Papers

Hyperuniform materials, characterized by anomalously suppressed long-wavelength density fluctuations, exhibit unique optical and photonic properties distinct from both crystalline and random media. While most prior studies have focused on single-species systems, we investigate the broader class of multihyperuniform systems inspired by biological photoreceptor mosaics. Using particle-based models with varying species number, size ratios, and interaction competition, we demonstrate that multispecies mixtures can achieve robust and stealthy hyperuniform configurations, even in highly disordered states. We further show how these configurations can be mapped to multifunctional composites with tailored optical responses, including isotropic structural coloration, enhanced absorption, and engineered dielectric properties that facilitate transmission while suppressing scattering. Our results highlight multihyperuniformity as a generalizable design principle for multifunctional disordered photonic materials, opening avenues for robust, tunable, and scalable optical applications.

Direct air capture of carbon dioxide (CO$_2$) is one of the most promising strategies to mitigate rising atmospheric CO$_2$ levels. Among various techniques, adsorption using porous materials is a viable method for extracting CO$_2$ from air, even under humid conditions. However, identifying optimal adsorbent materials remains a significant challenge. Moreover, the performance of existing materials can be improved by doping with active species that boost gas capture, a relatively unexplored field. In this study, we perform atomistic simulations to investigate the adsorption, structural, and energetic properties of CO$_2$ and water in realistic models of activated carbons. We first analyze the impact of explicitly considering surfaces containing functional groups, which aims to imitate the chemical environment of experimental samples. Additionally, we introduce potassium carbonate within the pores of the adsorbent to evaluate its effect on CO$_2$ and water adsorption. Our results demonstrate that both functional groups and potassium carbonate enhance adsorption, primarily by shifting the adsorption onset pressures to lower values. Specifically, potassium carbonate clusters act as extra adsorption sites for CO$_2$ and water, facilitating the nucleation of water molecules and promoting the formation of a hydrogen bond network within the activated carbon pores.

Phase separation in the presence of external forces has attracted considerable attention since the initial works for solid mixtures. Despite this, only very few studies are available which address the segregation process of liquid-vapor systems under gravity. We present here an extensive study which takes into account both hydrodynamic and gravitational effects on the coarsening dynamics. An isothermal formulation of a lattice Boltzmann model for a liquid-vapor system with the van der Waals equation of state is adopted. In the absence of gravity, the growth of domains follows a power law with the exponent $2/3$ of the inertial regime. The external force deeply affects the observed morphology accelerating the coarsening of domains and favoring the liquid accumulation at the bottom of the system. Along the force direction, the growth exponent is found to increase with the gravity strength still preserving sharp interfaces since the Porod's law is found to be verified. The time evolution of the average thickness $L$ of the layers of accumulated material at confining walls shows a transition from an initial regime where $L \simeq t^{2/3}$ ($t$: time) to a late-time regime $L \simeq g t^{5/3}$ with $g$ the gravitational acceleration. The final steady state, made of two overlapped layers of liquid and vapor, shows a density profile in agreement with theoretical predictions.

Forecasting complex, chaotic signals is a central challenge across science and technology, with implications ranging from secure communications to climate modeling. Here we demonstrate that magnons - the collective spin excitations in magnetically ordered materials - can serve as an efficient physical reservoir for predicting such dynamics. Using a magnetic microdisk in the vortex state as a magnon-scattering reservoir, we show that intrinsic nonlinear interactions transform a simple microwave input into a high-dimensional spectral output suitable for reservoir computing, in particular, for time series predictions. Trained on the Mackey-Glass benchmark, which generates a cyclic yet aperiodic time series widely used to test machine-learning models, the system achieves accurate and reliable predictions that rival state-of-the-art physical reservoirs. We further identify key design principles: spectral resolution governs the trade-off between dimensionality and accuracy, while combining multiple device geometries systematically improves performance. These results establish magnonics as a promising platform for unconventional computing, offering a path toward scalable and CMOS-compatible hardware for real-time prediction tasks.

Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computational framework for modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical foundations, but we need scalable data-driven techniques for modeling collective properties of high-dimensional neural networks from partial subsampled observations. Renormalization is a coarse-graining technique central to studying emergent scaling properties of many-body and nonlinear dynamical systems. While widely applied in physics and machine learning, coarse-graining complex dynamical networks remains unsolved, affecting many computational sciences. Recent diffusion-based renormalization, inspired by quantum statistical mechanics, coarse-grains networks near entropy transitions marked by maximal changes in specific heat or information transmission. Here I explore diffusion-based renormalization of neural systems by generating symmetry-breaking representations across scales and offering scalable algorithms using tensor networks. Diffusion-guided renormalization bridges microscale and mesoscale dynamics of dissipative neural systems. For microscales, I developed a scalable graph inference algorithm for discovering community structure from subsampled neural activity. Using community-based node orderings, diffusion-guided renormalization generates renormalization group flow through metagraphs and joint probability functions. Towards mesoscales, diffusion-guided renormalization targets learning the effective non-equilibrium dynamics of dissipative neural trajectories occupying lower-dimensional subspaces, enabling coarse-to-fine control in systems neuroscience and artificial intelligence.

We establish a novel mechanism for topological transitions in non-Hermitian systems that are controlled by the system size. Based on a new paradigm known as exceptional-bound (EB) band engineering, its mechanism hinges on the unique critical scaling behavior near an exceptional point, totally unrelated to the well-known non-Hermitian skin effect. Through a series of ansatz models, we analytically derive and numerically demonstrate how topological transitions depend on the system size with increasingly sophisticated topological phase boundaries. Our approach can be generically applied to design scaling-dependent bands in multi-dimensional lattices, gapped or gapless, challenging established critical and entanglement behavior. It can be experimentally demonstrated in any non-Hermitian platform with versatile couplings or multi-orbital unit cells, such as photonic crystals, as well as classical and quantum circuits. The identification of this new EB band mechanism provides new design principles for engineering band structures through scaling-dependent phenomena unique to non-Hermitian systems.

In quantum many-body systems with kinetically constrained dynamics, the Hilbert space can split into exponentially many disconnected subsectors, a phenomenon known as Hilbert-space fragmentation. We study the interplay of such fragmentation with symmetries, focusing on charge conserving systems with charge conjugation and translation symmetries as a concrete example. The non-Abelian algebra of these symmetries and the projectors onto the fragmented subsectors leads to the emergence of exponentially many logical qubits encoded in degenerate pairs of eigenstates, which can be highly entangled. This algebra also provides necessary conditions for experimental signatures of Hilbert-space fragmentation, such as the persistence of density imbalances at late times.

While quantum computing can accomplish tasks that are classically intractable, the presence of noise may destroy this advantage in the absence of fault tolerance. In this work, we present a classical algorithm that runs in $n^{\rm{polylog}(n)}$ time for simulating quantum circuits under local depolarizing noise, thereby ruling out their quantum advantage in these settings. Our algorithm leverages a property called approximate Markovianity to sequentially sample from the measurement outcome distribution of noisy circuits. We establish approximate Markovianity in a broad range of circuits: (1) we prove that it holds for any circuit when the noise rate exceeds a constant threshold, and (2) we provide strong analytical and numerical evidence that it holds for random quantum circuits subject to any constant noise rate. These regimes include previously known classically simulable cases as well as new ones, such as shallow random circuits without anticoncentration, where prior algorithms fail. Taken together, our results significantly extend the boundary of classical simulability and suggest that noise generically enforces approximate Markovianity and classical simulability, thereby highlighting the limitation of noisy quantum circuits in demonstrating quantum advantage.

We demonstrate that superconductivity driven by strong quantum-critical fluctuations can emerge near relativistic Mott transitions in twisted two-dimensional materials, taking on a remarkably rich character. In twisted double-bilayer WSe$_2$, all time-reversal-even, gap-opening collective modes promote pairing, whereas time-reversal-odd modes do not. In a Dirac model of twisted bilayer graphene, the Gross-Neveu transition into inter-valley-coherent insulators gives rise to a spectrum of degenerate and nearly degenerate superconducting states. More generally, we show that the richer the Dirac structure, the more readily pairs can form. A crucial ingredient of the theory is that critical fluctuations render the electronic states strongly incoherent, allowing attractive pairing channels to overcome the bare Dirac semi-metal behavior. Finally, we demonstrate a direct relation between boson-mediated pairing and the formation of charge-carrying skyrmionic excitations in the proximate insulating state.

We establish a fundamental connection between score-based diffusion models and non-equilibrium thermodynamics by deriving performance limits based on entropy rates. Our main theoretical contribution is a lower bound on the negative log-likelihood of the data that relates model performance to entropy rates of diffusion processes. We numerically validate this bound on a synthetic dataset and investigate its tightness. By building a bridge to entropy rates - system, intrinsic, and exchange entropy - we provide new insights into the thermodynamic operation of these models, drawing parallels to Maxwell's demon and implications for thermodynamic computing hardware. Our framework connects generative modeling performance to fundamental physical principles through stochastic thermodynamics.

Kagome metals have developed into a vibrant playground for materials physics, where geometric frustration, electronic correlations and band topology come together to create a variety of exotic phenomena. Recently synthesized CsCr$_3$Sb$_5$ has provided a rare opportunity to explore unconventional superconductivity in a strongly correlated kagome system with hints of frustrated magnetism and quantum criticality. Using spectroscopic imaging scanning tunneling microscopy, we reveal a cascade of density wave transitions with different symmetries in bulk single crystals of CsCr$_3$Sb$_5$. In particular, we discover a new electronic state $-$ a unidirectional density wave that breaks all mirror symmetries akin to a chiral density wave, but in contrast retains a mirror-glide symmetry. We term this state a frieze charge-stripe order phase, because its symmetry properties agree with one of the fundamental frieze symmetry groups. A combination of high-resolution imaging, Fourier analysis and theoretical simulations uncovers the crucial role of sublattice degrees of freedom in forming this phase, with internal chiral textures of opposite handedness. Our experiments reveal that superconductivity in CsCr$_3$Sb$_5$ develops from a new type of a unidirectional density wave, and set the foundation for exploring electronic states with frieze symmetry groups in quantum materials.

Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are not easily extended to predictions of the synthesizability of new materials and their properties. As a result, only a limited number of compositions identified to have desirable optoelectronic properties from these calculations have been realized experimentally. One way to bridge this gap is by learning from the experimental literature about how the perovskite composition-structure space relates to their likelihood of laboratory synthesis. Here, we present our efforts in combining high-throughput DFT data with experimental labels collected from the literature to train classifier models employing various materials descriptors to forecast the synthesizability of any given perovskite compound. Our framework utilizes the positive and unlabeled (PU) learning strategy and makes probabilistic estimates of the synthesis likelihood based on DFT- computed energies and the prior existence of similar synthesized compounds. Our data and models can be readily accessed via a Findable, Accessible, Interoperable, and Reproducible (FAIR) nanoHUB tool.

Propulsion of colloidal particles due to momentum transfer from localized surface reactions is investigated by solving the exact unsteady Stokes equation. We model the effect of surface reactions as either a force dipole acting on the fluid or a pair force acting on both the colloid and the fluid. Our analysis reveals that after a single reaction event the colloid's velocity initially decays as $\sim t^{-1/2}$, followed by a long-time tail decay $\sim t^{-5/2}$. This behavior is distinct from the $\sim t^{-3/2}$ decay seen for simple impulsively forced particles, a result of the force-free nature of the reaction mechanism. The velocity and transient dynamics are strongly controlled by the distance of the reaction from the colloid surface. For a colloid subject to periodic reactions, the theory predicts a steady-state velocity that is comparable to experimental results and previous simulations, suggesting that direct momentum transfer is a relevant mechanism for self-propulsion in systems like Janus particles. Finally, our study shows that fluid compressibility is not required for momentum transfer to produce colloidal propulsion.

Slater determinants underpin most electronic structure methods, but orbital-based approaches often struggle to describe strong correlation efficiently. Geminal-based theories, by contrast, naturally capture static correlation in bond-breaking and multireference problems, though at the expense of implementation complexity and limited treatment of dynamic effects. In this work, we examine the interplay between orbital and geminal frameworks, focusing on perfect-pairing (PP) wavefunctions and their relation to pair coupled-cluster doubles (pCCD) and Richardson-Gaudin (RG) states. We show that PP arises as an eigenvector of a simplified reduced Bardeen-Cooper-Schrieffer (BCS) Hamiltonian expressed in bonding/antibonding orbital pairs, with the complementary eigenvectors enabling a systematic treatment of weak correlation. Second-order Epstein-Nesbet perturbation theory on top of PP is found to yield energies nearly equivalent to pCCD. These results clarify the role of pair-based ansätze and open avenues for hybrid approaches that combine the strengths of orbital- and geminal-based methods.

Moiré materials offer a versatile platform for engineering excitons with unprecedented control, promising next-generation optoelectronic applications. While continuum models are widely used to study moiré excitons due to their computational efficiency, they often disagree with ab initio many-body approaches, as seen for intralayer excitons in WS$_2$/WSe$_2$ heterobilayers. Here, we resolve these discrepancies using an atomistic, quantum-mechanical framework based on the Bethe-Salpeter equation with localized Wannier functions as the basis for the electronic structure. We show that inclusion of dielectric screening due to hexagonal boron nitride (hBN) encapsulation is essential to reproduce the full set of experimentally observed features of moiré intralayer excitons. Our analysis reveals a competition between Wannier and charge transfer characters, driven by variations between direct and indirect band gaps at high symmetry stacking regions due to atomic relaxations and environmentally tunable electron-hole interactions. Building on this insight, we demonstrate that the lowest-energy bright excitons are Wannier-like in WS2/WSe2 heterobilayers but charge-transfer-like in twisted WSe2 homobilayers, despite having comparable moiré lengths when encapsulated in hBN. In the absence of hBN encapsulation, the lowest-energy bright exciton in twisted WSe$_2$ becomes Wannier-like. These results establish atomistic modeling as a powerful and efficient approach for designing and controlling excitonic phenomena in moiré materials.

We study the Josephson effect in junctions composed of two ferromagnetic insulator/diffusive superconductor bilayers separated by an insulating barrier. By computing the free energy of the system, we identify two distinct contributions: (i) The work performed by a current source to create a supercurrent through the junction, and (ii) an antiferromagnetic coupling between ferromagnetic insulators, mediated by the superconducting condensate across the insulating barrier. The competition between these contributions allows for switching between parallel and antiparallel configurations of the magnetizations of the ferromagnetic insulators. We explicitly show that the switching occurs at finite temperatures and for superconducting phase differences satisfying $\pi/2 < \phi < 3\pi/2$. Importantly, this effect can be realized in ferromagnetic insulators with sufficiently large easy-plane anisotropy energy. Using realistic junction parameters, we demonstrate that the switching can be controlled by phase bias and triggered by half-flux-quantum voltage pulses or external magnetic field pulses on the microsecond timescale. These results provide a route towards controllable Josephson-based superconducting memory devices based on EuS/Al heterostructures.

Deep neural networks have achieved remarkable success, yet our understanding of how they learn remains limited. These models can learn high-dimensional tasks, which is generally statistically intractable due to the curse of dimensionality. This apparent paradox suggests that learnable data must have an underlying latent structure. What is the nature of this structure? How do neural networks encode and exploit it, and how does it quantitatively impact performance - for instance, how does generalization improve with the number of training examples? This thesis addresses these questions by studying the roles of locality and compositionality in data, tasks, and deep learning representations.

Topological gauge theories constitute a framework for understanding strongly correlated quantum matter in terms of weakly interacting composite degrees of freedom. Their topological properties become evident when these theories are realized on a space of non-trivial topology. Here, we propose a scheme to realize a one-dimensional topological gauge theory, the so-called chiral BF theory, on a ring geometry. We obtain such a theory by dimensionally reducing Chern-Simons theory on a disk to the chiral BF theory defined on the ring. Then, we encode the theory into a Hamiltonian with a coupling between angular momentum and density, and we propose and numerically benchmark its realization in an optically-dressed Bose gas confined in a ring-shaped trap. There, the topological properties of the underlying theory manifest themselves through a magnetic flux variable that is density-dependent. We quantify such density-dependent magnetic flux in terms of the ground-state angular momentum and the chiral properties of the system through a Bogoliubov analysis. Our proposal enables the observation of topological features of the chiral BF theory that become manifest due to the non-trivial topology of the ring geometry.

The chiral central charge $c_-$ is a key topological invariant of the edge characterizing the bulk two-dimensional chiral topological order, but its direct evaluation in microscopic spin models has long been a challenge, especially for non-abelian topological order. Building on the recently developed modular commutator formalism, we numerically obtain $c_-$ directly from single ground-state wave functions of two-dimensional interacting spin models that have chiral topological order. This provides a geometry-independent and bulk diagnostic of chirality. We study two nonintegrable systems – the Zeeman-Kitaev honeycomb model and the kagome antiferromagnet – both subjected to scalar spin chirality perturbations. We find that the modular commutator yields results consistent with the expected topological quantum field theories. We also compute the topological entanglement entropy which provides an independent diagnostic of the topological orders. Our work establishes modular commutators as a powerful numerical probe of chiral topological order in strongly correlated quantum magnets.

We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even under low signal-to-noise and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust computational super-resolution for nanoscale characterization and quantum photonic device fabrication.

Controlling optical and tribological properties of metal surfaces, like color and wear rate, without altering their chemical composition is a highly desirable process across numerous fields of science and industry. It represents a cost-effective alternative to traditional chemical methods, particularly for copper, one of the most important metals widely used where high electrical and thermal conductivity, alongside resistance to corrosion, are required. We investigated the control of copper surface texture through a controlled micromodification process, utilizing constant force and velocity with abrasive silicon carbide sandpaper on ultrapure copper pellets exhibiting elongated crystallographic grains, and its impact on optical properties. Systematically varying grit size and rubbing direction, both along and across the grains, resulted in tunable microgroove morphology, demonstrating a marked difference in wear rate between single-grain and multi-grain abrasion. Furthermore, modification along copper grain boundaries yielded a change in the wear rate by a factor of two, related to single-grain and multi-grain abrasion regime changes, enabling precise control over material performance via tuned abrasion conditions. Colorimetric analysis via C-Microscopy revealed a strong, statistically significant relationship between abrasive parameters, microgroove geometry (inclination angle, depth, and size), and optical spectral signatures, which were then parametrized to achieve targeted control. This research demonstrates a simple yet effective approach to color and reflectance modification via microgroove engineering, offering a pathway to customized material properties by uniquely coupling contact mechanics, surface morphology, and colorimetry at the microscale level.

Nanoplatelets open up a wide range of possibilities for building materials with novel properties linked to their shape anisotropy. A challenge consists of controlling dynamically the order of positioning and orientation in three dimensions by assembly to exploit the collective properties at the macroscale. While most studies to date have focused on hard platelets that cannot be stimulated by an external trigger, in the present work we tackle the case of core/shell platelets, composed of a mineral core (laponite) coated with a soft polymeric shell (PEO100-PPO65-PEO100 copolymer) whose swelling can be triggered by temperature variation. We identified unambiguously the signature associated with the different phases obtained as a function temperature and concentration by combining local (X-ray scattering, electron microscopy) and global (rheology, optical birefringence) methods of analysis. In this way, we obtained two main results. The first shows that the deposition of a soft layer onto laponite surface enables a phase transition from isotropic liquid towards liquid suspensions of random stacks which is not observed for bare laponite suspensions in the studied weight concentration range (\Phi < 14 wt.). The second result shows that an increase of nanoplatelet effective volume fraction triggered by temperature (swelling of the polymer shell) induces a phase transition from liquid suspensions of random stacks towards birefringent gels of nematic stacks. Both results agree with numerically predicted phase sequences expected by variation in particle density under similar charge screening conditions, taking into account the contribution of the copolymer layer to the particle volume fraction. We believe that these results pave the way for the control of nanoplatelet self-assembly by external action.

Biological processes like the sequestration of Superoxide Dismutase 1 (SOD1) into biomolecular condensates such as FUS and stress granules are essential to understanding disease mechanisms, including amyotrophic lateral sclerosis (ALS). Our study demonstrates that the hydration environment is crucial in these processes. Using the advanced CVF water model, which captures hydrogen-bond networks at the molecular level, we show how water greatly impacts SOD1's behavior, residency times, and transition rates between different associative states. Importantly, when water is included to hydrate an implicit solvent model (OPEP), we gain a new perspective on the free energy landscape of the system, leading to a conclusion that clarifies that suggested by OPEP alone. While the OPEP model indicated that Bovine Serum Albumin (BSA) crowders reduce SOD1's partition coefficient (PC) mainly due to nonspecific interactions with BSA, our enhanced explicit-water approach reveals that the hydration entropy behavior in BSA drives the observed decrease in PC. This highlights that explicitly modeling water is essential for accurately understanding protein-crowder interactions and their biological relevance, emphasizing water's role in cellular phase separation and disease-related processes.

Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach inefficient. Failures can also occur if the search ventures too far into regions that are not represented well enough by the GP model. Here, these challenges are resolved by using geometry-aware optimal transport measures and an active pruning strategy using a summation over Wasserstein-1 distances for each atom-type in farthest-point sampling, selecting a fixed-size subset of geometrically diverse configurations to avoid rapidly increasing cost of GP updates as more observations are made. Stability is enhanced by permutation-invariant metric that provides a reliable trust radius for early-stopping and a logarithmic barrier penalty for the growth of the signal variance. These physically motivated algorithmic changes prove their efficacy by reducing to less than a half the mean computational time on a set of 238 challenging configurations from a previously published data set of chemical reactions. With these improvements, the GP approach is established as, a robust and scalable algorithm for accelerating saddle point searches when the evaluation of the energy and atomic forces requires significant computational effort.

We investigate how fermionic anticommutation shapes transport in a noninteracting resonant-level model where a single central site is coupled to an environment. To this end, we compare a fermionic reservoir with a bath of spin-half modes using exact diagonalization and a perturbative expansion of the master equation to identify the differences. Notably, we find different reduced dynamics for the spin and fermionic baths even though the particles remain noninteracting and both models enforce local Pauli blocking. These differences originate from higher-order terms in the system-bath coupling, where fermionic anticommutation introduces exchange signs in higher-order correlations. By contrast, all second-order contributions, set solely by two-point correlators, coincide. Deviations are largest for small to intermediate bath sizes, fading in the effectively Markovian regime where higher-order corrections are negligible. These results identify when spin baths can be a substitute for fermionic reservoirs and vice versa.

From the formation of ice in small clusters of water molecules to the mass raids of army ant colonies, the emergent behavior of collectives depends critically on their size. At the same time, common wisdom holds that such behaviors are robust to the loss of individuals. This tension points to the need for a more systematic study of how number influences collective behavior. We initiate this study by focusing on collective behaviors that change abruptly at certain critical numbers of individuals. We show that a subtle modification of standard bifurcation analysis identifies such critical numbers, including those associated with discreteness- and noise-induced transitions. By treating them as instances of the same phenomenon, we show that critical numbers across physical scales and scientific domains commonly arise from competing feedbacks that scale differently with number. We then use this idea to find overlooked critical numbers in past studies of collective behavior and explore the implications for their conclusions. In particular, we highlight how deterministic approximations of stochastic models can fail near critical numbers. We close by distinguishing these qualitative changes from density-dependent phase transitions and by discussing how our approach could generalize to broader classes of collective behaviors.

The magnetic properties of two-dimensional (2D) materials have been attracting increasing attention in recent years due to their unique behavior and possible applications in new devices. One material of great interest is the 2D van der Waals (vdW) crystal CrSBr, that exhibits antiferromagnetic (AF) order at low temperatures due to an interlayer AF exchange interaction. Here we present a full quantum spin-wave theory for CrSBr considering three intralayer and one interlayer exchange interactions, and triaxial magnetic anisotropy. The fits of the theoretical results to antiferromagnetic resonance (AFMR) measurements and inelastic neutron scattering data yield reliable values for the seven interaction parameters that can be used to calculate other properties of this interesting material.

We report a combined experimental and numerical investigation of spin-wave dynamics in a hybrid magnonic crystal consisting of a CoFeB artificial spin ice (ASI) of stadium-shaped nanoelements patterned atop a continuous NiFe film, separated by a 5 nm Al2O3 spacer. Using Brillouin light scattering spectroscopy, we probe the frequency dependence of thermal spin waves as functions of applied magnetic field and wavevector, revealing the decisive role of interlayer dipolar coupling in the magnetization dynamics. Micromagnetic simulations complement the experiments, showing a strong interplay between ASI edge modes and backward volume modes in the NiFe film. The contrast in saturation magnetization between CoFeB and NiFe enhances this coupling, leading to a pronounced hybridization manifested as a triplet of peaks in the spectra - predicted by simulations and observed experimentally. This magnon-magnon coupling persists over a wide magnetic field range, shaping both the spin-wave dispersion and frequency-field response throughout the hysteresis loop. Our findings establish how ASI geometry can selectively enhance specific spin-wave wavelengths in the underlying film, identifying them as preferential channels for magnonic signal transport and manipulation.

We investigate the zero-magnetization phase diagram of a spin-1/2 chain with competing ferromagnetic nearest-neighbor and antiferromagnetic next-nearest-neighbor exchange couplings in the strongly interacting regime. Using density matrix renormalization group (DMRG) simulations, we discover two successive commensurate-incommensurate transitions of the non-conformal Pokrovsky-Talapov universality class, occurring (even) at zero magnetic field. The first transition marks the condensation of bound pairs of magnons into a critical phase with central charge $c=2$, emerging from a gapped period-4 phase. At the second transition, an incommensurate quadrupolar (or nematic) Luttinger liquid forms out of a gapped phase separation state, via the pairwise condensation of domain walls. We argue that both transitions involve the same underlying incommensurate nematic Luttinger liquid, and that the $c=2$ phase can be understood as a coexistence of a conventional (single-magnon type) and quadrupolar (two-magnon type) Luttinger liquids. Our results demonstrate that frustration alone is sufficient to drive continuous commensurate-incommensurate transitions of Mott type and stabilise incommensurate quasi-long-range order without doping.

We report resistivity and Hall effect measurements in the chiral antiferromagnet EuPtSi. Depending on the magnetic field orientation with respect to the crystallographic axes, EuPtSi presents different topological magnetic phases below the Néel temperature $T_N=4.05$K. In particular, for a field $H \parallel $ [111], it exhibits the well known skyrmion lattice A-phase inside the conical phase between $T=0.45$K and $T_N$ in the field range from 0.8T to 1.4T. Remarkably, the skyrmion lattice state in EuPtSi, composed of nanoscale skyrmions, can be extended down to very low temperature (lower than 0.1K) through field-cooling regardless of the cooling rate and of the magnetic history. Similarly the metastability of the A'- and B-phases ($H \parallel $ [100]) at low temperature is evidenced by our measurements. These results suggest that EuPtSi is a peculiar example where the competition between the topological stability and the thermal agitation can lead to metastable quantum skyrmion state.