Papers

This paper investigates the formation of Spin Faraday waves in spin-orbit-coupled Bose-Einstein condensate under the stripe phase and explores the dispersion relation under three different phases. We discover that the SFW exhibit temporal and spatial patterns when the interaction is modulated periodically, and appear with resonant waves and higher order harmonics. SFW can be excited even when the modulation frequency resonates with the trap frequency. Furthermore, we study the dispersion relation of these Faraday modes through periodic modulation, which agrees well with our theoretical results under three quantum phases. Our work indicates novel physical phenomena originating from the introduction of spin-orbit coupling and provides a possible method for studying the dispersion of Bose gases.

Muon spin rotation ($\mu$SR) measurements of internal magnetic field shifts, known as the muon Knight shift, is used for determining pairing symmetries in superconductors. While this technique has been especially effective for $f$-electron-based heavy-fermion superconductors, it remains challenging in $d$-electron-based superconductors such as Sr$_2$RuO$_4$, where the Knight shift is intrinsically small. Here, we report high-precision muon Knight shift measurements of superconducting Sr$_2$RuO$_4$. We observe that using multiple pieces of crystals, a common practice in $\mu$SR measurements, induces a substantial paramagnetic shift below the superconducting transition temperature, $T_c$, when a weak magnetic field is applied. We attribute such an unresolved paramagnetic shift to stray fields generated by neighboring diamagnetic crystals. To avoid this, one piece of crystal was used in this study. We experimentally determine the muon Knight shift of Sr$_2$RuO$_4$ in the normal state to be -116$\pm$7 ppm. By combining the observed muon Knight shift with independently determined bulk magnetization data from the same crystal used in $\mu$SR and carefully separating various contributions to the shift, we confirm a significant reduction in the spin Knight shift below $T_c$, consistent with spin-singlet-like pairing. This result constitutes the precise muon Knight shift measurement in a $d$-electron-based superconductor. Our results highlight the potential of $\mu$SR as a powerful complementary technique to the established method of nuclear magnetic resonance for probing the spin susceptibility in superconductors.

Diffusive approximations of Markov jump processes often fail to accurately capture large fluctuations. This is confounding, as the rare events triggered by these large fluctuations, such as the failure of electronic memories, are often the object of interest. In this paper we present an improved diffusive approximation, extending a method previously limited to equilibrium systems. Using new tools from stochastic thermodynamics, we prove its validity to linear order in departures from equilibrium and demonstrate its superior accuracy over the Kramers-Moyal expansion in predicting both steady-state and transient properties, including the error rate of a non-equilibrium electronic memory.

We study the one-dimensional S=1 XXZ spin model with single-ion anisotropy. It is known that at the transition points between the Haldane and large-D phases, the model exhibits a quantum criticality described by the Gaussian theory, i.e., a conformal field theory with the central charge c=1. Using the bosonization approach, we investigate various correlation functions at the phase transition and derive their asymptotic forms. This allows us to clarify their peculiar behavior: the longitudinal (transverse) two-point spin correlation function has components that decay algebraically only in the uniform (staggered) sector. These theoretical predictions are verified by the numerical calculations using the density-matrix renormalization group method. The effect of weak bond alternation on the critical ground state at the phase transition is also discussed. It is shown that the bond alternation induces the missing power-law components in the correlation functions.

Spin spirals represent a fundamental class of noncollinear yet coplanar magnetic structures that give rise to diverse emergent phenomena through their coupling to itinerant electrons. We investigate metallic systems with spin spirals and uncover an unconventional anomalous Hall effect (AHE) induced by spiral magnetism in both noncentrosymmetric and centrosymmetric crystals. The spin spiral generates odd-parity spin splitting with polarization perpendicular to the helical plane, forming spin-nodal lines in the electronic structure. In the presence of spin-orbit coupling, finite magnetization opens gaps at these nodal lines, resulting in the Berry curvature concentrated near the gaps and a distinctive AHE. We identify the interplay among the spin-orbit coupling, helical plane orientation, and magnetization direction as the key ingredient for this spiral-induced AHE. Our results establish a broadly applicable mechanism for anomalous Hall responses across a wide range of materials hosting spin spirals.

Unraveling the mechanisms of ergodicity breaking in complex quantum systems is a central pursuit in nonequilibrium physics. In this work, we investigate a one-dimensional spin model featuring a tunable long-range hopping term, $H_{n}$, which introduces nonlocal interactions and bridges the gap between Hermitian and non-Hermitian regimes. Through a systematic analysis of level-spacing statistics, Krylov complexity, and entanglement entropy, we demonstrate that $H_{n}$ acts as a universal control parameter driving the transition from integrability to quantum chaos. Specifically, increasing the strength of $H_{n}$ induces a crossover from Poissonian to Gaussian Orthogonal Ensemble statistics in the Hermitian limit, and similarly triggers chaotic dynamics in the non-Hermitian case. Most remarkably, despite the onset of global chaos, we identify a tower of exact nonthermal eigenstates that evade thermalization. These states survive as robust quantum many-body scars, retaining low entanglement and coherent dynamics even under strong non-Hermitian perturbations. Our findings reveal a universal mechanism by which long-range and non-Hermitian effects reshape quantum ergodicity, offering new pathways for preserving quantum coherence in complex many-body systems.

With the rapid growth of the electric vehicles with drive systems with higher voltages, power outputs, frequencies, and speeds, mitigating electrically induced bearing damage (EIBD) in electric motors has become critical. In this study, a novel numerical model characterizing discharge-induced current density and voltage drop at the elastohydrodynamic lubrication line contact interface is presented. The current density and voltage drop constitute a linear complimentarily problem, which is efficiently solved using the conjugate gradient method. This paper sheds light on electrical characteristics at the inaccessible lubrication interface during discharge, highlighting the significance of roughness radius of curvature on current density. This numerical model lays the groundwork for future research on mitigating or even permanently solving EIBD problems in electric motor bearings.

The propagation of second sound, and more broadly the ballistic transport of heat, is of central importance in heat dissipation from electronic devices at very short length and time scales. Recently, we have developed thermal-response functions appropriate for elucidating physics beyond the diffusive regime, including time-dependent sources and wave-like heat propagation. The methods are applied to graphene simulated using molecular-dynamics (MD) with empirical potentials. The simulations predict a strong oscillatory transport at T=300K for length scales equal to L=68.1nm and below. It is shown that at these temperatures and scales, the lifetime of the oscillatory transport is determined largely by wave coherence connected to the phonon band structure. While most BTE theories for second sound neglect this effect, and may not be suitable at very short length scales, they nevertheless are accurate for describing perturbations at longer length scales. Calculations using the linearized BTE (LBTE) are also presented, along with analysis of second sound. This approach results in significantly longer lifetimes for second sound in comparison to our MD simulation results. Predictions for the response due to time-dependent sources are also presented, including insight into how time-dependent experiments might probe the spectra associated with second sound. Results are discussed in relation to recent experiments on graphite.

Intracellular processes often rely on the timely encounter of mobile reaction partners, including intermittently motor-driven organelles. The underlying cytoskeletal network presents a complex landscape that both directs particle movement and introduces quenched disorder through filament organization. We investigate the mean first encounter times for pairs of intermittently processive and diffusive particles, moving in two dimensions with and without a fixed filament network. In unstructured domains, increasing particle run-length enhances exploration of the domain, but tends to slow down the encounter times compared to equivalent diffusing particles. Encounters for long-running particles occur preferentially near the periphery, contrasting with bulk encounters for the purely diffusive case. When particles are unbiased in their runs along dense filament networks, encounters are shown to be well approximated by a continuum run-and-tumble model. For biased particles, regions of convergent filament orientation can serve as traps that slow the overall spatial exploration but can allow for faster encounter rates by funneling particles into regions of reduced dimensionality. These findings provide a framework for estimating intracellular encounter kinetics, highlighting the role of key physical features such as the effective diffusivity, run times, and network architecture.

This dissertation presents a systematic theoretical investigation into realizing a condensed matter analogue of the Chiral Magnetic Effect (CME) in a quasi-planar, 2+1D system. The research establishes a conceptual bridge between the anomalous transport phenomena of high-energy physics and the emergent electronic properties of engineered honeycomb lattices. The central objective is the formulation of a low-energy effective Hamiltonian that incorporates the necessary ingredients for a CME-like effect. This is achieved by moving beyond pristine graphene, whose inherent sublattice symmetry precludes the formation of a mass gap necessary for defining robust pseudo-chiral states. The core of this work is a model based on a honeycomb lattice with explicitly broken sublattice symmetry, which introduces a band gap and endows the quasi-particles system with a well-defined pseudo-chirality based on sublattice polarization. A time-reversal symmetry-breaking parameter is introduced to asymmetrically modify the valley gaps, creating a controllable non-equilibrium imbalance analogous to the chiral chemical potential in relativistic systems. A key finding is the validation of the physical model consistency; through commutator calculations, the total angular momentum - comprising both orbital and an emergent lattice spin component - is shown to be a conserved quantity. This research successfully transforms the abstract possibility of a 2D CME into a concrete, self-consistent theoretical framework, detailing the precise symmetry conditions required for its manifestation.

Accurate prediction of temperature evolution is essential for understanding thermomechanical behavior in friction stir welding. In this study, molecular dynamics simulations were performed using LAMMPS to model aluminum friction stir welding at the atomic scale, capturing material flow, plastic deformation, and heat generation during tool plunge, traverse, and retraction. Atomic positions and velocities were extracted from simulation trajectories and transformed into physics based two dimensional spatial grids. These grids represent local height variation, velocity components, velocity magnitude, and atomic density, preserving spatial correlations within the weld zone. A two-dimensional convolutional neural network was developed to predict temperature directly from the spatially resolved atomistic data. Hyperparameter optimization was carried out to determine an appropriate network configuration. The trained model demonstrates strong predictive capability, achieving a coefficient of determination R square of 0.9439, a root mean square error of 14.94 K, and a mean absolute error of 11.58 K on unseen test data. Class Activation Map analysis indicates that the model assigns higher importance to regions near the tool material interface, which are associated with intense deformation and heat generation in the molecular dynamics simulations. The results show that spatial learning from atomistic simulation data can accurately reproduce temperature trends in friction stir welding while remaining consistent with physical deformation and flow mechanisms observed at the atomic scale.

We develop an extended Onsager real-cavity framework to describe the Casimir–Polder interaction of small molecules dissolved in dielectric liquids near planar interfaces. By analytically resolving the geometry of the cavity opening, we derive closed-form expressions that capture the modification of the interaction as the molecule approaches a surface and connect smoothly to the asymptotic medium-assisted limit. Using experimentally established dielectric functions for water, propanol, and PTFE together with accurate molecular polarisabilities for O$_2$ and N$_2$, we compute the full distance-dependent potential for representative molecule–liquid–surface combinations. The results reveal how local-field screening, cavity geometry, and material response jointly determine both the magnitude and shape of the interaction, including the characteristic transition between open-cavity ($z\lesssim z_{\rm C}$) and closed-cavity ($z\gtrsim z_{\rm C}$) regimes. Beyond providing quantitative predictions, the framework offers an analytically transparent decomposition of dispersion forces in liquids, enabling a direct identification of the underlying physical contributions and an efficient exploration of parameter dependencies across different systems. The approach thus provides a useful baseline for interpreting dispersion interactions in complex environments within a continuum, local-field corrected description.

Curve-fold origami, composed of developable panels joined along a curved crease, exhibits rich dynamic behaviors relevant to metamaterials and soft robotic systems. Despite multiple approximated models, a comprehensive and exact dynamical theory for curve-fold origami remains absent, limiting the precise predictions of its dynamics, especially for those with wide panels. In this work, we develop an exact dimension-reduced theory that focuses on the dynamics of curve-fold origami, utilizing the intrinsic one-dimensional nature of developable surfaces. Starting from a single developable surface, we investigate the kinematics and kinetic energy of a moving developable surface. By overcoming the difficulty of describing the motion of local frames, we derive the exact velocity field of wide surfaces solely described by the motion of the reference curve, which leads to the kinetic energy of the entire surface. Owing to the one-dimensional feature, the Lagrangian of the system, composed of both kinetic and elastic energy, is a functional of the reference curve. Thus, we may variate the Lagrangian and derive a nonlinear dynamical theory for the reference curve, which comprises governing equations similar to the rod model but can precisely describe the motion of developable surfaces. The theory is validated consistently in both Lagrangian and Eulerian frameworks and is further extended to curved-fold origami modeled as a coupled bi-rod system. Utilizing our exact 1D model, we theoretically analyze the dynamical behaviors of various developable structures, revealing that the coupling of curvature and torsion along with the motion of local frames in our theory leads to the accurate modeling of arbitrarily deformed developable surfaces, which are validated by finite element analysis quantitatively.

Non-Markovian dynamics are ubiquitous across physics, biology, and engineering. Yet our understanding of non-Markovian processes significantly lags that of simpler Markovian processes, due largely to a lack of tractable models. In this article, we present a minimal model of non-Markovian dynamics in which the current state copies past states with arbitrary history dependence. We show that many properties of this process can be studied analytically, providing insight into the relationships between history dependence, autocorrelations, and information-theoretic metrics like entropy and dynamical information. Strikingly, we find that autocorrelations can fail, even qualitatively, to capture the underlying dependencies. Ultimately, this model serves as a tractable sandbox for exploring non-Markovian dynamics.

Non-Markovian stochastic processes are ubiquitous in biology. Nevertheless, we lack a general framework for quantifying historical dependencies. In this Letter, we propose an information-theoretic approach to decompose history dependence in systems with non-Markovian dynamics, quantifying the information encoded in dependencies of each order. In minimal models of non-Markovian dynamics, we show that this framework correctly captures the underlying historical dependencies, even when autocorrelations do not. In prolonged recordings of fly behavior, we find that the scaling of non-Markovian dependencies is invariant across timescales from fractions of a second to minutes. Despite this invariance, the overall amount of non-Markovian information is non-monotonic, suggesting a unique timescale on which historical dependencies are strongest.

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), an active learning framework where large language models autonomously design, execute, and interpret atomistic simulations. In MASTER, a multimodal system translates natural language into density functional theory workflows, while higher-level reasoning agents guide discovery through a hierarchy of strategies, including a single agent baseline and three multi-agent approaches: peer review, triage-ranking, and triage-forms. Across two chemical applications, CO adsorption on Cu-surface transition metal (M) adatoms and on M-N-C catalysts, reasoning-driven exploration reduces required atomistic simulations by up to 90% relative to trial-and-error selection. Reasoning trajectories reveal chemically grounded decisions that cannot be explained by stochastic sampling or semantic bias. Altogether, multi-agent collaboration accelerates materials discovery and marks a new paradigm for autonomous scientific exploration.

We consider a binary system of particles with repulsive interactions that move in opposite or perpendicular directions to each other under an applied external drive. For opposite driving, at higher drives a phase-separated laned state forms that has strong hysteresis in the velocity-force curve and the fraction of topological defects as the drive is cycled up and down from zero. The amount of hysteresis depends on the drive value at which the drive changes from increasing to decreasing. For perpendicular driving, we find a jammed state that transitions into a disordered state or a tilted lane state, both of which also show strong hysteresis effects. Additionally, a negative drag effect can appear in which one species moves in the direction opposite to the other species due to a tilting of the lanes by the perpendicular drive. When a constant drive is applied along one direction while the drive in the perpendicular direction is increased, we observe a series of drops and jumps in the velocity as the system forms locked and tilted laned states. For weakly interacting particles, the jammed system can show co-tilted stripe-forming states.

We present molecular dynamics simulations of the adsorption of mixed CO2-water vapors on a graphene flakes substrate, a model inspired by the microporous structure of activated carbons. Adsorption strength is quantified through a reduced energy measure that avoids ambiguities associated with defining adsorption regions. We find that CO2 adsorbs more strongly and more rapidly than water across all temperatures studied. Adsorption from wet vapors was observed to result in higher CO2 uptake compared with dry vapors at temperatures below 375 K. At intermediate temperatures, water molecules were seen to form clusters that interact with both the substrate and CO2, potentially promoting CO2 adsorption. Additionally, we observe that the GF substrate inhibits the formation of large water clusters, altering water aggregation dynamics near the surface. These findings highlight a cooperative adsorption mechanism in humid environments and suggest that graphene-flakes-based materials may perform effectively for carbon capture under realistic, moisture-containing conditions.

Lyotropic chromonic liquid crystals are formed by the self-assembly of aromatic compounds in concentrated solutions. Despite numerous applications of chromonic systems in optical and photonic devices, they all make use of the anisotropic linear optical properties of the nematic or columnar liquid crystalline phases. This paper extends the investigations of chromonic systems to the domain of nonlinear optics. For this purpose, the magnitude and sign of the nonlinear refractive indices, $n_2,$ were measured by the nonlinear ellipse rotation (NER) technique. This was performed on aqueous solutions of sunset yellow azo dye, the prototypical chromonic system. Samples with different concentrations and temperatures were used, both in the isotropic and nematic phases. In addition, the molecular aggregation states of the chromonic samples as a function of temperature and concentration were investigated by wide angle X-ray scattering. NER measurements as a function of the laser pulse width from $65\,fs$ to $\sim 5\,ps$ allowed the decomposition of $n_2$ into a fast contribution, $n_{2,fast},$ associated with molecular electronic processes, and a slow one $n_{2,slow},$ associated with molecular reorientational processes. It was shown that $n_{2,fast}$ doubled from the isotropic phases of the $15$ to the $30\,\%\,\text{w/w}$ samples, proportionally to the increase in mass fraction. However, $n_{2,fast}$ for the aligned nematic phase of $30\,\%\,\text{w/w}$ sample was higher than the double of the corresponding value for the $15\,\%\,\text{w/w}$ sample, showing an effect associated to the orientational order of this phase. Also, $n_{2,fast}$ was shown to depend linearly on temperature.

Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods to describe this scale exponentially with particle number; simpler mean-field approaches neglect essential two-particle correlations. The time-dependent two-particle reduced density matrix (TD2RDM) formalism offers a middle ground by propagating the two-particle reduced density matrix (2RDM) and closing the BBGKY hierarchy with a reconstruction of the three-particle cumulant. But the validity and existence of time-local reconstruction functionals ignoring memory effects remain unclear across different dynamical regimes. We show that a neural ODE model trained on exact 2RDM data (no dimensionality reduction) can reproduce its dynamics without any explicit three-particle information – but only in parameter regions where the Pearson correlation between the two- and three-particle cumulants is large. In the anti-correlated or uncorrelated regime, the neural ODE fails, indicating that no simple time-local functional of the instantaneous two-particle cumulant can capture the evolution. The magnitude of the time-averaged three-particle-correlation buildup appears to be the primary predictor of success: For a moderate correlation buildup, both neural ODE predictions and existing TD2RDM reconstructions are accurate, whereas stronger values lead to systematic breakdowns. These findings pinpoint the need for memory-dependent kernels in the three-particle cumulant reconstruction for the latter regime. Our results place the neural ODE as a model-agnostic diagnostic tool that maps the regime of applicability of cumulant expansion methods and guides the development of non-local closure schemes. More broadly, the ability to learn high-dimensional RDM dynamics from limited data opens a pathway to fast, data-driven simulation of correlated quantum matter.

In this book chapter, we review how systems of simple motile agents can be used as a pathway to intelligent systems. It is a well known result from nature that large groups of entities following simple rules, such as swarms of animals, can give rise to much more complex collective behavior in a display of emergence. This begs the question whether we can emulate this behavior in synthetic matter and drive it to a point where the collective behavior reaches the complexity level of intelligent systems. Here, we will use a formalized notion of "intelligent matter" and compare it to recent results in the field of active matter. First, we will explore the approach of emergent computing in which specialized active matter systems are designed to directly solve a given task through emergent behavior. This we will then contrast with the approach of physical reservoir computing powered by the dynamics of active particle systems. In this context, we will also describe a novel reservoir computing scheme for active particles driven ultrasonically or via light refraction.

Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to assess material properties, ideally based on a single measurement. Here, ML models are developed to predict magnetization from X-ray diffraction (XRD) for iron oxide nanoparticles. Our approach is to first develop a set of simulated data that links modulated XRD, based on a crystallographic information file (CIF), to a simple magnetic model to determine magnetization at a given magnetic field, thereby enabling us to train Random Forest and Gradient Boosting regression models on a large amount of simulated data. The models are validated by synthesizing iron oxide nanoparticles and measuring their crystal structure via XRD and room-temperature magnetization curves. In doing so, we can fine-tune both the training hyperparameters and the optimal size of the simulated datasets used to train the models. Through this optimization, the best models can achieve an $R^2$ greater than 0.9 for five experimental samples, used for tuning, for predicting the max magnetization (at 2.8 T) of the measurement. Lastly, we demonstrate reasonable predictions on the full magnetization vs. magnetic field curve, showing that the RF model excels at predicting the high magnetic field values, which is key for determining the success of an iron oxide nanoparticle synthesis for applications like magnetic particle imaging (MPI), thermal magnetic particle imaging (T-MPI), and hyperthermia.

We consider a prime example of simulating interacting relativistic QFT with cold atoms: the realisation of the sine-Gordon model by tunnel-coupled quasi-1D Bose gases. While experiments have shown that it can realise the sine-Gordon model in equilibrium, studies of non-equilibrium dynamics have revealed phase-locking behaviour that contrasts with predictions from sine-Gordon field theory. Here, we examine a one-dimensional field-theoretic model of the system and find that the phase-locking behaviour can be understood in terms of the longitudinal harmonic trap, and that the additional degrees of freedom observed in the experiment do not appear to play a significant role. Therefore, the experimental setup provides a good simulator of the sine-Gordon quantum field theory, even out of equilibrium, if the inhomogeneous background induced by the trap is taken into account. Furthermore, our results support the idea that modifying the longitudinal trap to a box shape should result in agreement with standard sine-Gordon dynamics. The main remaining open issues are accounting for 3D corrections and modelling the effect of the boundaries.

Van der Waals (vdW) heterostructures combining two-dimensional (2D) ferromagnets and semiconducting transition-metal dichalcogenides (TMDCs) offer highly promising opportunities for tailoring 2D magnetism through interfacial proximity effects, enabling unique physical phenomena inaccessible in 3D systems and achieving functionalities beyond conventional spintronics. However, current fabrication of vdW heterostructures still relies heavily on the manual stacking of exfoliated 2D flakes, leading to critical challenges in scalability, interfacial quality, thickness control and device integration. This work reports on the realization of all-epitaxial, high-quality Fe5-xGeTe2(FGT)/WSe2 heterostructures exhibiting perpendicular magnetic anisotropy (PMA) and room-temperature ferromagnetism. The FGT/WSe2 system demonstrates temperature-driven magnetic transitions, higher-order PMA contributions and large anisotropic magnetoresistance, highlighting sublattice-specific contributions to magnetic and transport properties. Notably, the FGT/WSe2 heterostructures display unconventional physical phenomena, including thickness- and temperature-dependent sign reversal of exchange bias, a reversed thickness trend in the unconventional Hall effect, and a non-monotonic PMA-thickness dependence. These anomalies indicate pronounced interfacial contributions arising from proximity effects enhanced by epitaxial interface quality. Collectively, this study provides deep insights into the magnetic and transport properties of FGT/WSe2 vdW heterostructures, establishing a scalable platform for exploring emergent 2D physics and advancing next-generation 2D spintronic technologies.

We propose a renormalization group (RG) approach to compare and collapse eigenvalue densities of random matrix models of complex systems across different system sizes. The approach is to fix a natural spectral scale by letting the model normalization run with size, turning raw spectra into comparable, collapsed density curves. We demonstrate this approach on generalizations of two classic random matrix ensembles–Wigner and Wishart–modified to have power-law variance profiles. We use random matrix theory methods to derive self-consistent fixed-point equations for the resolvent to compute their eigenvalue densities, we define an RG scheme based on matrix decimation, and compute the Beta function controlling the RG flow as a function of the variance profile power-law exponent. The running normalization leads to spectral collapse which we confirm in simulations and solutions of the fixed-point equations. We expect this RG approach to carry over to other ensembles, providing a method for data analysis of a broad range of complex systems.

One of the recurring hurdles in studying unconventional superconductivity is the challenge of efficiently and conclusively identifying the symmetry of the superconducting order parameter in a new material. Uranium ditelluride (UTe$_2$) exhibits an unprecedented number of superconducting phases as a function of pressure and magnetic field, each presumably characterized by a different symmetry of the superconducting gap function. None of these phases has had its symmetry conclusively identified so far. In this article, we report results of an extensive study of the thermal conductivity of UTe$_2$ in its low-field, low-temperature superconducting state as a function of the angle of an applied magnetic field rotated in the $b$-$c$ plane. We observe clear and substantial oscillations in the thermal conductivity as a function of field angle, which naturally suggests the existence of point nodes in the gap. Utilizing the experimentally determined Fermi surface, we are able to model this phenomenon for all the potential gap structures in UTe$_2$ and positively identify the location of these nodes as being along the crystallographic $b$-axis, implying that the superconducting order parameter belongs to the $B_{2u}$ irreducible representation of the crystal point group. The clarity of this result will accelerate the identification of other superconducting phases in UTe$_2$, and guide future studies through the use of high resolution field-angle-dependent measurements.

We study a nonreciprocal generalization [EPL 60, 418 (2002)] of the classical Heisenberg spin chain, in which the exchange coupling is nonsymmetric, and show that it displays a ballistic spreading of chaos as measured by the decorrelator. We show that the interactions are reciprocal in terms of transformed variables, with conserved quantities that can be identified as magnetization and energy, with a Poisson-bracket algebra and Hamiltonian dynamics. For strictly antisymmetric couplings in the original model the conserved quantities diffuse, the decorrelator spreads symmetrically, and a simple hydrodynamic theory emerges. The general case in which the interaction has symmetric and antisymmetric parts presents complexities in the limit of large scales. Ballistic propagation of chaos survives the inclusion of interactions beyond nearest neighbours, but the conservation laws in general do not.

Recently, nanocrystals in the regime of vanishing quantum confinement-termed bulk nanocrystals (BNCs)-have demonstrated remarkable optical gain characteristics. While their high-power lasing performance was demonstrated convincingly, the photophysics at low and intermediate powers-where charge-carrier populations are discrete-remain unexplored. Using single-photon avalanche diode (SPAD) array technology, we resolve the dynamics and energetics of six multi-carrier excited states in individual CdSe/CdS core/shell BNCs, containing up to four electrons and two holes. Each state exhibits bimodal emission, indicative of thermal equilibrium between closely spaced electron and hole levels, confirmed via temperature-dependent single-particle measurements. Quantification of radiative and nonradiative decay channels reveals strongly suppressed Auger recombination through both the negative- and positive-trion pathways. We present a model that combines statistical scaling of rate constants with Fermi-Dirac thermal occupations of electron and hole levels, bridging the transitional regime between quantum-confined and bulk nanocrystals, and providing a comprehensive framework for understanding this emerging class of materials.

Classical autoassociative memory models have been central to understanding emergent computations in recurrent neural circuits across diverse biological contexts. However, they typically neglect neuromodulatory agents that are known to strongly shape memory capacity and stability. Here we introduce a minimal, biophysically motivated associative memory network in which neuromodulator-like signals are modeled by a self-adaptive, activity-dependent gating mechanism. Using many-body simulations and dynamical mean-field theory, we show that such gating fundamentally reorganizes the attractor structure: the network bypasses the classical spin-glass transition, maintaining robust, high-overlap retrieval far beyond the standard critical capacity. Mechanistically, when the gates act as fully open or fully closed switches, the gate stabilizes transient "ghost" remnants of stored patterns even far above the Hopfield limit, converting a single memory fixed point attractor into a cluster of fixed points. The retrieved state is set by the initial overlap rather than quantized to the stored pattern. This graded dependence may be exploited for input-driven computation with persistent activity. These results establish that neuromodulation-like gating alone, without modifying synaptic weights or introducing explicit higher-order couplings, can dramatically expand memory capacity, eliminate catastrophic breakdown, and reshape a discrete attractor landscape into a rich continuum, suggesting a general and biologically accessible route to enhanced computational capabilities in neural circuits and neuromorphic architectures.

Shell-shaped Bose-Einstein condensates (BECs) represent a paradigmatic instance of quantum fluids in hollow geometries exhibiting phenomena that bridge from ultracold atomic to astrophysical realms. In this work, we present a comprehensive survey of the dynamics, thermodynamics, and collective excitations of shell-shaped BECs, synthesizing two decades of our group's theoretical work in light of recent experimental breakthroughs. We begin by analyzing the evolution of a BEC from filled-sphere to hollow-shell geometries, illustrating the necessity of microgravity conditions to avoid gravitational sag. We then analyze the collective mode structure across this evolution and pinpoint a universal dip in the frequency spectra as well as mode reconfiguration due to inner-surface excitations as robust signatures of the hollowing-out transition. Turning to vortex physics, we show that the closed-surface topology enforces vortex-antivortex configurations in shell-shaped BECs and that the natural annihilation of these pairs can be stabilized by rotation, with the critical rotation rate depending on shell thickness. In the thermodynamic domain, we investigate the interplay between shell inflation and the BEC phase transition, where adiabatic expansions lead to condensate depletion. This behavior motivates a study of the nonequilibrium dynamics of shell-shaped BECs; we perform such a study by constructing a time-dependent dynamic technique that can capture the evolution in both adiabatic and non-adiabatic regimes. Finally, we review recent experimental realizations of shell-shaped BECs, including the landmark creation of ultracold shells aboard the International Space Station, and outline prospects for exploring quantum fluids in curved geometries.