Papers

Topological classifications of quantum critical systems have recently attracted growing interest, as they go beyond the traditional paradigms of condensed matter and statistical physics. However, such classifications remain largely unexplored in aperiodic environments, particularly under quasiperiodic modulations. In this Letter, we uncover a family of topological quasiperiodic fixed points with critical scalings intermediate between the clean and infinite-randomness limits. By exactly solving the quasiperiodic cluster-Ising chain, we demonstrate that all phase boundaries separating quasiperiodically modulated phases are governed by topological Ising-like fixed points unique to strongly modulated quasiperiodic systems. Although their local bulk critical properties match those of conventional quasiperiodic Ising critical points reported before, these fixed points host robust topological edge degeneracies and exhibit a distinct symmetry-enriched realization of the quasiperiodic Ising criticality, as further supported by complementary lattice simulations.

Artificial intelligence (AI) is rapidly emerging as a new paradigm of scientific discovery, namely data-driven science, across nearly all scientific disciplines. In materials science and engineering, AI has already begun to exert a transformative influence, making it both timely and necessary to examine its interaction with materials plasticity. In this study, we present a holistic survey of the convergence between AI and plasticity, highlighting state-of-the-art AI methodologies employed to discover, construct surrogate models for, and emulate the plastic behavior of materials. From a materials science perspective, we examine cause-and-effect relationships governing plastic deformation, including microstructural characterization and macroscopic responses described through plasticity constitutive models. From the perspective of AI methodology, we review a broad spectrum of applied approaches, ranging from frequentist techniques such as classical machine learning (ML), deep learning (DL), and physics-informed models to probabilistic frameworks that incorporate uncertainty quantification and generative AI methods. These data-driven approaches are discussed in the context of materials characterization and plasticity-related applications. The primary objective of this survey is to develop a comprehensive and well-organized taxonomy grounded in AI methodologies, with particular emphasis on distinguishing critical aspects of these techniques, including model architectures, data requirements, and predictive performance within the specific domain of materials plasticity. By doing so, this work aims to provide a clear road map for researchers and practitioners in the materials community, while offering deeper physical insight and intuition into the role of AI in advancing materials plasticity and characterization, an area of growing importance in the emerging AI-driven era.

Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suffer from limited generalizability. Here we present a robust ML framework for predicting the thermoelectric figure of merit (zT) in half-Heusler (hH) compounds, emphasizing prediction of previously unseen compositions. A principal component analysis (PCA)-based train/test splitting strategy is introduced to generate representative partitions of chemical space. Bayesian hyperparameter optimization is combined with correlation-based k-best feature selection for Random Forest, XGBoost, and Neural Network models, together with the interpretable SISSO symbolic-regression method. Relative to the full set of 118 input features, the optimized feature subsets reduce the test RMSE by 16-27%. SHAP and SISSO analyses identify temperature, A-site dopant concentration, and A-site heat of vaporization as the most important descriptors of zT. The framework is validated through prediction of previously unexplored compositions, recovery of literature-reported optimal zT values for established hH systems, and first-principles calculations for the new compounds CeCoBi and CeIrBi, which exhibit favorable electronic transport properties. The framework is further applied to high-throughput screening of the doped hH chemical space, covering about 2.46 million elemental substitution combinations across 631 thermodynamically screened parent matrices under stability constraints. This screening yields a concise set of promising candidates for future study. Overall, the work establishes a generalizable workflow for reliable data-driven thermoelectric materials discovery.

Motivated by bulk replica wormholes, we study the boundary effective theory that describes the near-horizon fluctuations of a near-extremal Reissner-Nordström black hole. This theory consists of a Schwarzian mode and a $U(1)$ phase mode. We compute the partition function of this boundary theory on connected geometries, from which the entropy is derived. Our analysis reveals a rich phase structure, in which the dominance of connected or disconnected configurations leads to a phase transition controlled by the temperature and the coupling constants $C$, $K$, and $\mathcal{E}$ of the 1d effective theory.

Electron gases in low dimensional materials exhibit unconventional transport and optical phenomena due to reduced phase space, enhanced interactions, and strong sensitivity to lattice symmetry. While commonly realized in quantum confined systems and engineered heterostructures, such states are rare in naturally occurring materials. Hyperbolic materials provide a compelling alternative, as extreme lattice anisotropy can host unconventional electronic states and novel electron-phonon interactions. Here, we investigate the angle resolved polarized Raman (ARPR) response of MoOCl2, the first naturally occurring hyperbolic material whose hyperbolicity originates from a highly anisotropic electron gas. We observe pronounced polarization dependent Fano line shapes, revealing coherent coupling between phonons and an anisotropic electronic continuum. We characterize the directional response of this continuum, incorporating it into effective Raman tensors that quantitatively reproduce the ARPR measurements and capture the distinct Raman fingerprint of MoOCl2. Excitation energy and thickness dependent ARPR measurements further demonstrate a tunable quasi 1D electronic continuum with weak interlayer coupling, establishing MoOCl2 as a model system for Raman studies of electron-phonon coupling in hyperbolic materials

Motile bacteria shape microbial function by occupying space, yet how collective motion becomes population-scale architecture remains poorly resolved. Bacterial swarming is not merely surface motion, but a process by which motile populations commit to future macroscopic form. Here, in Enterobacter sp. SM3, a gut-associated swarmer linked to mucosal repair, we treat the advancing colony–environment interface as a morphodynamic state through which local motility becomes spatial order. We built SwarmEvo across thermal, hydration, and substrate-mechanical conditions and developed Morpher to resolve and propagate interface states. Counterintuitively, within the permissive assay range, condition labels only weakly separated future trajectories, whereas colony-specific interface geometry constrained later expansion, indicating that swarm fate is written into the interface rather than prescribed by condition identity. Boundary fidelity was decisive: a 0.67 percentage-point segmentation gap expanded into a 2.4–3.1 IoU-point forecasting loss. Preserving front displacement, protrusion continuity, and branch memory, Morpher predicted late-stage expansion with 95.42% mIoU, 10.61 px HD$_{95}$, and 3.93 px ASSD. These results identify the advancing interface as a state-bearing layer through which motility and environmental constraint are converted into future spatial form, enabling disease-relevant microbial organization to be read before endpoint architecture emerges.

We provide a local probabilistic description of the limiting statistics of large preferential attachment trees in terms of the ordinary degree (number of neighbors) but augmented with information on leafdegree (number of neighbors that are leaves). The full description is the joint degree-leafdegree distribution $n_{k,\ell}$, which we derive from its associated multivariate generating function. From $n_{k,\ell}$ we obtain the leafdegree distribution, $m_{\ell}$, as well as the fraction of vertices that are protected (nonleaves with leafdegree zero) as a function of degree, $n_{k,0}$, among numerous other results. We also examine fluctuations and concentration of joint degree-leafdegree empirical counts $N_{k,\ell}$. Although our main findings pertain to the preferential attachment tree, the approach we present is highly generalizable and can characterize numerous existing models, in addition to facilitating the development of tractable new models. We further demonstrate the approach by analyzing $n_{k,\ell}$ in two other models: the random recursive tree, and a redirection-based model.

Acoustic metamaterials offer exceptional control over wave propagation, but their potential remains unfulfilled due to fabrication constraints. Conventional processes yield mostly rigid, planar structures, whereas soft-matter alternatives have so far been confined to ultrasounds. This work overcomes prior limitations with a fully soft 3D metamaterial operating around 200Hz. The design combines a 3D-printed elastomer lattice with resonant inclusions of liquid metal, injected via a network of mesofluidic channels. Its dynamic response is derived from a hybrid strategy uniting a lumped-element model with finite element analysis. Simulations reveal how the dual-phase design decouples flexural and torsional modes, opening a subwavelength band gap for low-frequency elastic waves. Empirical validation is achieved via a custom camera-based vibrometer. Its high spatiotemporal resolution and full-field capabilities enable direct capture of local modes and evanescent waves underlying the band gap. Accelerometer data corroborate these findings and demonstrate greater attenuation than common silicone elastomers at only half of the density. By combining scalable fabrication, compliance, and operations at frequencies relevant to human tactile perception, this novel metamaterial paves the way for lightweight, high-performance cushioning and handles that protect users from harmful vibration exposure.

We derive a universal relation for the critical temperatures of the $q$-state Potts model based on the counting of domain-wall microstates. By balancing interface energy against configurational entropy, we show that the critical temperature is determined by the ratio of the coordination-dependent energy cost to the logarithm of a total multiplicity factor. This factor decomposes into a lattice-topological constant, representing a projection from an underlying orthogonal Euclidean space, and a term representing Markovian sampling in the $q$-dimensional state space. The framework recovers exact solutions for two-dimensional square, triangular, and honeycomb lattices and achieves sub-3% accuracy for three-dimensional simple cubic, bcc, fcc, and diamond geometries. This approach unifies the Potts universality class into a single geometric classification, revealing that the phase transition is governed by the saturation of interface propagation through the lattice manifold and providing a predictive tool that characterizes the entire $q$-state family from a single topological calibration.

We estimate the plateau moduli of highly entangled end-pinned bead-spring polymer melts with Z = 100 and Z = 200 from the time-dependent elastic response to a step strain, which we first extrapolate to infinite time and then interpolate to zero strain. We present data for systems deformed in the melt state as well as for systems deformed at the primitive path level following the recent iPPA protocol. We observe excellent agreement between the plateau moduli obtained via the two deformation protocols and good agreement with the available experimental data for commodity polymer melts using a common mapping on the Kuhn scale.

Atomistic simulations provide valuable insights into the physical processes governing material behavior. However, their applicability is fundamentally constrained by the limited time scales accessible to brute-force simulations. This bottleneck often stems from complex energy landscapes where the systems stay trapped in metastable states for long periods of time. Yet, the long-term evolution is controlled by the transitions between the metastable states, which are rare events and difficult to observe. We present an importance sampling method designed to accelerate the time scale of Markov chain Monte Carlo (MCMC) simulations. By employing a bias potential, our approach enhances the sampling of rare transition events while preserving the relative probabilities of distinct transition pathways. The bias potential is represented by a neural network which enables the flexibility needed for high-dimensional systems. We propose a rigorous formulation to obtain the original transition rates between metastable states using transition paths obtained from the biased simulation. We further use a branching random walk (BRW) technique to enhance efficiency and to reduce variance. The proposed methodology is validated on 2-dimensional and 14-dimensional systems, demonstrating its accuracy and scalability.

Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely unknown. Probabilistic context-free grammars (PCFGs) provide a tractable testbed for studying these questions. However, prior work has focused either on the post-hoc characterization of the parsing-like algorithms used by trained networks; or on the learnability of PCFGs with fixed syntax, where parsing is unnecessary. Here, we (i) introduce a tunable class of PCFGs in which both the degree of ambiguity and the correlation structure across scales can be controlled; (ii) provide a learning mechanism – an inference algorithm inspired by the structure of deep convolutional networks – that links learnability and sample complexity to specific language statistics; and (iii) validate our predictions empirically across deep convolutional and transformer-based architectures. Overall, we propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.

I present the results from a spatial model of the prisoner's dilemma, played on a toroidal lattice. Each individual has a default strategy of either cooperating ($C$) or defecting ($D$). Two strategies were tested, including “tit-for-tat” (TFT), in which individuals play their opponent's last play, or simply playing their default play. Each individual also has a probability of telling the truth ($0 \leq P_{truth} \leq 1$) about their last play. This parameter, which can evolve over time, allows individuals to be, for instance, a defector but present as a cooperator regarding their last play. This leads to interesting dynamics where mixed populations of defectors and cooperators with $P_{truth} \geq 0.75$ move toward populations of truth-telling cooperators. Likewise, mixed populations with $P_{truth} < 0.7$ become populations of lying defectors. Both such populations are stable because they each have higher average scores than populations with intermediate values of $P_{truth}$. Applications of this model are discussed with regards to both humans and animals.

Recently, numerical examples of stable soap bubble clusters with multiple torus bubbles have been presented. The geometry of these clusters is based on the Platonic solids whose vertices have valence $3$ (in order to fulfill Plateau's laws): the tetrahedron, the cube, the dodecahedron. The clusters respectively contain a bubble of genus $3, 5, 11$. The construction is quite generic and can be used with any convex polyhedron. If stable, the cluster obtained using a polyhedron with $n$ faces has $3n+2$ bubbles and one of these bubbles has genus $n-1$. We propose here to show that is it possible to get stable soap bubble clusters with multiple torus bubbles using a geometry based on prisms and Archimedean solids as well.

H$_3$S sulfur hydride has been widely investigated for its high superconducting critical temperature $T_c$ of 203 K at about $p_c = 155$ GPa. Despite being the precursor of superconducting hydrides, a detailed picture of its structural phase diagram in an extended temperature and pressure range is still missing. To determine it with inclusion of both thermal and quantum effects, we carry out path integral molecular dynamics combined to a MACE neural network potential trained on BLYP density functional theory configurations. The resulting H$_3$S phase diagram is characterized by the displacive transition between the centrosymmetric Im$\bar{3}$m and polar R3m phases, which originates from a quantum critical point (QCP) located at $p_\mathrm{QCP} \approx 134$ GPa. We show that the experimental $T_c$ peak falls into a centrosymmetric region of large nuclear quantum fluctuations above the displacive QCP, as measured by local phonon Green's functions resolved in imaginary time, where fluctuating moments are at play. We study the critical behavior of the system in the proximity of the QCP by a finite-size scaling analysis, showing that it belongs to the 4D Ising universality class. We finally discuss its implications for the superconducting state.

Optical resonances in nanostructures enable strong enhancement of nonlinear processes at the nanoscale, such as second-harmonic generation (SHG), with high-$Q$ modes providing intensified light–matter interactions and sharp spectral selectivity for applications in filtering, sensing, and nonlinear spectroscopy. Thanks to the recent advances in thin-film lithium niobate (TFLN) technology, these key features can be now translated to lithium niobate for realizing novel nanoscale nonlinear optical platforms. Here, we demonstrate a large-area metasurface, realized by scalable nanoimprint lithography, comprising a slanted titanium dioxide (TiO$_2$) nanograting on etchless TFLN for efficient narrowband SHG. This is enabled by the optimal coupling of quasi-bound state in the continuum (q-BIC) modes with a narrowband pulsed laser pump. The demonstrated normalized SHG efficiency is $0.15\%\,\mathrm{cm}^2/\mathrm{GW}$, which is among the largest reported for LN metasurfaces. The low pump peak intensity ($3.64~\mathrm{kW}/\mathrm{cm}^2$) employed, which enables SHG even by continuous-wave pumping, allows envisioning integrated and portable photonic applications. SHG wavelength tuning from $870$ to $920~\mathrm{nm}$ with stable output power as well as polarization control is also achieved by off-normal pump illumination. This versatile platform opens new opportunities for sensing, THz generation and detection, and ultrafast electro-optic modulation of nonlinear optical signals.

Disorder in photonic crystals and waveguides creates states inside the photonic band gap. These states are often described as Lifshitz tails despite exhibiting energy distributions inconsistent with Lifshitz statistics near the band edge. Here we show that in photonic-crystal waveguides with intentionally engineered anisotropic disorder, the band-edge tail accessible experimentally follows an Urbach law universally, with cumulative statistics $F(\Delta)=\exp[-(\Delta/\alpha)^\beta]$, where $\Delta$ is the spectral detuning from the band edge, and an exponent $\beta \approx 1$ independent of disorder strength and orientation. In contrast to Lifshitz behavior, the density of states is maximal at the band edge and decays into the gap. Crucially, we find that the Urbach energy $\alpha$ is anisotropic, with a pronounced directional splitting and qualitatively different scaling for disorder parallel and perpendicular to the waveguide axis. These conclusions are supported by quantitative agreement between optical measurements of GaAs photonic-crystal waveguides and full-vector simulations. The anisotropic Urbach energy emerges as a sensitive probe of disorder-mode coupling and a practical metric to characterize structural disorder in photonic devices.

Nanofluidic systems exploit nanometre-scale confinement in channels and pores to regulate ionic transport, enabling functionalities such as osmotic energy harvesting and neuromorphic ionic memory. Studying such confined transport requires both precise electrical instrumentation and careful data analysis, yet, in practice, measurements are still often taken with vendor software, exported as files, and processed later in separate environments. In this work, we bring these steps together in a unified Python-based framework built around three interoperable graphical user interfaces (GUIs) for nanochannel, nanopore and memristor experiments. The framework is organised into two functional parts, measurement and analysis. On the measurement side, two GUIs drive Keithley Source Meters to run continuous voltage sweeps and user-defined memristive pulse sequences, while providing live plots, configuration management and controlled shutdown routines. On the analysis side, a dedicated nanochannel and nanopore GUI reads raw I-V datasets, applies unit-consistent processing, extracts conductance and ion mobility, evaluates selectivity and osmotic power, and is complemented by a web-based calculator that performs the same mobility analysis without a local Python installation. All three GUIs are implemented in Python/Tkinter with modular plotting and logging layers so that flexible control sequences and physics-based post-processing share a common data format, improving reproducibility, timing stability and day-to-day efficiency in nanofluidic and electronic device studies.

In standard treatments of electron transport, momentum relaxation in a perfect, defect-free crystal is linked with phonon creation or annihilation. In this work, we reconsider this problem for a finite, isolated crystal, retaining the lattice center-of-mass (recoil) degree of freedom and enforcing conservation of total mechanical momentum together with discrete crystal pseudomomentum. Starting from the density-density form of the electron-lattice interaction, we show that an electron in the interior of a perfect crystal admits elastic momentum-transfer channels in which total momentum is conserved by recoil of the lattice background without phonon excitation. These elastic channels can provide the leading contribution to momentum relaxation. We further identify mixed quasi-elastic and superelastic processes in which phonon occupations change but do not account entirely for the electron's momentum transfer. The elastic channels arise within the standard microscopic Hamiltonian and do not require additional disorder or defects. The resulting framework provides a complementary microscopic perspective on momentum relaxation in clean crystals and is consistent with experimental phenomena such as weak localization, quantum oscillations, ultrasonic attenuation, and the observed separation of momentum and energy relaxation times.

Phase transitions occur when a macroscopic number of local degrees of freedom coherently change their behavior. In ground states of quantum many-body systems, phase transitions due to quantum fluctuations are observed as non-analytic behaviors of order parameters, such as magnetization, as functions of a conjugate parameter, such as the magnetic field. However, as soon as thermal fluctuations are present, these effects are believed to disappear for local observables. We show that this is not necessarily the case: order parameters may still show non-analytic behaviors within their dynamics. With the example of the Ising model and using methods based on hydrodynamic fluctuations, we evaluate the exact order-parameter correlation time, in space-time directions of all velocities, in equilibrium states at nonzero temperature. We reveal non-analytic behaviors of spin correlation times as functions of the magnetic field, velocity, and temperature. As a function of the magnetic field, they occur at values that continuously approach that of the zero-temperature equilibrium transition point as the velocity is decreased and reach it within the light cone, where we obtain a new, temperature-independent logarithmic divergence characterizing the collective dynamics. Thus, collective effects induced by quantum fluctuations persist within the dynamics of local observables.

Anti-Heusler alloys, being a new addition to the Heusler alloys family, exhibit atomic disorders, and almost all of them are reported as a re-entrant spin-glass system. Although such spin-glass feature is generally attributed to the inherent atomic disorder, a comprehensive and extensive investigation on the individual roles of different types of disorders in magnetic interactions remains lacking for any of the reported anti-Heusler systems. As an illustrative case, we have carried out an in-depth experimental as well as theoretical investigation of structural, magnetic, and transport properties of a polycrystalline anti-Heusler alloy, Al$_2$MnFe. While the major atomic disorder is found to be among Fe and Mn atoms, which are randomly distributed among the two octahedral sites, 4$a$ and 4$b$ (B2-type disorder), a relatively small fraction ($\sim$12%) of Mn atoms also replace Al atoms at the tetrahedral 8$c$ site. Magnetically, the system undergoes two transitions: a paramagnetic to a ferromagnetic transition at $T_{\rm C}\sim$113~K, followed by a spin-glass phase transition below $T_{\rm f}\sim$20~K. Here, the magnetic moment is primarily confined to Mn atoms. Very interestingly, our theoretical analysis reveals that the ferromagnetic spin arrangement remains rather robust in spite of the 50% disorder of moment-carrying Mn atoms between the two octahedral sites, but a much smaller ($\sim$12%) cross-distribution of Mn atoms between octahedral and tetrahedral sites are sufficient to impose a reentrant spin-glass state at low temperature. Our analysis brings forth the importance of understanding the role of individual types of swap-disorder on magnetic properties in the anti-Heusler family of materials.

Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles techniques. To address this, recent research has created experimental databases from information extracted from scientific literature. However, most existing experimental databases do not provide full atomic coordinate information, which prevents them from supporting advanced ML architectures such as Graph Neural Networks (GNNs). In this work, we propose to bridge this gap through an alignment process between experimental databases and Crystallographic Information Files (CIF) from the Inorganic Crystal Structure Database (ICSD). Our approach enables the creation of a database that can fully leverage state-of-the-art model architectures for material property prediction. It also opens the door to utilizing transfer learning to improve prediction accuracy. To validate our approach, we align NEMAD with the ICSD and compare models trained on the resulting database to those trained on NEMAD originally. We demonstrate significant improvements in both Mean Absolute Error (MAE) and Correct Classification Rate (CCR) in predicting the ordering temperatures and magnetic ground states of magnetic materials, respectively.

Energy Absorption Interferometry (EAI) is a technique for measuring the responsivities and complex-valued spatial polarimetric forms of the individual degrees of freedom through which a many-body system can absorb energy. It was originally formulated using the language of quantum correlation functions, making it applicable to different kinds of excitation (electromagnetic, elastic and acoustic fields). EAI has been applied in a variety of theoretical and experimental ways. It is particularly effective at characterising the multimode behaviour of ultra-low-noise far-infrared and optical devices, imaging arrays, and complete instruments, where it can be used to ensure that a system is maximally responsive to those partially coherent fields that carry signal whilst avoiding those that only carry noise. Despite its utility there is no comprehensive overview of electromagnetic EAI. In this paper we describe the theoretical foundations of the method, and present a range of new techniques in areas relating to sampling, phase referencing, mode reconstruction and noise. We present, for the first time, an analysis of how noise propagates through an experiment resulting in errors and artefacts on spectral and modal plots. A noise model is essential, because it determines the signal to noise ratio needed to ensure a given level of experimental fidelity.

Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. Using tools from statistical physics, we analyze the emergence of generative distortions induced by CFG, namely the mismatch between the CFG sampling distribution and the true conditional distribution. We study this phenomenon in analytically tractable settings with exact score functions, characterizing its dependence on data dimensionality and the number of classes. For high-dimensional Gaussian mixtures, we use dynamic mean-field theory to show that distortions persist when the number of classes scales exponentially with the data dimension, whereas they vanish in the sub-exponential regime due to a dynamical phase transition. Moreover we found that, in the infinite-class limit, distortions remain unavoidable regardless of dimensionality because of the increasing density of classes. Finally, we show that standard CFG schedules cannot prevent variance shrinkage, and we propose a theoretically grounded guidance schedule incorporating a negative-guidance window that improves both class separability and sample diversity in real-world latent diffusion models.

This dissertation explores various nonlinear responses that arise from the rich interplay between quantum geometry, disorder, magnetism and topology in quantum materials. In addition to presenting generalizations of quantum kinetic theory, Kubo formulas and semiclassical Boltzmann transport theory to the nonlinear response regime, we discuss several predictions of novel transport effects and physical insights that emerge from these developments.

Van Hove singularities (VHSs) amplify electronic correlations, providing a crucial platform for discovering novel quantum phase transitions. Here, we show that VHSs in multi-orbital systems can stabilize a variety of competing $\bm{Q}=0$ magnetic orders, including intrinsic altermagnetism emerging from spontaneous orbital antiferromagnetism. This intrinsic phase, in which antiparallel spins reside on distinct orbitals, is realized across all four 2D Bravais lattices. It is driven by orbital-resolved spin fluctuations enhanced by inter-orbital hopping and favors suppressed Hund's coupling $J_H$, strong inter-orbital hybridization, and filling near a VHS from quadratic band touching. Through Hubbard-$U$-$J_H$ phase diagrams we map several magnetic phase transitions: (i) ferrimagnet to $d$-wave extrinsic altermagnet, (ii) $d$-wave intrinsic altermagnet to ferromagnet, and (iii) $g$-wave extrinsic altermagnet to either $d$-wave extrinsic altermagnet or ferromagnet. Our work identifies VHSs as a generic route to altermagnetism in correlated materials.

Collective electronic excitations, including plasmons, excitons, and intra- and interband transitions, play a central role in determining the dynamic screening, optical response, and energy transport properties of materials. Recent advances in momentum- and frequency-resolved spectroscopies, such as electron energy-loss spectroscopy (EELS) and inelastic x-ray scattering (IXS), together with progress in first-principles many-body perturbation theory (MBPT) calculations, now allow collective excitations to be mapped with considerable precision across the Brillouin zone. This topical review surveys current developments in the representation and interpretation of both experimental and theoretical dielectric-response spectra. Particular emphasis is placed on recent ways of representing spectral band structures (SBS) of the direct and inverse dielectric functions, such as analytical approaches based on multipole-Padé approximants in momentum and frequency (MPA($\q$)), which provide a combined band-like description of the dispersion of the main collective excitations. We discuss how features observed in metals, semiconductors, and low dimensional systems reflect the interplay between electronic structure, screening strength, and local-field effects, and how post-processing procedures can improve the quantitative comparison between experiment and theory. Finally, we provide perspectives on open challenges and potential developments in quantitative dielectric-function analyses.

In this article, we study the two-mode method to analyze the Josephson oscillation for a trapped binary Bose-Einstein condensate while taking into account the beyond mean-field and three body interactions. For this purpose, we use the archetypal model of double well potential and study the Josephson oscillation and self-trapping phases in quasi-one dimension. Additionally, our analysis provides quantitative discussion on the effect of asymmetry and dimension. We further corroborate our findings with Bogoliubov quasi-particle method and notice the existence of roton-like mode.

Dirac exceptional points (EPs) represent a novel class of non-Hermitian singularities that, unlike conventional EPs, reside entirely within the parity-time unbroken phase and exhibit linear energy dispersion. Here, we theoretically investigate the quantum geometry of Dirac EPs realized in nitrogen-vacancy centers in diamond, utilizing fidelity susceptibility as a probe. We demonstrate that despite the absence of a symmetry-breaking phase transition, the Dirac EP induces a pronounced geometric singularity, confirming the validity of the fidelity in characterizing non-Hermitian EPs. Specifically, the real part of the fidelity susceptibility diverges to negative infinity, which serves as a signature of non-Hermitian criticality. Crucially, however, we reveal that this divergence exhibits a distinct anisotropy, diverging along the non-reciprocal coupling direction while remaining finite along the detuning axis. Furthermore, we establish that this anisotropy, characterized by at least one exact dark direction coexisting with divergent directions, is a generic consequence of the Dirac EP structure whenever the parameter derivatives collectively span the off-diagonal operator space at the Dirac EP. This behavior stands in stark contrast to the omnidirectional divergence observed in conventional EPs. Our findings provide a comprehensive picture of the fidelity probe near the Dirac EP, highlighting the critical role of parameter directionality in exploiting Dirac EPs for quantum control and sensing applications.

We investigate the transport properties of a triangular triple quantum dot (TTQD) system connected with two reservoirs under linear response regime. By employing the hierarchical equations of motion(HEOM), we compute the thermopower and thermoelectric figure of merit. The impact of interaction scheme among three quantum dots on the thermopower is thoroughly analyzed, while the thermoelectric current and spectral function throughout this process are also elaborated. Our results reveal that, under low-temperature conditions, the alleviation of the geometric blockade in the TTQD system leads to a significantly faster enhancement of the heat current compared to the electric current. This phenomenon consequently elevates the thermopower, resulting in a remarkably high thermoelectric figure of merit.