Skyrmion Spin Ice in Liquid Crystals
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We consider mid-spectrum eigenstates of the Sachdev-Ye-Kiteav (SYK) model. We prove that for subsystems whose size is a constant fraction of the system size, the entanglement entropy deviates from the maximum entropy by at least a positive constant. This result highlights the difference between the entanglement entropy of mid-spectrum eigenstates of the SYK model and that of random states.
Highlights: • Provides an overview of the current understanding of the many-body localization phase transition. • Discusses the subtleties of finite-size scaling near this transition • Assesses the implications of these subtleties for numerical studies of spin chains. • Explores scaling of diagnostics in models with known localization transitions. • Presents suggestions for future numerical work. We re-examine attempts to study the many-body localization transition using measures that are physically natural on the ergodic/quantum chaotic regime of the phase diagram. Using simple scaling arguments and an analysis of various models for which rigorous results are available, we find that these measures can be particularly adversely affected by the strong finite-size effects observed in nearly all numerical studies of many-body localization. This severely impacts their utility in probing the transition and the localized phase. In light of this analysis, we discuss a recent study (Šuntajs et al., 2020) of the behaviour of the Thouless energy and level repulsion in disordered spin chains, and its implications for the question of whether MBL is a true phase of matter.
In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.
The quantitative analysis of local ordering principles in disordered crystalline systems has gained much attention over the past few years, as it is often considered crucial for optimizing material functionality. This development has been driven by significant advancements in computational and experimental methods, which have led to the establishment and widespread use of various analytical techniques. In this study, we perform model calculations to compare the effectiveness of atomic resolution holography and three-dimensional difference pair distribution function analysis (3D-ΔPDF). Using Cu 3 Au as a model system, we demonstrate an approach to derive local order parameters quantitatively and show that both techniques are well suited to quantifying chemical short-range order correlations and local bond-distance variations. By evaluating the strengths and limitations of both techniques, we advocate for their combined use to solve complex short-range order problems accurately.
Hyperuniform many-body systems in d-dimensional Euclidean space R d are characterized by completely suppressed (normalized) infinite-wavelength density fluctuations, and appear to be endowed with novel exotic physical properties. Recently, hyperuniform systems of disordered varieties have been observed in the context of various atomic-scale two-dimensional (2D) materials. Here, we analyze the effects of localized defects on the density fluctuations across length scales and on the hyperuniformity property of experimental samples of two-dimensional transition metal dichalcogenides. In particular, we extract atomic coordinates from time series annular dark field-scanning transmission electron microscopy (ADF-STEM) imaging data of 2D tungsten chalcogenides with the 2H structure (Te-doped 2H-WSe 2 ) showing continuous development and evolution of electron-beam induced defects, and construct the corresponding chemical-bonding informed coordination networks between the atoms. We then compute a variety of pair statistics and bond-orientational statistics to characterize the samples. At low defect concentrations, the corresponding materials are nearly hyperuniform, characterized by significantly suppressed scattering at the zero wave-number limit (omitting forward/ballistic scattering). As more defects are introduced, the (approximate) hyperuniformity of the materials is gradually destroyed, and the system becomes non-hyperuniform even when the material still contains a significant amount of crystalline regions. At high defect concentrations, the structures become antihyperuniform with diverging (normalized) large-scale density fluctuations, mimicking those typically observed at the thermal critical points associated with phase transitions. Overall, the defected materials possess varying degrees of orientation order, and there is apparently no intermediate hexatic phase emerging. To understand the observed nearly hyperuniform density fluctuations in the slightly defected materials, we construct a minimalist structural model and demonstrate that the experimental samples can be essentially viewed as perturbed honeycomb crystals with small correlated displacements and double chalcogen vacancies. Moreover, the small correlated displacements alone can significantly degrade hyperuniformity of the perfect honeycomb structure. Therefore, even a small amount of vacancies, when coupled with correlated displacements, can completely destroy hyperuniformity of the system.
Recent magnetotransport measurements in low-disorder electron systems confined to GaAs/AlGaAs samples reveal that the v = 1 integer quantum Hall plateau is broken into three distinct regions. These three regions are associated with two phases with different types of bulk localization: the Anderson insulator is due to random quasiparticle localization, and the integer quantum Hall Wigner solid is due to pinning of a stiff quasiparticle lattice. Universal properties of the v = 1 plateau are highlighted: the structure of the stability diagram, the nonmonotonic dependence of the activation energy on the filling factor, and the alignment of features of the activation energy with features of the stability regions of the different phases are found to be similar in three samples spanning a wide range of electron densities. Quantitative differences between the samples are also discussed, such as the dependence of the onset temperature and the activation energy of the integer quantum Hall Wigner solid on the electron density. The findings provide insights into the localization behavior along the v = 1 integer quantum Hall plateau in the low disorder regime.
Localized bases play an important role in understanding electronic structure. In periodic insulators, a natural choice of localized basis is given by the Wannier functions which depend on a choice of unitary transform known as a gauge transformation. Over the past few decades, there have been many works that have focused on optimizing the choice of the gauge so that the corresponding Wannier functions are maximally localized or reflect some symmetry of the underlying system. In this work, we consider fully nonperiodic materials where the usual Wannier functions are not well defined and gauge optimization is impractical. To tackle the problem of calculating exponentially localized generalized Wannier functions in both periodic and nonperiodic systems, we discuss the ‘iterated projected position (IPP)” algorithm. Here, the IPP algorithm is based on matrix diagonalization and therefore unlike optimization-based approaches, it does not require initialization and cannot get stuck at a local minimum. Furthermore, the IPP algorithm is guaranteed by a rigorous analysis to produce exponentially localized functions under certain mild assumptions. We numerically demonstrate that the IPP algorithm can be used to calculate exponentially localized bases for the Haldane model, the Kane-Mele model (in both Z 2 invariant even and Z 2 invariant odd phases), and the p x + ip y model on a quasicrystal lattice.
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.
Here we report local structure measurements for CsPbBr 3 and ((NH 2 ) 2 CH)PbBr 3 (FAPbBr 3 ) and compare them with recent results for (CH 3 NH 3 )PbBr 3 (MAPbBr 3 ). The Pb-Br bonding is similar for all three systems; the effective spring constants, κ, are comparable (ranging from 1.20 to 1.95 eV/Å 2 ), but small in magnitude indicating very soft materials. However, there are also important differences between the three systems. Static disorder is very small for CsPbBr3 but increases somewhat with the size of the organic molecular ions MA + and FA + . At room temperature, dynamic disorder dominates in all compounds. The thermal disorder of the Pb-Br pair distribution function (PDF), i.e., the Debye-Waller factor σ 2 follows a correlated Debye or Einstein model up to 300 K in CsPbBr 3 (orthorhombic phase), but for FAPbBr 3 and MAPbBr 3 , there is a break in the σ 2 (T) curve at the orthorhombic-tetragonal transition (o-t) near 150 K, indicating a small change in the spring constant κ. κ increases for MAPbBr 3 but decreases in FAPbBr 3 at this transition. These changes are attributed to changes in the H-bonding between Br - and MA + or FA + at this transition, as a result of librations or rotations of these molecular cations. In addition, the Pb-Br PDF becomes asymmetric at a relatively low temperature for FAPbBr 3 and MAPbBr 3 , while this effect is significantly smaller for CsPbBr 3 . Finally, we address the question of a model to explain the asymmetric PDF. Two main models are discussed in the literature, an anharmonic pair potential and a split-pair distribution, possibly driven by the presence of a lone pair on the Pb ion. We show that the fourth cumulant C 4 can differentiate between these two models and other possible models. Experimentally C 4 is positive at 250 K and above, for all three systems and that is inconsistent with a split-peak model, for which C 4 is negative for splittings larger than 0.12 Å.
Here, the memory effect in a single crystal spin glass (Cu 0.92 Mn 0.08 ) has been measured using 1 Hz ac susceptibility measurements over a reduced temperature range of 0.4 - 0.7 T g and a model of the memory effect has been developed. A double-waiting-time protocol is carried out where the spin glass is first allowed to age at a temperature below T g , T w$_{1}$ , followed by a second aging 4 K lower, T w$_{2}$ . The 4 K separation is sufficient to ensure rejuvenation has occurred. The model is based on calculating overlaps between the growth of the correlation lengths at the two temperatures. It accounts for the absolute magnitude of the memory effect as a function of both waiting times and temperatures. The data can be explained by the memory loss being a function of the relative change in the correlated volume at the first waiting temperature due to growth in the correlations at the second waiting temperature.
Here, the unexpected physical phenomena resulting from the seemingly inconsequential substitutions of chemically similar lanthanide elements in the Pr 1-x Gd x ScGe system are exploited to further the understanding of rare-earth magnetism and inform materials design. By directly probing magnetic moments of crystallographically indistinguishable Pr and Gd we solve the puzzles of how an unusual magnetic memory and strong exchange bias emerge at specific, easily predictable chemistries. Both effects are rooted in a robust antiparallel arrangement of large 4f magnetic moments of light and heavy lanthanides. This enables precise control of nearly zero net magnetization either opposed to, or aligned with, the external magnetic field that persists over a wide range of temperatures and fields. Further, spontaneous perturbations in the random distribution of lanthanide ions makes strong exchange bias possible in bulk single-phase compounds bordering magnetic compensation, consequently expanding the materials base beyond artificial magnetic multilayers and broadening the range of potential applications of the phenomenon.
Here, we study the honeycomb lattice with a single magnetic impurity modeled by adding imaginary next-nearest-neighbor hopping 𝑖ℎ on a single hexagon. This Haldane defect gives a topological mass term to the gapless Dirac cones and generates chirality. For a small density of defects, Neehus et al. [Phys. Rev. Lett. 135, 126604 (2025)] found that the system's chirality reverses at a critical ℎ 𝑐 ≈ 0.95 associated with an unexpected tricritical point of Dirac fermions at zero defect density. We investigate this zero-density limit by analyzing a single defect and computing two experimentally relevant measures of chirality: (1) orbital magnetization via local Chern marker, a bulk probe of all occupied states; and (2) electronic currents of low-energy states. Both probes show a chirality reversal at a critical ℎ 𝑐 ≈ 0.9–1.0. Motivated by this consistency, we propose a defect-scale toy model whose low-energy states reverse their chirality at ℎ$^{'}_{c}$ ≈ 0.87. Remarkably, the same pair of zero-energy bound states also generates the critical point ℎ 𝑐 in the full impurity projected T-matrix. Our results show how the chirality reversal produced by an impurity can be observed either in local probes or in the global topology, and suggest a possible role of the microscopic defect structure at the critical point.
Highlights: • Anderson transition from ergodicity to localization on random regular graphs (RRG). • Analytical, pool method, and exact-diagonalization study of correlations on RRG. • Many-body localization (MBL) to ergodicity transition: quantum dots and spin chains. • Dynamical eigenstate correlation functions in RRG and MBL problems. • Anderson localization on RRG as a toy model for MBL. The article reviews the physics of Anderson localization on random regular graphs (RRG) and its connections to many-body localization (MBL) in disordered interacting systems. Properties of eigenstate and energy level correlations in delocalized and localized phases, as well at criticality, are discussed. In the many-body part, models with short-range and power-law interactions are considered, as well as the quantum-dot model representing the limit of the “most long-range” interaction. Central themes – which are common to the RRG and MBL problems – include ergodicity of the delocalized phase, localized character of the critical point, strong finite-size effects, and fractal scaling of eigenstate correlations in the localized phase.
Owing to the challenges in obtaining realistic atomic configurations in large chemical phase spaces, it is not straightforward to describe structure–property relations in materials exhibiting configurational disorder. One example is iron-substituted strontium titanate (SrTi 1–x Fe x O 3–d , STF), a promising perovskite-derivative cathode material in solid oxide fuel cells that exhibits full solid solubility 0 ≤ x ≤ 1 and a tendency to exhibit short-range order. Here we demonstrate a multisublattice cluster expansion (CE) framework and apply it to STF across the full composition range. The CE approach is distinct from more traditional CE formulations in that clusters are defined explicitly by the chemical species distributed among multiple sublattices, rather than via cluster functions of occupation variables with decoration. The modified CE approach makes it easy to distinguish meaningful chemical interactions that are harder to extract from conventional CE, since for the latter chemical identity in a cluster is expressed as a product of site occupations. The least absolute shrinkage and selection operator (LASSO) is implemented as a regression analysis tool to select key clusters and avoid overfitting. We demonstrate this formulation on STF, and show that it can accurately predict configurational energies in comparison to conventional CE. From the key clusters, we identify that short-range ordering between substitutional Fe and oxygen vacancies (V O ) results in the formation of Fe–VO strings. In addition, we consider the stability of STF through CE-based Monte Carlo (MC) simulations and confirm the presence of superstructures that were previously observed in transmission electron microscopy. In this work, analysis of atomic configurations from MC samples reveals variations in the oxidation state of Fe atoms, which can be explained by the ordering tendency of Fe and V O . The cluster description and selection formalism described here may be applied to other disordered multisublattice systems for accurate and efficient material modeling.
The Ln n+1 M n X 3n+1 (Ln = lanthanide, M = transition metal, and X = tetrel) homologous series provides a platform to study collective phenomena in quantum materials. Here, in this work, we compare the crystal growth, structure, and magnetic properties of the n = 3 members of the Pr n+1 M n Ge 3n+1 (M = Fe, Co) analogues, Pr 4 Fe 3 Ge 10 (a = 4.3207 (10) Å, b = 35.523 (8) Å, c = 4.2982 (15) Å, and V = 659.7 (3) Å 3 ) and Pr 4 Co 3 Ge 10 (a = 4.3091 (12) Å, b = 35.750 (9) Å, c = 4.2807 (11) Å, and V = 659.4 (3) Å 3 ). We determined that the ideal flux growth conditions for each compound are highly dependent on the concentration of Sn flux and quench temperature. Pr 4 Fe 3 Ge 10 orders ferromagnetically at 10 K along the c-direction while Pr 4 Co 3 Ge 10 orders antiferromagnetically at 16 K along the b-direction. For both compounds, we observed a magnetic moment higher than that expected for only Pr 3+ ions (3.58 µ B /Pr), implying that the transition metal ions contribute to magnetic ordering (3.91, 3.48, and 3.69 µ B /Pr for Pr 4 Fe 3 Ge 10 , and 3.76, 4.04, and 3.83 µ B /Pr for Pr 4 Co 3 Ge 10 measured along the a-, b-, and c-directions, respectively). Moreover, the zero-field Mössbauer spectrum obtained at 4.2 K for Pr 4 Fe 3 Ge 10 demonstrates that the iron sites participate in magnetic ordering.
Co 3 Ga 2 Ge 5 was synthesized through arc-melting stoichiometric ratios of the elements, and a Ru 3 Sn 7 -type structure was confirmed by X-ray diffraction. Because Co 3 Ga 2 Ge 5 contains Ga and Ge, which have very similar X-ray and neutron scattering factors, any Ga/Ge crystallographic site preference cannot be determined with diffraction alone. The purpose of this study is to highlight the importance of using multiple techniques to characterize otherwise structurally ambiguous intermetallic compounds. Here, we utilize 71 Ga nuclear magnetic resonance spectroscopy and an analysis of the X-ray absorption fine structure to clarify the amount of Ga/Ge site mixing. Our combined use of X-ray diffraction and spectroscopy provides a comprehensive structural analysis of Ga site mixing across the Ge crystallographic sites, enhancing the understanding of the structure and properties of Co 3 Ga 2 Ge 5 .
Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.