Papers

In core/shell quantum dots (QDs), the interface between semiconductors of different chemical character largely determines their optoelectronic properties. In III-V/II-VI systems, this boundary involves pronounced chemical and electronic discontinuities that can generate trap states even under complete surface passivation. Using density functional theory on atomistic models of InAs/CdSe QDs, we systematically reconstruct atomic arrangements at the surface and interface to evaluate how local coordination and interfacial dipoles influence the electronic structure. Abrupt interfaces induce charge imbalance and band-gap collapse, whereas introducing an alloyed interlayer that mixes core and shell atoms and vacancies restores energetic alignment and yields delocalized band-edge states, consistent with experimental findings. We also introduce a charge-flow analysis that quantifies charge redistribution across the QD, providing a framework for realistic modeling of interlayer formation and predictive design of defect-free interfaces in core@shell architectures.

Dynamical quantum phase transitions (DQPTs) occur at times when a quantum state exhibits a nonanalytic change in its return probability. This can be viewed as the probability of collapsing the evolved state to the initial state by quantum measurement. However, the initial wave function usually has exponentially small amplitude in the late time evolved state. Here we perform statistical characterization for all the possible post-measurement states distributed according to the Born's rule, by sampling a one-dimensional quantum Ising chain after a quantum quench dynamics. The statistical ensemble can also be viewed as a mixed state when the time evolved state is subjected to maximally dephasing noise in a certain basis. We map the distribution to a statistical model and characterize its effective "energy" spectrum, and introduce the average dynamical free energy, establishing a framework for the statistical DQPTs. We show the recovering of DQPT under high-moment average and a delocalized level distribution following critical times. Through analytic continuation into the complex time plane, we demonstrate the vanishing of Yang-Lee-Fisher zeros and the emergent level crossing near critical times. Finally, we propose a measurement-based quantum computation protocol to simulate the unitary evolution via single-qubit measurements on a two-dimensional cluster state. Our results provide a way for experimentally investigating statistical DQPTs in quantum devices, shedding light on the structured circuit sampling with insights from DQPT and generalizing the understanding of mixed state due to decoherence beyond equilibrium.

Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increase in autocorrelation times near phase transitions. Although deep learning approaches, such as neural-network-based samplers, have been proposed to alleviate this issue, they face another serious problem: the difficulty of training the models. This difficulty partially stems from the overly general nature of original machine-learning architectures, which often ignore underlying physical symmetries and force networks to relearn them from scratch. In this paper, we demonstrate that incorporating physical priors into the model significantly enhances performance. Building upon existing strategies that integrate spin-spin interactions, we propose a framework that utilizes a prior probability distribution as a starting point for training. Our results for the Ising model, as well as for the Edwards-Anderson spin glass model, suggest that moving away from `blank slate' models in favor of physics-informed priors reduces the training burden and facilitates the simulation of larger system sizes in discrete spin models.

We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (MEAM), and a neural network-based machine-learned model in the form of an ephemeral data-derived potential (EDDP). Using nested sampling and replica-exchange nested sampling simulations, we computed thermodynamic and structural properties at pressures up to 60 GPa, mapping both melting behaviour and solid-phase stability. Both the EAM and MEAM models predict the face-centred cubic (FCC) phase to remain stable up to approximately 60 GPa. In contrast, the EDDP model captures the experimentally-observed FCC-to-hexagonal close-packed (HCP) transition at around 15 GPa. These results highlight the importance of training data and model flexibility in accurately describing high-pressure phase behaviour, and demonstrate the effectiveness of nested sampling as a robust framework for exploring phase stability in materials. Particularly, the combination of nested sampling with modern machine-learned interatomic potentials - delivering near ab initio accuracy at tractable cost - opens the door to truly predictive and exhaustive exploration. EDDPs trained on diverse, out-of-equilibrium configurations appear particularly well suited to this task, offering a robust and transferable framework for unbiased phase discovery.

The product formula, commonly known as Trotter decomposition, is a central tool for digital quantum simulation, whose performance depends critically on how the Hamiltonian is partitioned into tractable blocks. Standard decompositions typically rely on direct commutativity among Hamiltonian terms in a chosen operator representation, which can lead to large residual errors and deep circuits for complex, practically relevant many-body quantum systems. We address this fundamental bottleneck by introducing a new decomposition principle that goes beyond commutativity, grouping Hamiltonian terms into local three-site clusters according to the underlying SU(2) symmetry of the local dynamics. We show that three-site generators fall into at most four SU(2)-symmetry classes, each admitting an effective two-qubit SU(4) representation with exact and efficient implementations. By reducing the number of clusters, this decomposition principle substantially suppresses commutator-induced errors and circuit overhead while preserving underlying physical structures that commutativity-based decompositions may violate. We demonstrate the proposed method on several physically relevant spin-lattice models, where the reduced cluster structure can even realise the second-order product formula without doubling the circuit depth, as would be required by conventional decompositions. Numerical simulations of a Kagome Heisenberg model with triangular spin-chirality interactions show that the proposed method reduces both state infidelity and average spin-chirality bias by more than three orders of magnitude compared with conventional decompositions, while using substantially fewer gates. These results establish local symmetry as a flexible and practical design principle for product-formula simulation, opening a route to more accurate and hardware-efficient simulations of broader classes of many-body systems.

BEDT-TTF-based organic conductors host a number of ground states, tuned by electron repulsion from Mott and charge ordered insulators to superconductors. Knowing charge distribution on the molecular sites in the insulating state of these materials is a key to understanding the origin of these ground states. We survey and discuss the C=C stretching modes in BEDT-TTF based molecular conductors. These molecular vibrations are extremely crucial in characterization of charge-ordered insulators, and are recently linked to superconductivity in some compounds. Focusing on the known examples of BEDT-TTF$^{+0.5}$ salts, we analyse the reliability of the C=C stretching modes for the determination of charge ordering and absolute site charge. Considering the charge-ordered states, a prominent shift in frequency of 141 cm$^{-1}$ per elementary charge $e$ for $\nu_{27}(b_{1u})$ and 98 cm$^{-1}$$e$ for $\nu_2$($a_g$) can be clearly realised, however, the distribution resulting from different compounds span over 20 cm$^{-1}$. For nominal BEDT-TTF$^{+0.5}$ compounds, the distribution of the resonance also extends around 20 cm$^{-1}$, yielding an unexpected large uncertainty of $\Delta\rho~\approx~(~\pm~0.045)e$, which is presumably due to the influence of small differences in the structure. This highlights the limitations of charge-frequency relations to detect small deviations in absolute charge values on molecular lattice sites, and emphasises on the use of the relations to estimate charge-ordering, rather than absolute site charge.

We derive the canonical-ensemble scaling of Tan's contact for $N$ harmonically trapped Tonks–Girardeau bosons at finite temperature in the large-$N$ limit. The leading scaling coefficient reproduces the local-density-approximation result and is obtained from a contour-integral representation of the canonical partition function followed by a saddle-point reduction to a phase-space integral with a self-consistent scaled chemical potential. The subleading coefficient is the central new object of this work: it admits an explicit representation in terms of universal phase-space integrals of the Fermi factor, having closed-form Sommerfeld and virial limits, and is identified with the canonical-versus-grand-canonical ensemble difference at fixed mean particle number. In the high-temperature Boltzmann regime the ratio of subleading to leading coefficients collapses to a universal value, traceable to the Poissonian particle-number statistics of the dilute grand-canonical gas. We construct Padé approximants for both scaling functions that interpolate uniformly between the low-temperature Sommerfeld and high-temperature virial regimes; for the subleading coefficient we report a form that is uniformly accurate on our working range of temperatures and asymptotically correct beyond. The scaling law is verified against canonical contour-integration data across the full temperature range, and the experimental visibility of the ensemble correction is quantified: for the mesoscopic particle numbers and temperatures of current one-dimensional experiments it reaches the several-percent level.

We introduce thermodynamic networks, a general framework for autonomous, physics-based computation using non-equilibrium steady states. These networks are modeled as a collection of finite-size reservoirs that exchange conserved quantities–such as electric charge or molecular number–while relaxing to a non-equilibrium steady state, which encodes the solution of a computational problem. We identify Negative Differential Conductance (NDC) as the critical physical property governing the computational expressivity of the thermodynamic network. While networks lacking NDC are restricted to computing monotonic functions, the presence of NDC enables universal function approximation. For the training of the network, we use protocols that take advantage of the natural tendency of the system to equilibrate. We illustrate the versatility of our approach via two different platforms: quantum dot networks and enzymatic reaction networks. Both systems can be engineered to have NDC, enabling high performance in standard benchmarks, including sine function approximation and MNIST digit classification. Overall, our work establishes a rigorous link between non-equilibrium steady states and computational expressivity.

Recently, nonequilibrium orbital angular momentum in low-dimensional systems has attracted renewed attention. Here we introduce a minimal three-orbital tight-binding model for a single helical chain and show that chirality alone generates a momentum-dependent orbital-angular-momentum texture through Slater–Koster hybridization in the local basis $(p_r,p_\phi,p_z)$, without requiring atomic spin–orbit coupling. In the single-helix geometry, the radial orbital texture vanishes identically, while the azimuthal and longitudinal components remain finite and arise from the odd-in-momentum $(p_z,p_r)$ and $(p_r,p_\phi)$ sectors. As a result, the equilibrium average orbital texture vanishes by parity, although persistent-like orbital angular momentum currents may still exist and imply chirality-dependent end magnetization in a finite helix. Under an applied longitudinal electric field, the system develops a finite orbital Edelstein response, whereas the projected longitudinal orbital-current conductivity vanishes in the linear regime by parity. When spin degrees of freedom are included, the orbital texture acts as a source of spin polarization through orbital-to-spin transduction. The resulting spin response is controlled by orbital overlap scales much larger than the bare relativistic spin–orbit scale, making it a stronger candidate for spin injection than the conventional spin Edelstein mechanism. These results identify chirality as the minimal microscopic ingredient for generating orbital angular momentum response in one-dimensional systems and support an orbital route to spin selectivity in chiral conductors.

The Lieb-Schultz-Mattis (LSM) theorem and its higher-dimensional extensions forbid the existence of a unique, symmetric, and gapped ground state at fractional fillings in quantum many-body systems with a conserved particle number (or spin angular momentum) and the conventional translation symmetry of Euclidean lattices. In this work, we propose a generalization of the LSM theorem to quantum many-body systems on hyperbolic lattices, i.e., regular tessellations of two-dimensional negatively curved space. By leveraging concepts from hyperbolic band theory in a many-body setting, we adapt Oshikawa's flux-threading argument to periodic hyperbolic lattices with a non-Euclidean (Fuchsian) translation symmetry and compute a lower-bound to the ground-state degeneracy as a function of filling and lattice geometry. We explore the consequences of LSM constraints for gapped phases of hyperbolic quantum matter and suggest frustrated spin models on hyperbolic analogs of the square and triangular lattices as promising platforms for realizing symmetric spin liquids in hyperbolic space.

Classical transport equations with probabilistic initial conditions can be viewed as quantum systems. In a discrete version they are probabilistic automata. The time-local probabilistic information is encoded in a classical wave function. Its unitary evolution obeys a Schrödinger equation. Statistical observables measure properties of the classical probability distribution. In the quantum formalism they are represented by operators which do not commute with the ones associated to classical observables. Momentum, quantum energy and angular momentum or charges yield conserved quantities which constrain the evolution of the classical probability distribution. The characteristic features of quantum mechanics, as the superposition of wave functions, interference, the importance of phases, non-commuting operators or a unitary time evolution, are realized by probabilistic classical transport equations. Stochastic transport equations are described by a quantum system with a stochastic Hamiltonian.

A systematic study of chiral effects is presented using an Effective Field Theory framework. By integrating out a massive Dirac fermion at finite temperature in presence of vector and axial background fields, the currents and their anomalies are computed from the path-integral. Chiral effects previously considered separately naturally arise in a unified computation, including new mass corrections. The link between each anomalous transport effect and the anomalies is clearly established, beyond the identification of their coefficients. In particular, we can appreciate how these effects are sourced by the anomalous nature of the theory even in configurations where the anomaly itself vanishes. The consistent and covariant anomalies are both encapsulated in master formulae for the currents which result from a careful treatment of the regularisation. It is finally found that, at finite temperature, the physical currents cannot be inferred simply from the Chern-Simons terms whose divergence reproduce the chiral anomalies.

Crossed Andreev reflection (CAR) is a nonlocal quantum transport phenomenon that arises at the interfaces between a superconductor and two spatially separated metals. In this process, an electron incident from one metal combines with another electron originating from the other metal to form a Cooper pair in the SC. In this work, we theoretically investigate the quantum transport properties of a device comprising a central $s$-wave superconductor sandwiched between two bipolar magnetic semiconductor (BMS) leads. BMS materials possess a unique bandstructure where the conduction and valence bands are fully spin-polarized in opposite directions. By applying independent gate voltages to the two BMS leads, we selectively tune their chemical potentials to configure an antiparallel spin alignment at the Fermi energy. In the absence of spin-orbit coupling (SOC), this specific configuration perfectly suppresses both local Andreev reflection and direct electron tunneling (ET), thereby isolating and profoundly enhancing CAR. The introduction of Rashba SOC, however, induces spin-flip scattering that relaxes these strict selection constraints, yielding finite probabilities for competing scattering channels and subsequently reducing the overall CAR efficiency. Nevertheless, for SOC strengths below a critical threshold, CAR remains the dominant transport mechanism over ET. This proposed architecture provides a scalable, stray-field-free method to achieve purely electrostatic control over nonlocal transport, advancing the development of highly efficient superconducting spintronic devices.

In this paper, the Bak–Tang–Wiesenfeld model for various substrate topologies and a variety of neighborhoods is reconsidered. With computer simulation, we study the distribution of avalanche sizes. Using the Z-score we confirm that independently of the substrate topology and the range of neighborhood, the exponent that governs the power law of the probability distribution of the size of avalanches is the same and approximately equal 1.208(39). However, this requires a smartly chosen number of deposited grains in relation to the linear size of the system.

We study the abelian sandpile model in two dimensions on a directed cylindrical lattice with periodic transverse boundary conditions in the transverse direction and dissipation at one boundary. Recurrent configurations form a finite abelian group, and repeated grain addition at a specific site generates deterministic dynamics on this group. Using Dhar's formulation, the sandpile group is identified with the co-kernel of the reduced directed Laplacian. We show that the group structure admits an exact reduction to a transverse problem, allowing complete determination of its cyclic decomposition. Our results establish a direct connection between the algebraic structure of the sandpile group and the periodicity of the driven dynamics, establishing the manner in which the underlying algebraic structure governs both deterministic and stochastic evolution in directed sandpile.

Subcellular protein complexes and organelles exhibit diverse dynamic behaviors that reflect the mechanical constraints and organization of the intracellular environment. Although some structures follow classical Brownian motion, many display anomalous dynamics. The transitions between these regimes are increasingly recognized as critical for subcellular organization, yet how they influence pattern formation remains unclear. Here, we investigate the spatial arrangement of cilia on the apical surface of multiciliated cells (MCCs) in developing Xenopus laevis embryos, where coordinated ciliary beating depends on the precise organization of hundreds of centriole-derived basal bodies (BBs). Using quantitative confocal, high-resolution and high-speed TIRF imaging together with theoretical modeling, we show that BB trajectories undergo time-resolved transitions between diffusive and anomalous motion, with distinct regimes that correlate with apical surface expansion. During the early stages, actin remodeling facilitates the dispersal of BBs by providing a permissive, low-confinement environment. As development progresses, the actin network becomes increasingly cross-linked that constrains BB movement and promotes uniform spacing across the apical domain. Disruption of $\alpha$-actinin-1, a major actin cross-linking protein, impairs the integrity of the apical actin meshwork, weakens BB confinement, and disrupts regular spatial patterning, ultimately compromising the arrangement of BBs required for proper cilia alignment. Together, we show that progressive apical actin cross-linking coordinates BB positioning and regulates their dynamic state, guiding the shift from diffusive to confined motion. This transition in dynamics enables the emergence of a uniform BB pattern, which in turn ensures the aligned deployment of motile cilia necessary for effective directional fluid flow.

We show that the late-time dynamics of a minimal three-component mass-conserving reaction–diffusion system reduce to a scalar active field theory, Active Model B$^-$ (AMB$^-$), in which a density-dependent interfacial coefficient $\kappa(\phi)$ turns negative at high density. This drives a finite-wavelength instability and stabilises microphase-separated patterns, in contrast to the unbounded coarsening of two-component mass-conserving systems. Unlike Active Model B$^+$, AMB$^-$ retains a chemical potential that remains a state function, inherited from the underlying conservation law, but admits no equation of state for the pressure.

The performance of tip-enhanced optical microscopy is often limited by inefficient coupling of the excitation field to the plasmonic tip apex, as well as by thermal drift and optical aberrations. Here, we demonstrate that adaptive wavefront shaping based on Zernike mode provides a practical approach to achieving robust near-field optimisation at the tip apex. Using a sequential feedback algorithm, initially using the near-field signal, we narrow the illumination point-spread function and suppress sidelobes. This demonstrates that Zernike-mode control can be used for both aberration correction and field engineering. In tip-enhanced Raman measurements of a Janus MoSSe monolayer, conventional near-field optimisation increases the signal intensity by around 1.4 fold. A second optimisation step based directly on the Raman-band intensity yields a further 5 to 15 fold enhancement, depending on the specific tips used. These results establish a systematic, optics-based strategy for optimising tip fields, providing a transferable framework for improving tip-enhanced and related near-field spectroscopies.

Variational neural network models have achieved remarkable success in solving ground-state problems of quantum many-body systems. However, addressing classical and quantum spin glasses remains challenging, as disorder and energy frustration give rise to an exponentially large number of local energy minima separated by high-energy barriers, hindering the efficiency of conventional Metropolis-based Monte Carlo methods. To bridge this gap, we introduce Deep Boltzmann Quantum States, a class of neural quantum states inspired by deep Boltzmann machines that inherit efficient block Gibbs sampling. We also propose two key advances in the training algorithm. Firstly, we combine natural-gradient updates with state-of-the-art stochastic optimizers. Secondly, we gradually tune the hardness of the problem Hamiltonian by interpolating from an easy to a hard regime, without the need to closely approximate the instantaneous adiabatic state at intermediate times. We match the exact solution or the best available estimate for several instances of classical and quantum Ising spin-glass models with infinite-range interactions and hundreds of spins. We also solve instances of the NP-hard Job Shop Scheduling Problem exceeding the current limitations of quantum annealing hardware. To summarize, deep neural architectures with efficient global update rules and trained within an annealing-like scheme, provide a powerful framework for solving real-world hard combinatorial optimization and for investigating disordered quantum many-body systems.

The complex eigenvalues of Markov generators govern oscillatory properties of relaxation, autocorrelation, and linear response. We show that these eigenvalues are localized by nonequilibrium cycles of the generator, thus revealing a fundamental tradeoff between thermodynamic driving, oscillation, and decay of eigenmodes. Specifically, we prove that each complex eigenvalue is confined to a region determined by the cycle affinity and the eigenvector “winding number” of some nonequilibrium cycle. In discrete and continuous unicyclic systems, we also demonstrate that the winding number is determined by the ordered eigenvalue index, yielding new thermodynamic bounds on the slowest and fastest relaxation modes. In discrete multicyclic systems, our approach unifies and extends several previous inequalities and proves the Uhl–Seifert ellipse conjecture.

Precise control of magnetic domain formation at the nanoscale remains constrained by stochastic defect-mediated and unstable pinning, limiting scalability and reproducibility in spintronic architectures. Here we demonstrate that spatially engineered anisotropy gradients provide a deterministic alternative. Using focused Ga+-ion irradiation, we pattern magnetic energy landscapes containing nanoscale "anisotropy wells" that confine magnetic domain walls and enable bidirectional sequential switching without reliance on difficult-to-control material disorder. An analytical framework describing domain-wall energetics in graded anisotropy profiles yields predictive design rules for depinning and stability, which are supported by micromagnetic simulations and experiments. We realize programmable multi-domain configurations in continuous ferromagnetic films and demonstrate robust, reproducible switching of 750 nm regions, while first results for 100 nm are shown, approaching the theoretical limit set by the domain-wall width. By replacing unstable pinning with engineered energy landscapes, this anisotropy landscape establishes a scalable materials strategy for deterministic magnetic-state programming and opens a pathway toward dense, energy-efficient spintronic and reconfigurable magnetic nanodevices.

Exact firing rate models, also known as next-generation neural mass models (NG-NMMs), provide a rigorous description of the dynamics of neural populations. While in its simplest form a single population only displays fixed-point activity, multi-population models may display a range of different behaviors. In this work, we study the dynamics of all-excitatory or all-inhibitory NG-NMMs coupled through sparse random networks with row-normalized network topology. Linear stability analysis of the homogeneous states of the system, representing asynchronous neural activity, provides a dispersion relation linking the emergence of spatiotemporal dynamics to the spectra of the connectivity matrix. Using bounds from random matrix theory, we identify the parameter regions where instabilities occur. In undirected networks, only inhibitory systems produce heterogeneous stationary patterns, corresponding to a winner-takes-all mechanism. In directed networks, exotic rhythmic states with high frequencies emerge in both, excitatory and inhibitory systems. Numerical simulations reveal that these hectic oscillatory states correspond to high-dimensional chaos with extensive properties.

Wegner's $\mathbb{Z}_2$ gauge model is the earliest formulation of pure lattice gauge theory and predicts the topological nature of the confinement-deconfinement transition. In three dimensions ($D=3$), the equilibrium critical behavior of the model is understood in terms of geometrically defined objects, namely loop excitations and Fortuin-Kasteleyn (FK) clusters. This work investigates the critical quench dynamics of this model from a geometric perspective, following quenches from both a high-temperature percolation phase and the zero-temperature ground state. Using time-dependent finite-size scaling analysis, we find that the critical non-equilibrium relaxation of the percolation order parameter is governed by a dynamical exponent $z_{\rm p} \simeq 2.6$, consistent with that associated with the energy density, $z_{\rm c}$. Importantly, the value of $z_{\rm p}$ is robust with respect to the initial quench condition and the choice of geometrical objects. Furthermore, we provide a detailed characterization of the kinetics of different geometrical objects during the evolution from the percolation phase. Notably, we observe that the quench dynamics obeys dynamic scaling in terms of a growing lengthscale, $\xi_{\rm p}(t) \sim t^{1/z_{\rm p}}$, despite the absence of a local order parameter.

We show theoretically that a superconducting critical temperature can be effectively increased in a two-compound solid solution producing high-entropy mixture of electrons. In order to employ the electron entropy of mixing into the superconducting phase dynamics, we suggest to use a quasiparticle trap that removes quasiparticle excitations from the superconductor. This makes the concentration of Cooper pairs a dynamic variable of the entropy of mixing, and thus affects the Ginzburg-Landau functional of the superconductor effectively reducing the first expansion coefficient or, in other words, increasing the critical temperature.

The honeycomb cobaltate Na$_2$Co$_2$TeO$_6$ has recently been proposed as a candidate material for hosting field-induced quantum spin liquid (QSL) behavior. Here, we present a comprehensive thermodynamic study of its low-temperature, high-field phase diagram using magnetization, specific heat, and magnetocaloric-effect measurements down to 1 K. In zero field, we observe a weak residual moment that provides further insight into the nature of the magnetic ground state. For in-plane magnetic fields ($B \parallel a^*$), we identify three field-induced transitions at $B_{c1} \simeq 6$ T, $B_{c2} \simeq 7.8$ T, and $B_{c3} \simeq 10.4$ T. The magnetic Grüneisen parameter and specific heat reveal clear thermodynamic signatures of these successive phase transitions enclosing two intermediate phases. Contrary to expectations for a field-induced QSL, the phase between $B_{c2}$ and $B_{c3}$ lacks enhanced magnetic entropy but instead shows behavior consistent with a distinct ordered state. Above $B_{c3}$, the absence of additional anomalies indicates a crossover to a conventional spin-polarized regime. Our results place stringent thermodynamic constraints on the proposed QSL scenario in Na$_2$Co$_2$TeO$_6$, calling for further microscopic investigations to establish the precise nature of the field-induced phases.

Two-dimensional Ruddlesden-Popper lead halide perovskites provide a valuable platform for tailoring charge and spin properties through quantum confinement and reduced symmetry. While the electron and hole Landé $g$-factors in bulk lead halide perovskites exhibit a universal dependence on the band gap energy, their evolution in two-dimensional perovskites has remained largely unexplored. Here, the Zeeman splittings of electrons and holes in (PEA)$_2$MA$_{n-1}$Pb$_n$I$_{3n+1}$ perovskites with the number of inorganic layers ovarying in the range $n=1,...,8$ are measured by means of the spin-flip Raman scattering and time-resolved Kerr rotation magneto-optical techniques. A systematic evolution of the electron and hole $g$-factors with decreasing layer thickness, which deviates from the universal bulk behavior and reveals confinement-driven trends similar to those observed in perovskite nanocrystals, is found. The experimental results are in good qualitative agreement with empirical tight-binding calculations. The exciton $g$-factors are evaluated from the Zeeman splittings of the exciton resonances in reflectivity measured in pulsed magnetic fields up to 55~T. These results provide comprehensive insight into the spin properties of two-dimensional lead halide perovskites and establish them as a tunable platform for engineering spin-dependent phenomena in quantum-confined semiconductors.

In typical single-molecule force spectroscopy experiments the mechanical unfolding of molecular complexes or biomolecules is studied applying a force ramp to one end of the system while the other end is kept fixed in space. The computational counterpart of this type of experiments can routinely be performed using molecular dynamics simulations with atomistic resolution. However, due to the large difference in time scales often coarse graining procedures are applied in the simulations. Most of the applied techniques do not allow to follow the atomistic details of the relevant conformational transitions due to the structural simplifications used to speed up the simulations. Here, we apply an earlier developed dynamic coarse graining technique based on Markov state modeling to a model peptidic system that does not unfold in a simple two-state manner. Using the donor-acceptor distances of the helical hydrogen bonds as collective variables and performing a dimension reduction technique allows us to construct a Markov model of the unfolding process that correctly represents the microscopic behavior of the system. The chosen example shows that the method can be used to mimick the mechanical unfolding process of systems for which the end-to-end distance does not provide a sufficient order parameter and that do not unfold in a simple cooperative manner.

Complex systems display emergent phenomena that vary significantly across spatial and temporal scales. These variations originate from fine-grained system processes, yet arriving at macroscopic dynamics from micro-level data – particularly when large, high-resolution datasets are available – remains a persistent challenge. Here we develop event-based spatiotemporal networks, a computational modelling framework that encodes system processes as discrete events anchored in space and time. Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events. We demonstrate the effectiveness of event-based spatiotemporal networks through two illustrative real-world applications. First, following a local outbreak of a novel respiratory pathogen in the Netherlands, spatiotemporal networks enable fine-grained tracking of transmission routes and infection patterns through space and time. Second, we use spatiotemporal networks to model propagation of delays in a public transportation system (S-bahn) around Zürich, Switzerland. We also discuss broader uses of event-based spatiotemporal networks in fields like developmental biology and community ecology, where focusing on events rather than static system states can improve data analysis, simulation, and collection strategies.

The dynamics of dissipative many-body quantum systems sometimes admit an emergent classical description in terms of a Markov chain even though the corresponding state space contains highly entangled states. In particular, a bad-cavity laser is a paradigm system whose dynamics can be formulated as a Markov chain in a two-dimensional state space spanned by collective angular momentum states. In this article, we investigate the connection between a dissipative phase transition that occurs in the subradiant regime of this system in the large atom number limit, and the properties of the underlying Markov chain. In one of the phases, the Markov chain approaches the detailed-balance condition with increasing atom number $N$ and hence becomes effectively time-reversible. This is caused by a collective atomic emission process that effectively reduces the Markov chain to one dimension. In the other phase, we find the emergence of time-asymmetric probability currents in the two-dimensional state space that signals a breakdown of detailed balance. This is accompanied by a macroscopic internal entropy production rate in the Markov chain that scales extensively with the atom number $N$. An observable consequence of the probability currents is a self-pulsing of the cavity light output in this phase, which can be detected as an anti-correlation dip in the intensity correlation function.

Superconducting coplanar resonators are key building blocks of cryogenic microwave circuits, yet their performance in perpendicular magnetic fields is ultimately limited by Abrikosov vortices. In this work we investigate the dependence of the transmission parameter $S_{21}$ of niobium quarter-wave coplanar resonators on perpendicular magnetic fields up to 40 Oe and at temperatures between 18 mK and 5 K. Beyond the reversible Meissner regime, the entire resonance peak exhibits abrupt, staircase-like jumps as a function of magnetic field. Upon reversal of the field sweep, these jumps form an almost equidistant series with spacing 1.7-1.8 Oe, which, in agreement with theoretical estimates, we interpret as signatures of multiple-vortex entry and exit events. Additionally, we observe the non-proportional responses of the resonant frequency and the internal quality factor that indicate a complex contribution of vortex and antivortex configurations. We believe that our results will stimulate further studies of large vortex-antivortex systems, explicitly accounting for their discrete nature.