Papers

We extend our previous analysis [Phys. Rev. D 109, 065005 (2024)] of CPT-odd electromagnetic response in tilted, anisotropic Weyl semimetals to the overtilted (type-II) regime, where electron and hole pockets coexist at the Fermi level. Starting from the minimal QED sector of the Standard-Model Extension matched to a lattice-motivated anisotropic Dirac Hamiltonian with tilt, we compute the zero-temperature finite-density effective action nonperturbatively from the vacuum polarization tensor, and corroborate the result using a complementary chiral kinetic theory formulation that consistently incorporates both Fermi-sea and Fermi-surface contributions. In the type-II regime the unbounded linear dispersion necessitates a physical ultraviolet regularization. Implementing a hard momentum cutoff tied to the lattice bandwidth, we show that the CPT-odd, axion-like response remains finite across the type-I to type-II Lifshitz transition, while acquiring tilt- and anisotropy-dependent renormalizations together with nonuniversal, cutoff-sensitive terms governed by the geometry of the electron and hole pockets. As a concrete application, we evaluate the anomalous Hall conductivity in the prototypical type-II Weyl semimetal WTe$_2$, using parameters extracted from first-principles calculations and experiments, and find that Fermi-sea and Fermi-surface contributions are comparable and partially cancel, yielding a finite and strongly anisotropic Hall response characteristic of the overtilted regime.

Quantum error-correcting codes with translation symmetry and local checks have been studied extensively, leading to a wide variety of fracton codes in three or more dimensions which lack a complete unifying picture. Recently, the study of translation-invariant codes with long-range checks has revealed impressive performance for small fixed-size instances in two dimensions. Here, we provide a unifying picture for a large family of translation-invariant codes, both local and long-range, that captures many fracton codes and all Abelian Two-Block Group Algebra (A2BGA) codes, including the Bivariate Bicycle (BB) codes. The balanced product structure of A2BGA codes leads to a local parent code that is a hypergraph product fracton model in a higher dimension. Different compactifications of a parent code produce a wide variety of descendant codes which provides a unifying picture for their properties. In particular, all BB codes with the same check weight are derived from a single parent hypergraph product fracton model. This construction allows us to extend Wang and Pryadko's code-parameter bounds for Generalized Bicycle codes to A2BGA codes. We conjecture that the transversal gates and energy barriers of the translation-invariant descendant codes are limited by those of their parent fracton models.

The discovery of high-temperature superconductivity in Ruddlesden-Popper nickelates has drawn great attention. However, unlike cuprates and iron-based superconductors, Ruddlesden-Popper nickelates exhibit superconductivity either under high pressure in bulk samples or under compressive strain in thin films. Genuine bulk superconductivity under ambient pressure has remained elusive in these materials, precluding key measurements such as specific heat and superfluid density. In this work, we combine density-functional-theory, dynamical-mean-field-theory, and random-phase-approximation to solve the superconducting gap equation for bulk hole-doped bilayer nickelate La$_{3-x}$Sr$_x$Ni$_2$O$_7$ at ambient pressure. We find that hole doping induces a Ni-$d_{3z^2-r^2}$-derived $\gamma$ pocket on the Fermi surface, and serves as a tuning parameter for both its size and shape. As $x$ approaches 0.4, the $\gamma$ pocket evolves from circular to diamond-shaped and expands to span half of the Brillouin zone, resulting in nearly perfect Fermi surface nesting with the optimal nesting vector $\textbf{Q} = (\pi, \pi)$. This, in turn, strongly enhances antiferromagnetic spin fluctuations and substantially increases the leading superconducting eigenvalue to a level at which superconductivity becomes experimentally observable. Our work provides both a robust mechanism and an experimentally feasible route to inducing the long-sought bulk superconductivity in La$_3$Ni$_2$O$_7$ without pressure or strain.

We present a non-perturbative Floquet-based non-equilibrium Green's function (NEGF) method to study electron transport in a quantum system driven simultaneously by multiple independent terms (multi-mode). We first derive the two-mode Floquet NEGF based on two-step transformations of the retarded-advanced Green's function from the Kadanoff-Baym equation. This derivation proceeds by elaborating on the expectation values of the number and current operators. The two-mode Floquet NEGF is then extended to cases with multiple drivings. The method is tested by investigating current suppression in the presence of two drivings. We show that an extra sinusoidal off-diagonal driving can cause substantial modification to the current suppression, provided careful selection of the driving frequency. Consequently, we expect that the established method has broad applications in a wide range of open quantum systems driven by complicated drivings.

The PXP chain was recently shown to exhibit superdiffusive energy transport with Kardar-Parisi-Zhang-like scaling, $z\approx3/2$, joining a growing number of spin chains with this exponent. An understanding of how this anomalous hydrodynamics emerges from microscopics is, however, still lacking. In this work, we show that finite-temperature energy transport in this model provides a window into the emergence of superdiffusion. At finite temperature, the energy autocorrelation function exhibits a crossover from short-time coherent dynamics to long-time hydrodynamics. The short-time behavior is dominated by a single magnon band and can be understood analytically. In momentum space, this regime is characterized by spectral weight near $q=\pi$. The damping time $\tau$, which separates the short-time magnon-dominated behavior from the late-time hydrodynamics, grows rapidly upon cooling, consistent with an activated form $\tau(\beta)\sim \beta e^{\Delta\beta}$ with a gap scale set by the magnon band. At longer times, the spectral weight transfers to $q=0$ and the running decay exponent drifts toward the superdiffusive value $z=3/2$. Finite-temperature energy transport therefore provides a bridge between microscopic magnon physics and late-time superdiffusion in the PXP model.

Thermal cycle environments involving repeated temperature changes are common conditions observed in modern engineering processes. Under such conditions, materials undergo repeated thermal expansion and contraction, forming complex thermal stress fields. Thermal-crystal plasticity simulations that account for stress fields and thermal conduction at the polycrystalline microstructure scale are an effective method for numerically reproducing thermal cycle environments. However, the influence of thermal cycle frequency on the temporal behavior of the stress field and plastic response has not yet been fully understood, partly because a systematic analysis method capable of simultaneously capturing spatial heterogeneity and temporal evolution remains limited. In this study, we predicted the thermal stress field generated under different thermal cycle frequencies using thermal-crystal plasticity simulations and investigated the effect of frequency on the spatiotemporal structure of the stress response. The present framework illustrates that the resulting thermal-mechanical response can be represented as a superposition of multiple effective temporal components, reflecting the increased complexity of the system behavior. By employing dynamic mode decomposition (DMD) as a diagnostic and post-processing technique, we demonstrate that the spatiotemporal structure of the stress field under thermal cycle conditions can be systematically extracted and compactly represented. This approach enables a quantitative characterization of frequency-dependent changes in the thermal stress response beyond conventional averaging or snapshot-based analyses. The results highlight the utility of DMD as a framework for organizing complex simulation data and for interpreting the temporal structure of plastic response under cyclic thermal loading.

We formulate quantum geometry for non-Hermitian systems under open boundary conditions. By defining quantum-geometric quantities in both real-space and non-Bloch representations, we establish a unified framework beyond conventional Bloch band theory. Our central result is an exact equivalence between the real-space integrated quantum metric and a non-Bloch integrated quantum metric defined on the generalized Brillouin zone. We further introduce localized non-Bloch Wannier functions in the presence of the non-Hermitian skin effect and show that the non-Bloch integrated quantum metric gives the gauge-invariant part of their spread functional. These results establish quantum geometry as a natural framework for characterizing open-boundary non-Hermitian band structures and the localization properties encoded in skin modes.

The quantum geometry of Bloch wavefunctions underpins a wealth of emergent phenomena in quantum materials. Its imaginary part, the Berry curvature, has long been recognized as a key source for hallmark effects such as quantum Hall and topological phenomena, etc. The real part of quantum geometry, the quantum metric, has recently garnered considerable attention due to predictions of a range of unconventional nonlinear and nonequilibrium responses. Such responses usually vanish in centrosymmetric systems, largely restricting relevant studies to non-centrosymmetric materials. Here we challenge this convention by revealing that the vanished quantum metric response can survive in a hidden form. Using a non-local photovoltaic scheme in a layered magnetic semiconductor, we spatially separate mutually compensating photocurrents and thereby detect such hidden quantum metric response. We demonstrate this effect across distinct magnetic states and down to the ultrathin limit. Moreover, we realize reconfigurable, nonvolatile and probabilistic photodetection enabled by the quantum metric response. These results not only fundamentally expand the material landscape for quantum geometric physics, but also open new gateway to harvest the quantum geometric contributions for state-of-the-art nonvolatile reprogrammable sensing and computing applications.

Transconductance is a central figure of merit in field-effect transistors, typically governed by charge accumulation and carrier mobility. In multilayer WSe$_2$ transistors, however, it is shown to carry a nonlinear transport signature of inter-valley carrier redistribution between the $K$ and $\Gamma$ valleys. This valley-crossover contribution suppresses transconductance in bilayer WSe$_2$ and reverses sign in trilayer, while remaining absent in single-valley systems. Unlike extrinsic mechanisms such as trap-state filling or contact resistance, the anomaly leaves the subthreshold swing unchanged and cannot be reproduced within conventional single-valley transport models. Introducing the valley susceptibility $\chi_v \equiv \partial f_\Gamma/\partial V_{\rm GS}$, bounded by an intrinsic thermodynamic limit $(4k_BT)^{-1}$, we quantify this response and show that it reaches ${\sim}0.20\,\mathrm{V}^{-1}$ in bilayer WSe$_2$ near threshold at room temperature. The sign, magnitude, and temperature dependence of the anomaly provide directly measurable fingerprints of valley thermodynamics, establishing transconductance as an electrical probe of internal electronic degrees of freedom and revealing a previously hidden nonlinear response in standard transistor measurements.

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists.

Photocatalytic oxidation (PCO) is a promising strategy for indoor air purification and outdoor pollutant abatement, potentially offering treatment for climate- and health-relevant pollutants such as methane (CH$_4$), nitrogen oxides (NO$_\text{x}$) and volatile organic compounds (VOCs). In this work, we present experiments evaluating the PCO of CH$_4$ (2 to 10 ppm) under varying UV-C light intensities (4 to 59 W/m$^2$), using titanium dioxide (TiO$_2$) as the photocatalyst. At 2 ppm CH$_4$, TiO$_2$ achieves a maximum conversion efficiency of 24.4% and a maximum apparent quantum yield of $0.013$% over the tested UV-C light intensities, demonstrating activity at environmentally relevant concentrations. We develop a model to interpret the experimental results and assess the potential of PCO for ventilation applications. The model is validated against our CH$_4$ data and literature results for formaldehyde (HCHO) and NO$_\text{x}$. While laboratory-scale configurations achieve high conversions (e.g., 24.4% for CH$_4$), ventilation-scale performance is predicted to be limited by thin concentration boundary layers and short residence times, with conversion efficiencies dropping to around $0.017$%. Finally, we estimate the climate impact of CH$_4$ removal in terms of CO$_2$e emission rates, demonstrating that TiO$_2$-based PCO in ventilation applications can yield a net climate benefit (i.e., a net-negative CO$_2$e emissions rate) when the modelled CO$_2$e removal rate exceeds the emissions from catalyst material production and UV operation, particularly when pre-existing UV-C irradiation is leveraged.

Molecular spin qubits offer a versatile platform for quantum information processing due to their synthetic tunability and well-defined electronic structure. Here, a fitted-parameter-free computational framework combining density functional theory (DFT), time-dependent DFT (TD-DFT), and Redfield theory is applied to investigate the longitudinal spin-lattice relaxation time $T_1$ of the Eu nuclear spin qubit Eu(dpphen)(NO3)3. Using a single-molecule gas-phase model, the experimental long relaxation component $T_{1,\mathrm{long}} = 41.39$ s is reproduced within a factor of 1.4 (calculated: 55.88 s at 4.2 K), indicating that the slow relaxation channel is governed by intramolecular vibronic coupling. In contrast, the calculated $T_{1,\mathrm{short}}$ deviates by a factor of 66, highlighting the importance of crystal lattice and intermolecular effects absent from the model. The experimental $^5D_0 \rightarrow {}^7F_0$ optical transition is reproduced to within 1.1%, supporting the accuracy of the electronic structure description. Vibrational analysis identifies a large-amplitude dpphen rocking mode at a frequency of $332.02~\mathrm{cm}^{-1}$ as the dominant vibronic coupling channel, while electric field gradient (EFG) derivative analysis independently identifies another nitrate-rocking mode at $180.57~\mathrm{cm}^{-1}$ as the primary modulator of the nuclear spin environment via nitrate motion. These results are consistent with a near-maximal quadrupole asymmetry parameter $\eta = 0.941$, which creates state mixing through off-diagonal quadrupolar terms. Overall, the results establish a single-molecule relaxation baseline and suggest targeted ligand rigidification and substitution strategies to suppress decoherence.

Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conventional trial-and-error screening inefficient. We develop LEAP(LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model(LLM) with active learning for iterative additive prioritization. The LLM is trained to extract mechanism-relevant knowledge from the perovskite additive literature and to represent candidate molecules through interpretable descriptors, which are further integrated into a Bayesian optimization workflow for uncertainty-aware prioritization under low-data conditions. Benchmark results on unseen literature show that the domain-specialized model outperforms general-purpose models in mechanism-consistent reasoning. Experimental validation in an expert-in-the-loop proof-of-concept study suggests improved additive prioritization across three screening rounds, leading to average device PCEs of 20.13% and 20.87% for the later-round 6-CDQ- and 2-CNA-treated devices, respectively, compared with 19.25% for the control, with a champion PCE of 21.32%. These results provide preliminary evidence that literature-grounded mechanistic descriptors, when coupled with Bayesian optimization and expert feasibility review, can support mechanism-aware additive prioritization in perovskite photovoltaics.

The construction of models from data is a significant contributor to the energetic costs of computation. Because of this, understanding how foundational thermodynamic bounds apply to modeling algorithms will be increasingly important. Here, we study the thermodynamic costs of a basic and fundamental modeling algorithm: simple linear regression. Following Landauer, we approximate the thermodynamic lower bound on irreversibly performing both exact linear regression and linear regression via stochastic gradient descent as implemented on floating-point numbers. From this, we derive energycost aware scaling laws for the optimal dataset size for training a linear regression model given a generalization error dependent demand for inference. Additionally, we discuss a method to lower bound the entropy production from the mismatch cost for algorithms with continuous input variables.

Periodic driving provides a platform to dynamically tailor quantum states of matter, yet its impact on symmetry-protected topological phases remains incompletely understood. Here, we demonstrate that periodic driving enables the realization of an odd-parity altermagnetic (AM) higher-order topological insulator (HOTI) phase in the Cr$_2$CH$_2$ monolayer. In equilibrium, Cr$_2$CH$_2$ is a 2D antiferromagnetic (AFM) HOTI protected by $\mathcal C_3$ rotational symmetry, characterized by a symmetry indicator $\chi^{(3)}$ = $\{-2,1\}$ and robust corner states. Under circularly polarized light (CPL), the system develops a f-wave altermagnetic state governed by the symmetry $[C_{2}||\overline{3}_{001}]$ with odd-parity spin splitting. Despite substantial Floquet-induced band renormalization, the $\mathcal C_3$-protected corner states remain intact over a broad range of driving strengths, highlighting the altermagnetic higher-order topology under Floquet driving. As the light intensity increases, the system gradually evolves into an altermagnetic semimetallic state. These results establish a direct connection between magnetism and topology in a periodically driven AFM system, offering a route toward the control of coupled spin and topological transport.

We show that a nematic field constrained to a curved embedded surface develops an emergent geometric mass in its leading isotropic interaction sector. An auxiliary embedding-space closure mediated by the surface spin connection yields a massive scalar mode \(\chi_n\) with mass set by the extrinsic curvature invariant \(m^2=K_{ab}K^{ab}\). This mass arises directly from embedding geometry, promoting the intrinsic massless nematic interaction into a geometry-controlled massive field. The resulting theory identifies Gaussian curvature as a distributed geometric charge and establishes embedding geometry as the regulator of defect interactions on curved nematic membranes.

The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single neurons. In this work, we study the relationship between the statistical properties of the weights and the nonlinearity of the hidden unit in Restricted Boltzmann Machines (RBMs) on the one side, and the distribution induced on binary visible units. We do this for four commonly used activation functions: Linear, Step, ReLU, and Exponential, and make qualitative predictions about the ability of these models to learn distributions with strong higher order interactions over the visible nodes. We show that in general, in an ensemble of RBMs with Gaussian weights, these distributions are rare and hard to learn, except when the hidden unit activation function is an Exponential.

Supercooled water is expected to exhibit a liquid–liquid phase transition between low- and high-density liquid states, possibly terminating in a liquid–liquid critical point in the experimentally difficult no man's land. Because the hydrogen atoms are light, nuclear quantum effects (NQE) may alter the structural signatures used to identify this transition. Here, we compare classical molecular dynamics and path-integral molecular dynamics simulations of a flexible q-TIP4P/F-like water model in the deeply supercooled regime. The classical simulations show a pronounced density change at 180 K between 180 and 220 MPa, whereas the path-integral simulations exhibit a smoother pressure dependence. Radial distribution functions and bond-order parameters show that NQE broaden pair correlations, reduce the tetrahedral order of the first hydration shell, and slightly increase the Steinhardt $Q_6$ parameter. These results demonstrate that NQE modify both low- and high-density liquid structures and therefore need to be included when interpreting structural signatures of the liquid–liquid transition in supercooled water.

Altermagnetism is a recently identified class of magnetic order characterized by unconventional momentum-dependent spin splitting in the absence of net magnetization, and understanding its electronic and magnetic properties is essential for revealing its fundamental physics and potential applications. In this work we investigate two intercalated transition-metal dichalcogenides, Fe$_{1/4}$NbS$_2$ and V$_{1/3}$NbS$_2$, as candidate altermagnetic materials by using effective tight-binding and spin models complemented by first-principles calculations. We show that the $g$-wave electronic spin splitting originates from bond-dependent hopping anisotropy, leading to material-dependent nodal structures. For the magnetic excitations, the emergence of chiral splitting in the magnon dispersion is controlled by single-ion anisotropy, which manifests as altermagnetic-like nodal structures when spins are oriented along an easy-axis. Conversely, this altermagnetic signature disappears when the spins are aligned in an easy-plane. Beyond linear spin-wave theory, we find that $1/S$ corrections from magnon–magnon interactions preserve the symmetry and nodal structure of the band splitting while generally reducing its magnitude, with strong antiferromagnetic exchange leading to a non-negligible renormalization of the chiral splitting. Our findings establish intercalated transition-metal dichalcogenides as promising platforms for understanding the interplay between crystal symmetry, non-relativistic spin splitting, and magnetic properties in altermagnets.

We systematically discuss the equivalence of two variational formulations of magnetostatics, in terms of magnetization and magnetic field on the one hand and the single field formulation using only magnetic induction. To demonstrate that this link is stable also when the magnetic laws are coupled with other variational static models, elasticity is included in the models as well. Interestingly, despite the fact that the corresponding magnetoelastic energy densities in the material can be computed via Legendre-Fenchel transform in the magnetic state variables, the two formulations are not linked by standard convex duality on the level of the functionals. In addition, convexity and coercivity of the given functional are neither required for the transformation nor always preserved by it.

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how conventional and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate various computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.

Zero-field heat capacity of metallic helimagnet MnGe was analyzed based on the results of resistivity decomposition published previously by our group for the same crystal. Current procedure allowed identifying along with ($i$) electronic ($\tilde{\gamma}$ $\approx$ 7 mJ/mol$\cdot$K$^2$) and ($ii$) phononic ($\Theta_D$ $\approx$ 350 K) components ($iii$) the additional term, caused by the presence of spin fluctuations (SFs). The last contribution was found to exist in a wide range of temperatures in both paramagnetic (PM) and magnetically ordered states. However, its amplitude appears to be significantly lower in comparison with phononic component. The obtained value of spin fluctuation temperature $\theta_{sf}$(MnGe) $\approx$ 330 K correlates well with previous estimations, as well as with results of various experiments, which predict the existence of SFs in MnGe at least up to 250 $-$ 300 K.

Spectral fringes in Schwinger pair creation are usually attributed to structured driving, such as carrier oscillations, pulse trains, or multiple creation events. We show that pronounced fringes can arise even for smooth, carrier-free single-lobe electric-field pulses. Two bell-shaped profiles that are nearly indistinguishable in real time - a Gaussian pulse and a weakly deformed variant - produce qualitatively different longitudinal momentum spectra in the nonadiabatic crossover: the Gaussian spectrum remains smooth, whereas the deformed pulse develops strong fringes as the Keldysh parameter approaches unity. Exact numerical solutions in scalar and spinor QED agree with a semiclassical turning-point analysis and trace the effect to a turning-point dominance transition, where the leading saddle becomes irrelevant and subleading contributions interfere. We demonstrate the same mechanism in a solid-state Schwinger analog described by a gapped two-dimensional Dirac model relevant to epitaxial graphene on SiC, and discuss an energy-resolved pump-probe route to observing the predicted modulation.

We study two-banded, non-Hermitian random matrices inspired by sparse neural networks with a circular, 1d topology. We focus on two paradigmatic models, an SSH chain and a ladder model, which have both non-Hermitian directional bias and random sign disorder in the hoppings. The random sign disorder, which follows Dale's Law, leads to localization of the eigenstates, while the directional bias drives a delocalization transition in these states. The competition between disorder and directional bias results in rich eigenspectra with loops of extended states in the complex plane surrounded by regions of localized ones, and the eigenvalues are all confined to an annular region. Furthermore, the distinct band structures of the SSH chain and ladder model lead to different delocalization phenomena. Even in the absence of disorder, tuning the directional bias can lead to an eigenvalue degeneracy, which is an exceptional point for the SSH chain but a diabolic point for the ladder. In the presence of the disorder, these special eigenvalue degeneracies are preserved and also highlight key stages in the delocalization process. For both models, increasing the directional bias initially delocalizes states starting from within the bands. For the SSH chain, for large enough directional bias, the delocalized states open up a hole in the spectrum in the complex plane, similar to prior results for single band systems. But for the ladder model, as the directional bias is increased, the states delocalize in two stages, leading to two separate loops of extended states with localized states in between. The precise contours on which the extended states reside can be predicted from the Lyapunov exponents associated with products of random transfer matrices, in agreement with direct numerical diagonalization. Although we focus on periodic boundaries, results are discussed for open boundaries as well.

While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no "lookup table" that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our $\textit{information blueprint}$ algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for $\textit{E. coli}$ and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

We show that a non-interacting gas of charge-$e/3$ anyons with exchange statistics $\theta=-\pi/3$ can superconduct through a hidden weak-pairing mechanism. Such an anyon gas arises naturally in doped fractional Chern insulators at filling $1/3$ or $2/3$, where projective lattice translations enforce three degenerate anyon pockets. Exploiting this three-pocket structure, we develop a flux-attachment construction in which the average statistical flux vanishes, thereby mapping the problem to three species of composite fermions (CFs) in zero effective magnetic field. We show that the anyon statistics itself, encoded in statistical gauge field fluctuations, supplies the pairing glue and drives the CFs into a $p-\mathrm{i}p$ paired state, which corresponds to a $f-\mathrm{i}f$ physical superconductor. The CF strong-pairing phase is adiabatically connected to Laughlin's picture of anyon superconductivity, where charge-$e/3$ anyons bind into charge-$2e/3$ molecules, which then lead to superconductivity. By contrast, the more natural weak-pairing phase of CFs realizes a distinct superconducting phase - its edge is characterized by a chiral central charge $c_-=-1/2$, in contrast to the prediction of integer $c_-$ for the anyon superconductor based on Laughlin's picture, thereby resolving the discrepancy between previous theories and recent numerical results. Our theory provides a natural framework for understanding superconductivity near fractional Chern insulators, as observed in recent experiments. Finally, we discuss extensions of our theory that predict new chiral superconductors adjacent to FCIs at other fillings.

The chemical potential is defined as the work to quasi-statically add a particle to an equilibrium system. Inspired by this definition, we investigate how the work to add a particle to an active fluid depends on the activity, density, and insertion protocol. We find that the average work is protocol dependent and decreases with activity. Moreover, the work fluctuations retain asymmetric non-Gaussian tails even for slow particle insertions. We then compare the average particle-insertion work to the steady-state densities observed when two active fluids are brought into diffusive contact and observe opposing trends between density and work.

The ability to tune the Fermi level of semiconductors is at the heart of modern electronics. Here, we demonstrate that persistent photoconductivity (PPC) enables tuning of carrier density, conductivity type, and, consequently, the valley polarization in (Pb,Sn)Se/(Pb,Eu)Se quantum wells. Illumination of these samples induces Fermi level shifts that convert the system from a threefold-degenerate $\bar{M}$-valley two-dimensional hole gas to a single $\bar{\Gamma}$-valley-polarized electron gas with similar values of mobility. The optically induced state persists for more than $10^{3}$ minutes at cryogenic temperatures and enables stepwise optical gating without the need for device processing. These transitions are confirmed by the sign inversion of the Hall slope and the modification of quantum Hall plateau degeneracies measured in magnetic fields up to 35 T. Landau level $k\cdot p$ model calculations quantitatively reproduce the experimental data. Furthermore, studies of weak-field magnetoresistance demonstrate the significance of quantum localization phenomena at the transition between the weakly and strongly localized regimes in compensated narrow-gap semiconductors. Spectral studies allow us to identify the critical role of the barrier material and determine the photon energies that can reverse the PPC effect. The persistent light-induced upward shift of the Fermi level in the $p$-type quantum well is explained in terms of specific energy positions of donor and acceptor defect states in the studied system. Our results demonstrate that PPC is a powerful optical gating tool for the IV-VI quantum wells, a versatile platform for reconfigurable valleytronic architectures.

We present analytical results for first-passage processes in a deterministic one-dimensional cellular automaton (CA) model of traffic flow. Starting at time $t=0$ from a random initial state with car density p, at every time step $t\ge 1$ each car moves one step to the right if the cell on its right is empty, and is stopped if it is occupied by another car. The model, which coincides with CA rule 184 in Wolfram's numbering scheme, exhibits a continuous dynamical phase transition at $p=1/2$, between a low-density free-flowing phase and a high-density congested phase. Using the framework of first-passage processes, we derive a closed-form expression for the distribution $P(T_{FS}=t)$ of first-stopping (FS) times, which is the probability that a randomly selected car will be stopped for the first time at time $t$. We also obtain a closed-form expression for the stopping probability $P_S(t)$, which is the probability that a randomly selected car will be stopped at time $t$. In the low-density phase of $0<p<1/2$, the probability $P_S(t)$ yields a closed-form expression for the distribution $P(T_{LS}=t)$ of last-stopping (LS) times, which is the probability that a randomly selected car will be stopped for the last time at time $t$, beyond which it will move freely indefinitely. In this regime, we analyze the relation between the LS time and the number of stopping events $N_S$ which take place up to that time. We present closed-form expressions for the joint distribution $P(T_{LS}=t,N_S=n)$, for the two conditional distributions that emanate from it and for the marginal distribution $P(N_S=n)$. These results provide insight on the time scales of congestion and relaxation in deterministic traffic flow from the point of view of individual cars. In a broader context, they provide insight on complex relaxation processes that involve many interacting particles, such as deterministic surface growth.

We investigate the boundary phases of a (2+1)-dimensional quantum critical Heisenberg model with a dangling spin chain. By introducing a multispin $Q$-term along the boundary, we drive a continuous boundary transition from an antiferromagnetic (AF) order to a valence-bond solid (VBS) order. Using large-scale quantum Monte Carlo simulations, we locate the critical point at $Q_{c}=0.310(11)$, and obtain the critical exponents at $Q_{c}$, including $y_{s}=0.81(4)$ and the scaling dimensions of AF and VBS order parameters $\Delta_{s}=0.660(15)$ and $\Delta_{v}=0.204(14)$. The weak long-range AF order for $Q<Q_{c}$ is stabilized by quasi-long-range effective interactions mediated by the critical bulk state, while the VBS phase restores the ordinary critical behavior. Our findings highlight the synergy between topological terms and quasi-long-range interactions in low-dimensional quantum many-body systems.