Papers

Natural hyperbolic materials have attracted significant interest in the field of photonics due to their unique optical properties. Based on the initial successful explorations on layered crystalline materials, hyperbolic dispersion was associated with extreme structural anisotropy, despite the rarity of natural materials exhibiting this property. Here we show that non cubic electrides are generally promising natural hyperbolic materials owing to charge localization in interstitial sites. This includes elemental and binary electrides, as well as some two-dimensional materials that show prominent in-plane hyperbolic dispersion. They exhibit low plasma frequencies and a broad hyperbolic window spanning the infrared to the ultraviolet. In semiconductor electrides, anisotropic interband transitions provide an additional mechanism for hyperbolic behaviour. These findings remove the previously held prerequisite of structural anisotropy for natural hyperbolic materials, and open up new opportunities, which might change the current strategy for searching and design photonic materials.

Block copolymer (BCP) melts play a critical role in the design of thermoplastics, owing in large part to the creation of alternating nano-scale domains of soft and stiff components. Considerable attention has been given to the short-to-intermediate time response of BCP melts, when the storage modulus is expected to dominate the viscoelastic properties. However, less attention has been paid to the long-time relaxation and rigidity of microphase separated BCP melts or the role that domain morphology plays in modulating near-equilibrium response. We take advantage of the ability of self-consistent field theory (SCFT) to calculate equilibrium properties of BCP melts to explore the anisotropic elastic response of ordered ABA and AB copolymer melts as quasistatic deformation processes. This allows us to determine the anisotropic stiffness of the liquid crystal-like lamellar and columnar phases due to modulations in domain spacing, as well as the full stiffness tensor of the cubic BCC sphere and double gyroid phases. We explore elastic modulus landscapes for both single grain materials and random polycrystals over architectural parameters and segregation strengths, using AB diblock and ABA triblock melts as key examples. Comparing AB and ABA melts, we show slight differences in equilibrium rigidity, which are shown to be due to a difference in effective domain segregation. Finally, we consider the equilibrium bending stiffnesses of lamellae and columnar phases, showing that the columnar phase is significantly stiffer than lamellae, with a characteristic bending length scale that is an order of magnitude larger.

Dissipation anomaly-the persistence of finite energy dissipation in the inviscid limit-is a hallmark of turbulence, sometimes regarded as the "zeroth law" of turbulent flows. Here, we demonstrate that this phenomenon is not exclusive to turbulence. Using fluctuating hydrodynamics, we show that a simple gradient-driven nonequilibrium steady state, in which a fluid is subjected to a constant scalar gradient but remains macroscopically quiescent, also exhibits dissipation anomaly. Direct numerical simulations and self-consistent mode-coupling theory reveal that the anomaly originates from giant, long-range nonequilibrium fluctuations amplified by the imposed gradient. While linear theory predicts a divergent dissipation in the inviscid limit, nonlinear mode coupling regularizes the divergence, yielding a finite anomalous dissipation. Our findings identify a new, non-turbulent arena for dissipation anomaly and establish the interplay between thermal noise and nonequilibrium driving as a fundamental route to singular behavior in hydrodynamics.

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we propose a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160^3), our method reduces memory usage and runtime in inference by 117x and 115x, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder's ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. The training was optimized to learn from the stochastic Potts Monte Carlo method. Our findings provide a highly scalable approach for simulating grain growth.

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 {\deg}C across all phases except bainite, which shows a slightly higher MAE of 27 {\deg}C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows.

Entanglement has been identified as a key resource for enhancing charging performance in quantum batteries. We show that the kicked-Ising model at the self-dual point provides an explicit charging mechanism, where maximal entanglement growth yields maximal energy injection. Identifying the Floquet dynamics as a Clifford quantum cellular automata and considering exact diagonalization in momentum space, we analytically characterize the charging process, featuring a stable performance while achieving maximal charging. We further propose a fixed time window protocol that accelerates charging toward the continuously driven transverse-field limit. Spin-correlator analysis reveals that scrambling and light-cone spreading govern charging performance. The protocols remain compatible with diverse platforms, underscoring their scalability and practical feasibility.

Free energy landscapes encode the kinetics, intermediates, and transition states that govern molecular processes and are thus a key target of single biomolecule research. Typical approaches to deriving optimal, error-minimizing, non-equilibrium driving protocols for estimating these landscapes require a priori knowledge of the landscape. Here, we present an alternative: an iterative algorithm for optimizing full free energy landscape reconstructions which can be used alongside experiments on unknown landscapes. Our approach (i) takes experimental or simulated trajectory data; (ii) reconstructs an `approximate' energy landscape; (iii) derives optimal control protocols from low-dimensional differentiable Brownian dynamics simulations on the candidate landscape using automatic differentiation; (iv) re-runs the experiment or simulation using the updated protocol; and (v) iterates until convergence. Using this approach, we recover known benchmarks from the literature and probe far-from-equilibrium regimes for symmetric, asymmetric, and triple-well energy landscapes under both 1- and 2-dimensional control. Our control protocols – derived with no a priori knowledge of the energy landscape – yield substantially reduced variance and bias in free energy landscape reconstructions compared to naive linear protocols.

An important component of the success of large AI models is double descent, in which networks avoid overfitting as they grow relative to the amount of training data, instead improving their performance on unseen data. Here we demonstrate double descent in a decentralized analog network of self-adjusting resistive elements. This system trains itself and performs tasks without a digital processor, offering potential gains in energy efficiency and speed – but must endure component non-idealities. We find that standard training fails to yield double descent, but a modified protocol that accommodates this inherent imperfection succeeds. Our findings show that analog physical systems, if appropriately trained, can exhibit behaviors underlying the success of digital AI. Further, they suggest that biological systems might similarly benefit from over-parameterization.

Using a minimal aggregation-based model, we address the efficient information transfer observed in natural flocks during collective turns. In our system, the turning signal emitted by a single individual propagates throughout the collective primarily at a constant speed. To characterize the source of this capability, we analyze the system in the continuum limit. Thus, we demonstrate that wave-propagating features arise solely from the non-reciprocal nature of interactions. We explore spectral anomalies that emerge due to this mechanism and provide an explanation through a perturbative analysis. Remarkably, within this scenario, the description of velocity fluctuations is found to follow a structure analogous to a Born approximation. Our model can be extended to study other physical contexts exhibiting polar order, and our arguments can be tested in natural systems.

Digital computers are power-hungry and largely intolerant of damaged components, making them potentially difficult tools for energy-limited autonomous agents in uncertain environments. Recently developed Contrastive Local Learning Networks (CLLNs) - analog networks of self-adjusting nonlinear resistors - are inherently low-power and robust to physical damage, but were constructed to perform supervised learning. In this work we demonstrate success on two simple RL problems using Q-learning adapted for simulated CLLNs. Doing so makes explicit the components (beyond the network being trained) required to enact various tools in the RL toolbox, some of which (policy function and value function) are more natural in this system than others (replay buffer). We discuss assumptions such as the physical safety that digital hardware requires, CLLNs can forgo, and biological systems cannot rely on, and highlight secondary goals that are important in biology and trainable in CLLNs, but make little sense in digital computers.

The study of synthetic active matter systems holds the promise for designing smart materials and devices with emergent characteristics akin to those of living organisms, eventually opening the doors to the realization of artificial life. Such an investigation, however, is challenged by the difficulty inherent in identifying the relationship between the features of the elementary constituents and the emergent properties of the whole; to this end, a key step consists in the accurate quantification of the system's observed behavior. Here, we report the study of 50 self-propelled oil droplets floating on the surface of an aqueous solution. 25 droplets are stained with a red dye, and the other 25 are stained blue: the colorants affect the droplets' interfacial tension properties differently, consequently influencing their collective dynamics. Droplet trajectories extending for up to 5 hours are extracted from video recordings with a tracking pipeline developed ad hoc. The structural and dynamical analysis of the system reveals a “life-to-death” cycle unfolding in qualitatively distinct stages, showcasing a complex interplay between individual droplet mobility and collective organization. The tools developed and the results obtained in our work pave the way to the in silico modelling as well as the experimental design of synthetic active matter systems displaying life-like and programmable behavior.

Using programmable analog quantum annealing processors, we implement a sampling-based magnetic hysteresis protocol to probe the counterintuitive notion of magnetic memory in antiferromagnetic models. A key component of this protocol responsible for the hysteresis is a transverse field, which enables state transitions, while the longitudinal magnetic field sweep is done via a longitudinal control field. We observe intriguing non-monotonic magnetization, alongside full saturation and reversal of the hysteresis curve, Barkhausen-like noise, as well as emergent magnetic domains mediated by quantum fluctuations that give rise to the magnetic memory effect in antiferromagnets.

Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active learning when using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network model architectures. We compare uncertainty estimates from ALIGNN deep ensembles to loss landscape uncertainty estimates obtained for solubility, bandgap, and formation energy prediction tasks. We then evaluate how the quality of the uncertainty estimate impacts an active learning campaign that seeks model generalization to out-of-distribution data. Uncertainty calibration methods were found to variably generalize from in-domain data to out-of-domain data. Furthermore, calibrated uncertainties were generally unsuccessful in reducing the amount of data required by a model to improve during an active learning campaign on out-of-distribution data when compared to random sampling and uncalibrated uncertainties. The impact of poor-quality uncertainty persists for random forest and eXtreme Gradient Boosting models trained on the same data for the same tasks, indicating that this is at least partially intrinsic to the data and not due to model capacity alone. Analysis of the target, in-distribution uncertainty, out-of-distribution uncertainty, and training residual distributions suggest that future work focus on understanding empirical uncertainties in the feature input space for cases where ensemble prediction variances do not accurately capture the missing information required for the model to generalize.

Impacted with sufficiently large stress, a dense, initially liquid-like suspension can be forced into a solid-like state through the process of shear jamming. While the onset of shear jamming has been investigated extensively, less is known about the resulting solid-like state in the high stress limit and its failure. We experimentally produce such high-stress failure by impacting dense suspensions at a controlled speed. Using cornstarch suspensions we vary impact speed over several orders of magnitude and change fluid viscosity and surface tension in order to identify the conditions for failure. The results are compared with dense suspensions of potato starch or silica particles. In the case of fracture, we observe two types of cracks: a primary circular crack around the impactor followed by secondary radial cracks. Mapping out the onset of radial fracturing for different volume fractions and impact speeds, we identify the requirements for failure via crack formation to occur with at least 50% likelihood. We find that this likelihood is not sensitive to changes in particle diameter, but increases when the solvent's viscosity or surface tension are reduced. In the state diagram for dense suspensions we delineate the upper limit of shear-jammed rigidity and the crossover into a fracture regime at large volume fraction and normal stress, several orders of magnitude above the onset stress for shear-jamming. We find that the onset of fracturing in many cases is correlated with internal ductile deformation of the shear-jammed material underneath the impactor, observable in normal stress as a function of axial strain. For small suspension volumes and large impact speeds, we find strain-hardening up until fracturing. This more brittle behavior results in a modulus that, just before crack formation, is an order of magnitude larger than in shear-jammed suspensions undergoing ductile deformation.

The functional renormalization group (fRG) is acknowledged as a powerful tool in quantum many-body physics and beyond. On the technical side, conventional implementations of the fRG rely on regulators for bare propagators only. Starting from Schwinger–Dyson and Bethe–Salpeter equations, we develop here an fRG formulation where both bare propagators and bare interactions can be dressed with regulators. The approach thus obtained is an extension of the multiloop fRG recently introduced for many-fermion systems. Using the single-boson exchange decomposition, we show that the underlying flow equations are simply interpreted as adding a regulator to the bosonic propagator and that such an extension scarcely changes the original structure of the flow equations. Overall, we provide a framework for implementing approaches that cannot be realized with conventional fRG methods, such as temperature flows for models with retarded interactions. For concrete applications, we analyze the loop convergence of our scheme against conventional cutoff schemes for the Anderson impurity model. Finally, we devise a new temperature-flow scheme that implements a cutoff in both the propagator and the bare interaction, and demonstrate its validity on a model of an Anderson impurity coupled to a phonon.

The coupled entropy, $H_\kappa,$ is proven to uniquely satisfy the requirement that a generalized entropy be a measure of the uncertainty at the scale, $\sigma,$ for a class of non-exponential distributions. The coupled stretched exponential distributions, including the generalized Pareto and Student's t distributions, are uniquely parameterized to quantify linear uncertainty with the scale and nonlinear uncertainty with the tail shape for a broad class of complex systems. Thereby, the coupled entropy optimizes the representation of the uncertainty due to linear sources. Lemmas for the composability and extensivity of the coupled entropy are proven. The uniqueness of the coupled entropy is further supported by demonstrating consistent thermodynamic relationships, which correspond to a model used for the momentum of high-energy particle collisions. Applications of the coupled entropy in measuring statistical complexity, training variational inference algorithms, and designing communication channels are reviewed.

Real ecosystems are characterized by sparse and asymmetric interactions, posing a major challenge to theoretical analysis. We introduce a new method to study the generalized Lotka-Volterra model with stochastic dynamics on sparse graphs. By deriving local Fokker-Planck equations and employing a mean-field closure, we can efficiently compute stationary states for both symmetric and asymmetric interactions. We validate our approach by comparing the results with the direct integration of the dynamical equations and by reproducing known results and, for the first time, we map the phase diagram for sparse asymmetric networks. Our framework provides a versatile tool for exploring stability in realistic ecological communities and can be generalized to applications in different contexts, such as economics and evolutionary game theory.

Edge states of chiral topologically ordered phases are commonly described by chiral Luttinger liquids, effective theories that are exact only in the hydrodynamic limit. Motivated by recent bulk observations of fractional Chern insulators (FCIs) in two-dimensional materials and by synthetic realizations in ultracold atoms, we revisit this framework and quantify deviations from the hydrodynamic limit due to lattice effects. Using a combination of analytical arguments and numerical simulations, we disentangle universal from nonuniversal edge properties. We outline experimental probes in excitonic FCIs and in ultracold atom systems, and in particular propose time-resolved edge spectroscopy to directly access the predicted exponents and velocities.

We introduce a reinforcement learning algorithm designed to identify the fixed points of a given quantum operation. The method iteratively constructs the unitary transformation that maps the computational basis onto the basis of fixed points through a reward-penalty scheme based on quantum measurements. In cases where the operation corresponds to a Hamiltonian evolution, this task reduces to determining the Hamiltonian eigenstates. The algorithm is first benchmarked on random Hamiltonians acting on two and three qubits and then applied to many-body systems of up to six qubits, including the transverse-field Ising model and the all-to-all pairing Hamiltonian. In both cases, the algorithm is demonstrated to perform successfully; in the pairing model, it can also reveal hidden symmetries, which can be exploited to restrict learning to specific symmetry sectors. Finally, we discuss the possibility of post-selecting high-fidelity states even when full convergence has not been reached.

We develop a symmetry indicator framework to efficiently predict the topology of superlattice-induced minibands with spin-orbit coupling. Our algorithm requires input only from the parent material before the superlattice is applied. The simplification arises by assuming a perturbatively weak superlattice potential; however, our results extend beyond the perturbative regime as long as the superlattice-induced gaps remain open. We first consider a time-reversal- and inversion-symmetric system subject to a weak superlattice potential and derive a compact formula for the $\mathbb{Z}_2$ invariant of the lowest miniband. We then extend to time-reversal breaking systems and compute the Chern number. We apply our theory to selected transition metal dichalcogenides, HgTe/CdTe quantum wells, and thin films of three-dimensional topological insulators and Dirac semimetals. We find topological superlattice bands can arise even from non-topological materials, broadening the pool of candidates for realizing topological flat bands. Our theory predicts which geometry and periodicity of superlattice will yield topological bands for a given material, providing a clear guiding principle for designing topological superlattice heterostructures.

A detailed study of the structural and magnetic properties of a spin-$5/2$ triangular lattice antiferromagnet RbFe(HPO$_{3}$)$_{2}$ is presented using x-ray diffraction, magnetization, heat capacity, and $^{31}$P nuclear magnetic resonance (NMR) experiments on a polycrystalline sample. The crystal structure features an equilateral triangular lattice of Fe$^{3+}$ ions. The thermodynamic measurements reveal the onset of a magnetic long-range order at $T_{\rm N1} \simeq 7.8$ K in zero-field, followed by another low temperature field induced ordering at $T_{\rm N2}$ in higher fields. The transition at $T_{\rm N1}$ is further confirmed from the NMR spin lattice relaxation measurements. The value of the frustration ratio ($f \simeq 7$) implies moderate spin frustration in the compound. The $^{31}$P NMR spectra exhibit two distinct spectral lines corresponding to two inequivalent phosphorus sites (P1 and P2), consistent with the crystal structure. The P1 site is strongly coupled with an isotropic hyperfine coupling of $A_{\rm hf}^{\rm iso} = 0.55(2)$ T/$\mu_{\rm B}$ while the P2 site is weakly coupled with $A_{\rm hf}^{\rm iso} = 0.25(3)$ T/$\mu_{\rm B}$ with the Fe$^{3+}$ ions. The magnetic susceptibility and NMR shift data are described well assuming a spin-$5/2$ isotropic triangular lattice antiferromagnetic model with an average exchange coupling of $J/k_{\rm B} = 2.8(2)$ K. Below $T_{\rm N1}$, the spectra evolve into a nearly rectangular powder pattern, indicating a commensurate antiferromagnetic type order. The $^{31}$P spin-lattice relaxation rate well below $T_{\rm N1}$ follows a $T^3$ temperature dependence, implying a two-magnon Raman scattering mechanism in the ordered state. Three well-defined phase regimes are clearly ascertained in the $H-T$ phase diagram, reflecting a weak magnetic anisotropy in the compound.

Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron-phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al.

Quantum many-body scars are special eigenstates that violate the eigenstate thermalization hypothesis while residing at finite energy density along with thermalizing eigenstates. The spin-1 XY model is known to host a family of such exceptional states originating from long-lived quasiparticle excitations that exhibit anomalously low entanglement entropy and long-time periodic revivals, resulting in weak ergodicity breaking. We study the stability of these scarred states against typical U(1) symmetry preserving perturbation in the XY chain. While perturbation theory can describe the deformed scar states at small system sizes, finite-size scaling of the perturbation matrix elements indicate that the scars ultimately thermalize in larger chains. Nonetheless, we demonstrate that the long-range order associated with the scars decays under the perturbation, and we estimate the relaxation timescale of oscillatory dynamics in certain local observables to be of order $\lambda^{-2}$, where $\lambda$ is the perturbation strength.

We demonstrate a single-photon detector operating in the microwave domain, based on photo-assisted quasiparticle tunneling events that poison a superconducting island. The detection relies on continuously monitoring the island's charge parity using microwave reflectometry. This scheme achieves 10% detection efficiency with sub \SI{50}{\ns} time resolution and short dead time ($\sim \SI{1}{\us}$), for microwave photons at \SI{10}{\GHz}. The detector features three junctions connected to a superconducting island, which together carry out photoelectric conversion and charge readout. The enhanced light–matter coupling, crucial to photon to quasiparticle conversion, is provided by a granular aluminum-based high-impedance microwave resonator. The time resolved detection of itinerant microwave photon opens up new perspectives in quantum sensing, microwave quantum optics and mesoscopic physics.

We predict that the spin-polarized electrons in a two-dimensional triangular lattice with strong electron-electron repulsion gives rise to f-wave pairing. The key point is that the first-order interaction, which is usually pair-breaking, vanishes or nearly vanishes in certain f-wave channels due to symmetry constraints. As a result, these f-wave pairing channels are governed by the subleading-order processes which enable pairing when the perturbation theory is controlled. We illustrate this using the Hubbard model on the triangular lattice with on-site and nearest-neighbor repulsion, where we find a $T_c\sim 1\% $ of electron's bandwidth. For a general screened interaction, the same idea works asymptotically, but a third-order calculation is needed to fully determine the strength of f-wave pairing.

Emerging granularity in superconducting films by tuning disorder is a well-studied topic, both theoretically and experimentally. However, the orbital magnetic field generates a vortex lattice and contributes to the formation of periodic inhomogeneities. Here, we study superconducting films in the simultaneous presence of disorder and a magnetic field, examining how inhomogeneities in various superconducting correlations evolve under these two perturbations. By performing scanning tunneling spectroscopy (STS) on thin films of \ce{Sr2VO_{3-\text{x}}FeAs} layer structures under both zero and finite orbital magnetic fields, we report impressive similarities between our theoretical results and the experimental findings. Our results have strong implications for identifying the nature of vortices in disordered superconductors, demonstrating a crossover from Abrikosov to Josephson character with increasing disorder, and provide predictive guidance for interpreting STS and current mapping data in complex superconductors.

The past decade has witnessed a spectacular development of machine-learned interatomic potentials (MLIPs), to the extent that they are already the approach of choice for most atomistic simulation studies not requiring an explicit treatment of electrons. Typical MLIP usage guidelines emphasize the need for exhaustive training sets and warn against applying the models to situations not considered in the corresponding training space. This restricts the scope of MLIPs to interpolative calculations, essentially denying the possibility of using them to discover new phenomena in a serendipitous way. While there are reasons to be cautious, here we adopt a more sanguine view and challenge the predictive power of two representative and widely available MLIP approaches. We work with minimalist training sets that rely on little prior knowledge of the investigated materials. We show that the resulting models – for which we adopt modest/default choices of the defining hyperparameters – are very successful in predicting non-trivial structural effects (competing polymorphs, energy barriers for structural transformations, occurrence of non-trivial topologies) in a way that is qualitatively and quasi-quantitatively correct. Our results thus suggest an expanded scope of modern MLIP approaches, evidencing that somewhat trivial – and easy to compute – models can be an effective tool for the discovery of novel and complex physical phenomena.

We study the origin of large topological Hall effect in the single-crystalline EuCd$_2$Sb$_2$, which orders antiferromagnetically at the Néel temperature $T_{\rm N}=7.4$ K. Measurements of magnetoresistance and Hall resistivity disclose anomalies that evolve with temperature and magnetic field, closely tracking the magnetization process. Analysis of these data identifies three possible mechanisms responsible for the enhanced Berry curvature driving the observed topological Hall effect. Below and above $T_{\rm N}$, Weyl states are the main sources of large momentum-space Berry curvature, though their formation mechanisms differ in these two temperature ranges. Below $T_{\rm N}$, breaking of $C_{3}$ symmetry generates Dirac points that split into Weyl nodes in applied magnetic field, whereas above $T_{\rm N}$, strong spin fluctuations can induce Weyl states. The third contribution, which occurs below $T_{\rm N}$, arises from scalar spin chirality developing within antiferromagnetic domain walls, which generates a real-space Berry curvature.

We study a cell fluid model of a many-particle system with Curie-Weiss-type interaction potential. It is considered as an open system in a fixed volume partitioned into a large number of congruent cubic cells. The interaction potential comprises two competing components: a global uniform attraction acting between all particle pairs in the volume and a short-range repulsion between particles occupying the same cell. Previous studies have established that this model admits an exact solution, exhibits multiple critical points, and undergoes a sequence of first-order phase transitions. Despite variations in the interaction strengths, no triple point appears as long as these parameters remain fixed. We demonstrate that incorporating effective {temperature-dependent} attractive interactions fundamentally alters the phase behavior of the cell model. This modification preserves the model's exact solvability while resulting in the emergence of a triple point in the phase diagram.

Societies experience politically stable and unstable phases along history, whereas political power is usually passed to new elite groups by these changes. Structural dynamics of the elites in a society have been proposed to be one of the core drivers shaping long term behavior. As current models and data are rather macroscopic, the emergence of macroscopic behavior from microscopic dynamics is largely unclear. Here, we introduce an adaptive network model of directed links representing political power and competing political ideas, based on local dynamical rules, only. The model is based on two socially motivated behaviors: the cumulative advantage effect of political power and intra-elite conflict. We observe punctuated equilibria as an emergent behavior and find a phase transition towards a disordered phase. We define an advance warning measure for elite collapse and find that the states of only a few largest nodes are suitable as a proxy with predictive information.