiral central charge calculated based on the modular commutator. We also show that the magnetic ordering of kagome magnets is affected energetically by the proximity effect.

Non-Hermitian physics exhibits unique physical properties beyond those of traditional Hermitian systems, such as symmetry breaking, the emergence of exceptional points, topological phase transitions, and more. These phenomena have been extensively studied across various platforms, including quantum optics, cold atom systems, superconducting circuits, and condensed matter physics. Rydberg atoms, with their long-range interactions and flexible controllability, provide a promising platform for the experimental realization of non-Hermitian physics. This review primarily summarizes the key experimental and theoretical achievements in the field of non-Hermitian physics within Rydberg atomic systems in recent years. It outlines the fundamental construction of non-Hermitian Hamiltonians, reveals the effective dissipation mechanisms induced by Rydberg atomic interactions, and discusses their impact on spectral properties and symmetry breaking. These studies not only deepen the understanding of quantum phase transitions in non-Hermitian many-body systems but also highlight the unique value of Rydberg atomic platforms in realizing and controlling topological states.

This paper introduces ELAS3D-Xtal, a high-performance Fortran/OpenMP upgrade of the NIST ELAS3D voxel-based finite element solver for computing 3D elastic fields in polycrystals with defects. The code supports crystal anisotropy by precomputing rotated stiffness tensors from user-specified orientations and solves the equilibrium problem with a matrix-free, OpenMP-parallel preconditioned conjugate-gradient (PCG) method using a point-block Jacobi preconditioner. On a single shared-memory multicore PC, OpenMP threading accelerates the baseline CG solver by ~10X, while the block-preconditioned CG solver achieves 53-61X speedup relative to the serial CG baseline for meshes from 100^3 to 500^3 voxels (scaling to domains up to 800^3 voxels). Accuracy is validated against the analytical Eshelby inclusion solution. ELAS3D-Xtal also integrates microstructure construction, including statistically calibrated polycrystal generation via spatial filtering and parallel voxel-to-grain assignment, direct pore insertion from XCT centroid/radius data, and texture assignment. Full-field phase, orientation, and stress outputs are written in HDF5 to enable scalable post-processing and defect-mechanics workflows. Applications are demonstrated for (i) anisotropy-controlled defect-scale stress fields and (ii) LPBF SS316L microstructures with gas, lack-of-fusion, and keyhole pore morphologies.

Metal-organic frameworks (MOFs) are promising materials for applications such as direct air capture, where performance depends on how adsorbates bind within the framework. Accurately predicting MOF-adsorbate configurations is therefore important for screening candidate materials. However, traditional approaches are computationally expensive and require a known host structure, while existing generative models rely on rigid-body assumptions and do not explicitly model adsorbates. We introduce AtomMOF, a scalable all-atom flow-matching model that jointly predicts MOF-adsorbate structures from discrete building blocks and adsorbates. Built on a Diffusion Transformer with a building block-based pairwise attention bias, AtomMOF operates in an unconstrained all-atom space and exhibits clear scaling behavior. To improve the structural validity of flexible all-atom models, we also propose Feynman-Kac (FK) steering with machine-learned interatomic potentials (MLIPs). On the BW dataset, AtomMOF achieves a 85.61% relative increase in match rate and a 34.32% relative reduction in RMSD compared with prior work. MLIP-guided steering further improves validity by 28.7% and reduces formation energy error by 86.5%. On ODAC25, AtomMOF generates adsorption configurations faster than GCMC and, when combined with MLIP relaxation, identifies lower-energy configurations than those in the reference dataset.

Singular value decomposition (SVD) provides a convenient way to construct Hermitian-like diagnostics for non-Hermitian many-body systems, but its reliability for locating many-body localization (MBL) transitions remains unclear, particularly in systems preserving time-reversal symmetry (TRS). We benchmark SVD-based diagnostics against exact diagonalization (ED) in TRS-preserving non-Hermitian hard-core-boson chains with nonreciprocal hopping, considering quasiperiodic, random-disorder, and Stark potentials. We compare level statistics, half-chain entanglement entropy, inverse participation ratio, and spectral form factors. For the quasiperiodic and random-disorder models, ED-based entanglement and IPR yield mutually consistent finite-size transition estimates, whereas the corresponding SVD-based estimates are systematically shifted to larger disorder strengths and can lead to different phase assignments. The discrepancy is also reflected in the spectral form factors, where the ED-based dissipative spectral form factor and the SVD-based singular form factor can indicate different spectral regimes at the same parameters. In contrast, for the clean Stark model, ED and SVD give consistent transition estimates. We identify the origin of this model dependence as the fact that SVD probes the auxiliary Hermitian operator $\hat H^\dagger\hat H$, rather than the intrinsic right-eigenstate structure of $\hat H$; consequently, SVD can be quantitatively reliable only when the corresponding bulk-state structures remain aligned. Our results show that SVD-based diagnostics can capture qualitative RMT-to-Poisson trends, but are not generically reliable quantitative probes of MBL transitions in TRS-preserving non-Hermitian many-body systems.

Photon-assisted tunneling arises under an ac bias, with the drive frequency setting the photon energy. The excess photon-assisted noise is defined as the difference between the shot noise under a combined dc and ac bias and that under a dc bias alone. We investigate this quantity in tunneling into Majorana or Andreev bound states, which are of great interest in the search for topological superconductors. Under a harmonic bias $V(t)=V_\mathrm{dc}[1-\cos(\Omega t)]$, the excess photon-assisted noise exhibits distinct behaviors: for Majorana or quasi-Majorana bound states, it undergoes multiple sign reversals as $V_\mathrm{dc}$ increases and vanishes at nonzero integer values of $eV_\mathrm{dc}/\Omega$ (with $e$ the elementary charge), whereas for zero-energy Andreev bound states–particularly those producing nearly quantized zero-bias conductance peaks–it remains strictly negative over the entire $V_\mathrm{dc}$ range.

Deploying fibrous membranes for passive daytime radiative cooling (PDRC) on large and irregular surfaces is highly desirable but remains challenging, owing to the slow deposition rates and the need for electrically conductive substrates in conventional electrospinning. Here, we demonstrate a high-throughput in-situ strategy for fabricating nanocomposite PDRC fibrous membranes via solution blow spinning. This method achieves deposition rates 8-12 times faster than electrospinning and can be applied directly onto nonplanar, nonconductive objects. The resulting membranes, composed of styrene-ethylene-butylene-styrene (SEBS) fibers embedded with Y2O3 nanoparticles, achieve sub-ambient cooling of up to 7.0 {\deg}C outdoors, effectively delaying ice melting. Moreover, they are fully recyclable through simple cleaning, dissolution, and reprocessing. This scalable and sustainable fabrication route provides a versatile and practical platform for integrating PDRC fibrous membranes across diverse surfaces, paving the way toward real-world thermal management applications.

Topological magnetic lattices offer a fertile ground for exploring fundamental physics and developing novel spintronic devices. However, current research is predominantly confined to single-Q topologies hosting uniform type of quasiparticle. The realization of exotic mixed-Q states, where distinct topological quasiparticles co-assemble into an ordered lattice, remains largely unexplored. Here, we propose a generic mechanism to order disordered mixed-Q topological magnetism into periodic lattice via moire engineering. By leveraging the synergy between spatially modulated interlayer coupling and intrinsic intralayer magnetic frustration, we demonstrate that moire potential can effectively regularize skyrmions, antiskyrmions, and magnetic bubbles into a hybrid lattice. Combining first-principles with atomistic spin simulations, we validate this mechanism in twisted bilayer CrGaTe3, identifying it as an exemplary platform for hosting these complex ordered textures. We systematically map the phase evolution as a function of twist angle and biaxial strain, unveiling the critical role of moire potential in stabilizing mixed-Q lattice. Our findings significantly advance the frontier of topological and moire spintronics.

Ensemble Density Functional Theory (EDFT) is a promising extension to Density Functional Theory (DFT) for calculating excited states. While Kohn-Sham eigenvalue differences underestimate gaps, EDFT has been shown to provide more accurate excitation energies in atoms, molecules and isolated model systems. However, it is unclear whether EDFT is capable of calculating band gaps of periodic systems – and what an appropriate theoretical formulation would be to describe periodic systems. We explored how EDFT could calculate band gaps by estimating the thermodynamic limit with increasingly wide finite versions of the one-dimensional Kronig-Penney (KP) periodic model. We use Octopus, an ab initio, open-source, real-space DFT code, as in our previous work [R. J. Leano et al., Electron. Struct. 6, 035003 (2024)] in which we found with "particle in a box" models that EDFT can provide a reasonable effective mass correction for the homogeneous electron gas. Now, we use a periodic reference that is gapped. We find that the finite systems' Kohn-Sham gap approaches the same periodic limit for each of three ways of terminating the finite system, though the appropriate states corresponding to the valence band maximum and conduction band minimum have to be carefully identified in each case. Finally, our EDFT results, using a simple ensemblized LDA approximation, have a reasonable nonzero correction to the bandgap in the periodic limit. The results indicate that EDFT is promising for periodic systems, to motivate further work on developing a suitable formalism.

Fast prediction of suspension rheology is fundamental for optimizing process efficiency and performance in numerous industrial settings. However, traditional simulations are computationally demanding due to explicit evaluation of contact networks and stress tensors in dense regimes approaching shear thickening and jamming. This study presents a microstructure-informed multitask learning framework based on the graph neural network (GNN) that learns an implicit mapping between particle configurations and emergent microstructural and rheological properties of suspensions. This model simultaneously predicts particle pressure $\Pi$, viscosity $\eta$, and friction coordination $Z_\mu$, in a dynamic steady-state, without explicit knowledge of interparticle forces. Here, semi-dilute to dense suspension systems in 2D were simulated across a wide range of shear stresses $\sigma$, spanning continuous, discontinuous shear thickening, and shear-jamming conditions. The trained models demonstrated high correlation coefficients ($R^2$ = 0.99) with narrow mean absolute error for packing fractions up to $\phi \le \phi_J^\mu$ for all predictive targets. However, prediction scatter increases near jamming conditions, attributed to inherent fluctuations in suspension behavior as the critical packing fraction is approached, yet predictions remain in excellent agreement, closely following the trend of the simulated flow curves across stress evolution. Once trained, the model can infer rheological responses directly from structural topology, avoiding explicit stress evaluation during prediction. The approach yields computationally efficient mesoscale surrogates for accelerated simulation with potential for real-time exploration of particulate suspension behavior.

Metal-organic Frameworks (MOFs) have emerged as potential candidates for direct air capture (DAC) of green house gases and water. Thermal properties of MOFs, such as their heat capacity, are used to determine the energy penalty associated with the adsorbent retrieval during the Temperature Swing Adsorption process. To aid exploration of the vast experimental design space of MOFs for such applications, computational methods like Density Functional Theory (DFT) or surrogate machine learning models trained on DFT data have been developed for obtaining phonon-derived heat capacities of MOFs. However, the high cost of explicit phonon computation in large and flexible nanoporous MOFs often necessitates the use of small supercells or lower convergence criteria which decrease predictive accuracy. These approximations often result in spurious imaginary phonon modes which are commonly ignored in practice. At present, there is no clear consensus in the literature on what magnitude of negative frequency or what fraction of imaginary modes can be considered acceptable. Here, we systematically demonstrate that spurious imaginary phonon modes can introduce substantial errors in heat capacity estimates, leading to incorrect ranking of MOFs in thermal-property-based screening. We further show that benchmarking machine learning interatomic potentials (MLIPs) against DFT datasets containing spurious imaginary modes can misrepresent models that predict physically meaningful phonon spectra for dynamically stable MOFs. Finally, we introduce a simple, rapid post-processing workflow that can be applied to standard phonon calculations to effectively correct heat capacity estimates and account for spurious imaginary modes in MOFs.

The development of structural materials capable of sustained operation above 1200 {\deg}C is critical for next-generation energy and aerospace systems; however, Ni-based superalloys are fundamentally constrained by their melting temperatures, while conventional Nb-based refractory alloys are limited by modest specific strength and high cost. Here, we report on the design and mechanical performance of a cost-effective, non-equiatomic refractory complex concentrated alloy (RCCA), Nb45Ta15Ti20V20, engineered to overcome these limitations. Specifically, its specific strength surpasses wrought C-103 and rivals additively manufactured (AM) C-103 at temperatures up to 1300 {\deg}C while maintaining extensive room temperature tensile ductility (>10 %). Coupled with a high melting point (~2167 {\deg}C), reduced density (8.67 g/cc), and a raw material cost of ~$130/kg compared to >$500/kg for wrought C-103 and >$2,500/kg for AM C-103, this alloy delivers superior specific strength-cost efficiency, highlighting the promise of non-equiatomic RCCAs as viable alternatives to commercial refractory alloys.

A geometrically nonlinear continuum mechanical theory is formulated for deformation and failure behaviors of amorphous polymers. The model seeks to capture material response over a range of loading rates, temperatures, and stress states encompassing shock compression, inelasticity, melting, decomposition, and spallation. Thermoelasticity, viscoelasticity, viscoplasticity, ductile failure with localized shear yielding, and brittle fracture with crazing can all emerge under this ensemble of intense loading conditions. Known prior theories have considered one or more, but not all, such physical mechanisms. The present coherent formulation invokes thermodynamics with internal state variables for dynamic molecular and network configurational changes affecting viscoelasticity and plastic deformation, and it uses order parameters for more abrupt structural changes across state-dependent glass-transition and shock-decomposition thresholds. A phase-field order parameter captures material degradation from ductile or brittle fracture, including evolving porosity from crazing. The theory is applied toward polymethyl methacrylate (PMMA) under intense dynamic loading. The high-pressure equilibrium response, with shear strength and temperature over known ranges, is well represented along the principal Hugoniot to pressures far exceeding shock decomposition. Predicted release wave velocities agree with experiment. A semi-analytical solution for steady waves describes the relatively lower-pressure viscoelastic setting, providing insight into relaxation times. One-dimensional calculations assess suitability of the model for representing spall fracture strengths seen in experiments over a range of initial temperatures and loading rates.

On the contrary to the common intuition, which suggests that a steep expulsive potential makes quantum states widely delocalized, we demonstrate that one- and two-dimensional (1D and 2D) Schroedinger equations, which include expulsive potentials that are steeper than the quadratic ones, give rise to normalizable eigenstates, which may be considered as a manifestation of effective self-trapping in the linear system. These states constitute full continuous spectra in both the 1D and 2D cases. In 1D, they are spatially even and odd eigenstates. The 2D states may carry any value of the vorticity (alias magnetic quantum number). Asymptotic expressions for wave functions of the 1D and 2D eigenstates, valid far from the center, are derived analytically, demonstrating excellent agreement with full numerical solutions. Special exact solutions for vortex states are obtained in the 2D case. These findings suggest an extension of the concept of bound states in the continuum, in quantum mechanics and paraxial photonics. Gross-Pitaevskii equations are briefly considered as the nonlinear extension of the 1D and 2D settings. In 1D, the cubic nonlinearity slightly deforms the eigenstates, maintaining their stability

Symbolic sequences such as written language and genomic DNA display characteristic frequency distributions and long-range correlations extending over many symbols. In language, this takes the form of Zipf's law for word frequencies together with persistent correlations spanning hundreds or thousands of tokens, while in DNA it is reflected in nucleotide composition and long-memory walks under purine-pyrimidine mappings. Existing surrogate models usually preserve either the frequency distribution or the correlation properties, but not both simultaneously. We introduce a surrogate model that retains both constraints: it preserves the empirical symbol frequencies of the original sequence and reproduces its long-range correlation structure, quantified by the detrended fluctuation analysis (DFA) exponent. Our method generates surrogates of symbolic sequences by mapping fractional Gaussian noise (FGN) onto the empirical histogram through a frequency-preserving assignment. The resulting surrogates match the original in first-order statistics and long-range scaling while randomising short-range dependencies. We validate the model on representative texts in English and Latin, and illustrate its broader applicability with genomic DNA, showing that base composition and DFA scaling are reproduced. This approach provides a principled tool for disentangling structural features of symbolic systems and for testing hypotheses on the origin of scaling laws and memory effects across language, DNA, and other symbolic domains.

Chiral perovskites owing to their broken mirror symmetry exhibit selective absorption of circularly polarized light manifesting a strong circular dichroism (CD). CD spectroscopy has been a key technique to understand chiral perovskites and how these semiconductors achieve chirality at the molecular level. However, there is a debate on whether the observed CD is intrinsic to the chiral crystal structures or is modulated by extrinsic phenomena particularly linear dichroism (LD) and linear birefringence (LB) effects. This work investigates the chiroptical properties of $(R-/S-\text{MBA})_2\mathrm{CuCl}_4$ (MBA = Methylbenzyl ammonium) series by thoroughly studying the contribution from LD and LB to the observed CD signals. The comparison of highly oriented and randomly oriented films exhibits notable LD and LB contributions to the observed CD, which are caused by orientation-dependent electric-field interactions and local anisotropy. Both randomly and highly oriented films exhibit distinct CD responses, with LD–LB effects largely dominating the CD in highly oriented films. This has been revealed by the obvious shift in the observed CD signals to the below-absorption-edge ($\sim 430$ nm) regime and broadening of features. Our findings demonstrate that careful consideration of crystal orientation and structural effects is necessary for appropriate interpretation of CD spectra in chiral perovskite thin films.

A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or {\sigma}-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e).

The seminal 2009 paper by Bernard, Krauth, and Wilson marked a paradigm shift in Monte Carlo sampling. By abandoning the restrictive condition of detailed balance in favor of the more fundamental principle of global balance, they introduced the Event-Chain Monte Carlo (ECMC) algorithm, which achieves rejection-free, deterministic sampling for hard spheres. This breakthrough demonstrated that persistent, directional dynamics could dramatically accelerate equilibration in dense particle systems. In this commentary, we review this foundational work and elucidate its underlying mechanism using the broader Event-Driven Monte Carlo (EDMC) framework developed in subsequent years. We show how the original hard-sphere concept naturally generalizes to continuous potentials and modern lifted Markov chain formalisms, transforming a surprising specific result into a powerful general class of sampling algorithms.

Combined high-energy X-ray diffraction microscopy (HEDM) and crystal plasticity finite element (CPFE) modeling studies have emerged as a preferred paradigm to shed insight into the evolution of elasticity and plasticity at the intragrain scale of polycrystals. In particular, far-field HEDM measures the deformation response of upwards of thousands of individual grains simultaneously in situ during mechanical loading, though measurements are primarily limited, however, to the average state of each grain – i.e., the grain's full strain tensor, crystallographic orientation, spatial location and volume. CPFE is utilized to shed information on the intragrain deformation response, via the sub-discretization of each grain into many finite elements, though the direct point of comparison to HEDM remains the grain-averaged response. We thus seek to find the minimum simulation conditions necessary to provide consistent grain-averaged predictions in an attempt to limit computational cost. In this study, we perform a suite of simulations and systematically study the effects of mesh density and boundary conditions, and consider different materials. We discuss these results and show that accurate prediction of grain-averaged elastic strains in a given region of interest typically requires a mesh with 250 elements per grain on average and a buffer layer of at least three grains between the region of interest and the control surfaces.

While superconductors are highly attractive for energy-efficient computing, fundamental limitations in their logic circuit integration have hindered scaling and led to increased energy consumption. We therefore propose and experimentally demonstrate a novel superconducting switching device utilizing the proximity magnetization from a spin-orbit torque-switched magnet to control the resistivity of a superconductor. We further propose a complete logic family comprised solely of these devices. This novel implementation has the potential to drastically outperform existing superconducting logic families in terms of energy efficiency and scalability.

Spin-orbit coupling in two-dimensional materials gives rise to a Rashba spin splitting when inversion and mirror symmetries are broken, yet its microscopic origin and quantitative characterization in transition metal dichalcogenides remains incomplete. Both symmetries are broken in certain bilayer structures, enabling Rashba splittings in the absence of external electric fields. We determine this zero-field offset and the Rashba parameters that dictate the spin splitting in the linear regime. Surprisingly, the splitting is substantially smaller in bilayers than in monolayers at typical fields. This is clarified within a perturbative microscopic model, revealing that the spin splitting results from a competition between internal polarization and interlayer hybridization. We further introduce the orbital polarization imbalance as an order parameter that captures the asymmetry of the valence bands and determines the spin ordering of the Rashba-split states. Our results are both quantitative and qualitative, as they clarify the nature and origin of Rashba coupling in transition metal dichalcogenides.

Optical transmission spectra of YAl3(BO3)4 crystals doped with the Pr3+ ions in concentrations 1 and 2.5 at. % were studied by high-resolution (up to 0.05 cm-1) Fourier spectroscopy, including in magnetic field parallel to the trigonal c axis of the crystal. The g factors of several crystal-field doublets of Pr3+ were determined. The crystal-field calculations performed using the exchange-charge model and high-resolution spectroscopy data allowed us to obtain a physically reasonable set of crystal-field parameters. The observed splitting of a number of doublets in zero magnetic field is explained by the presence of random lattice deformations. Simulation of the profiles of observed deformational doublets was carried out taking into account both hyperfine and electron-deformation interactions. The width of the distribution function of random strains was estimated. The main sources of random strains in YAl3(BO3)4:Pr3+ crystals are discussed.

The magnetic excitations of the skyrmion lattice in MnSi comprise a multitude of individual modes, which are non-reciprocal and thereby propagate unidirectionally. We report inelastic neutron scattering experiments for temperatures near and above the skyrmion-paramagnetic phase transition in the chiral magnet MnSi tracking the evolution from the skyrmion lattice towards the high-temperature paramagnetic state. Within the resolution of the triple-axis measurements the excitations vary smoothly across the skyrmion-paramagnetic boundary, and, the quasi-elastic paramagnetic signal under applied field retains the non-reciprocal character seen in the skyrmion phase even far above the critical temperature. Using a resolution-convolution our results are consistent with linear spin-wave theory.

We show that quantum fluctuations of electromagnetic fields induce an extensive zero-point energy in insulators. The zero-point energy density is proportional to the quantum fluctuation of electric polarization in the many-body ground state, a fundamental quantum geometric property of solids known as the quantum weight. For fields produced by a superconducting LC circuit, the zero-point energy results in a repulsive force between the circuit and the material and an attractive force on the capacitor plates, which we call quantum geometric force. The force on the capacitor plates is proportional to the quantum weight, inversely proportional to the plate area, and independent of the plate distance. The proposed effects provide direct experimental access to the many-body quantum geometry and reveal a new quantum geometric effect in macroscopic solids induced by vacuum fluctuation.

In recent years, the traditional notion of symmetry in quantum theory was expanded to so-called generalised or categorical symmetries, which, unlike ordinary group symmetries, may be non-invertible. This appears to be at odds with Wigner's theorem, which requires quantum symmetries to be implemented by (anti)unitary – and hence invertible – operators in order to preserve probabilities. We resolve this puzzle for (higher) fusion category symmetries $\mathcal{C}$ by proposing that, instead of acting by unitary operators on a fixed Hilbert space, symmetry defects in $\mathcal{C}$ act as isometries between distinct Hilbert spaces constructed from twisted sectors. As a result, we find that non-invertible symmetries naturally act as trace-preserving quantum channels. Crucially, our construction relies on the symmetry category $\mathcal{C}$ being unitary. We illustrate our proposal through several examples that include Tambara-Yamagami, Fibonacci, and Yang-Lee as well as higher categorical symmetries.

Topological superconductors (TSCs) provide a promising route to fault-tolerant quantum information processing. However, the canonical Majorana platform based on $2e$ TSCs remains computationally constrained. In this work, we find a $4e$ TSC that overcomes these constraints by combining a charge-$4e$ condensate with an Abelian chiral $\mathbb{Z}_3$ topological order in an intertwined fashion. Remarkably, this $4e$ TSC can be obtained by proliferating vortex-antivortex pairs in a stack of two $2e$ $p+ip$ TSCs, or by melting a $\nu=2/3$ quantum Hall state. Specific to this TSC, the $hc/(4e)$ fluxes act as charge-conjugation defects in the topological order, whose braiding with anyons transmutes anyons into their antiparticles. This symmetry enrichment leads to $\mathbb{Z}_3$ parafermion zero modes trapped in the elementary vortex cores, which naturally encode qutrits. Braiding the parafermion defects alone generates the full many-qutrit Clifford group. We further show that a single-probe interferometric measurement enables topologically protected magic-state preparation, promoting Clifford operations to a universal gate set. Because the non-Abelian modes are bound to flux defects, they can, in principle, be externally controlled using superconducting circuit-based technology. More broadly, our results highlight hierarchical electron aggregation, the formation and condensation of higher-charge electron clusters, as a design principle for topological quantum matter with increased computational capability.

Although effective non-reciprocal interactions have been investigated in a variety of fields, their consequences have not been explored in hydrodynamical turbulence. We initiate such an exploration by introducing non-reciprocal binary-fluid tubulence and uncover its properties by developing a two-dimensional (2D) Non-Reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS) model. We show that, as we increase the strength of the non-reciprocal terms, this model displays a hitherto unanticipated type of turbulence, with an inverse cascade of energy and an energy spectrum $E(k)\sim k^{-5/3}$, reminiscent of the well-known inverse cascade in forced, 2D fluid turbulence, but distinct from it, in so far as it develops a non-reciprocal flux $\mathbf J$. We demonstrate how NRCHNS turbulence suppresses $J(t) = |\mathbf J|$, as the Reynolds number increases. We compare and contrast 2D NRCHNS turbulence with its fluid-turbulence counterpart by examining spectra, fluxes, spectral balances, flow topologies, and signatures of multifractality.

Finding analytically tractable model systems for quantum ergodicity breaking in interacting systems remains a theoretical challenge. This paper presents an algebraic theory of robust ergodicity breaking in time-periodic unitary circuits under local causal constraints. Our model is based on constructing tripartite unitaries (we dub 'walls') that permanently arrest local operator spreading. We show that the structure of the resulting causally independent subsystems can be understood rigorously through the invariance of embedded operator algebras (i.e. super-operator symmetries). This formalism gives a natural identification of local conserved quantities and permits the generalisation to time-dependent dynamics. Using representation theory, the general form of unitaries exhibiting causal decoupling is derived from the automorphism group of the embedded algebra with links to quantum error-correcting codes. From the point of view of operator spreading, our theory is a minimal model for non-ergodic quantum-circuit dynamics, and we explore its effects on probes of many-body quantum chaos. We prove an entanglement area law due to causal constraints and discuss its stability against local measurements. In a random unitary ensemble with causally independent subsystems, we compare spectral correlations with those of the universal random matrix ensemble using the spectral form factor. Our results offer a rigorous understanding of locally constrained quantum dynamics from a quantum information perspective.

Cs$_2$KInI$_6$ is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm$\bar{3}$m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc$2_1$ symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P$\bar{3}$ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first-principles validation for discovering stable, complex materials.