The inclusion of the process of multiple ionization of atoms in high-intensity electromagnetic fields into particle-in-cell (PIC) codes applied to the simulation of laser-plasma interactions is a challenging task. In this paper, we first revisit ionization rates as given by the Smirnov-Chibisov and Perelomov-Popov-Terent'yev formulas within the paradigm of sequential tunnel ionization. We analyze the limit of validity and possible inconsistencies of this approach. We show that a strongly limiting factor to a precise description of ionization is the competing contribution of different sequential ionization processes. To solve this an algorithm is proposed that allows one to find the dominant nonsequential path of tunnel ionization and significantly improves the precision in simulations. This procedure is implemented in the PIC code SMILEI, and includes the dependence of the ionization rates on the magnetic quantum number of the level. The sensitivity to variations in the ionization model is studied via full simulations of the ionization of an argon target by an incident high-intensity laser pulse. Finally, we analyze generalizations of the Perelomov-Popov-Terent'yev rate developed to describe the barrier suppression ionization in high fields and discuss the necessity and possibility of including these extensions in PIC simulations.

Machine-learned interatomic potentials (MLIPs) are rapidly becoming the standard for atomistic simulations, but their application to magnetic materials remains challenging because spin fluctuations must be treated either explicitly or implicitly. Here, we investigate this problem for the technologically important Fe-Cr-C system by constructing two deep machine learning potentials within the DeePMD framework: one trained on nonmagnetic DFT data (DP-NM) and one on spin-polarized DFT data (DP-M). Extensive validation against experiments reveals that DP-NM accurately reproduces dynamic, collective properties such as viscosity and melting temperatures, while DP-M excels in describing static, local properties such as density, especially for Fe-rich alloys. We rationalize these findings by noting that, at high temperatures, local magnetic moments self-average over space and time, making explicit spin treatment unnecessary for transport properties but essential for equilibrium volumes. To mitigate the high cost of spin-polarized DFT sampling, we employ a transfer-learning strategy, pretraining on nonmagnetic data followed by fine-tuning on a small spin-polarized dataset, which reduces computational expense by more than an order of magnitude. Furthermore, we benchmark several state-of-the-art foundation models: MACE, GRACE, DPA3 and fine-tuned variants of the latter against our specialized potentials. We find that foundation models offer competitive accuracy after fine-tuning but remain significantly slower in molecular dynamics simulations, limiting their practicality for large-scale transport property calculations. Our results establish clear design principles for MLIPs targeting magnetic alloys: (i) nonmagnetic training data suffice for dynamic properties of paramagnetic melts, and (ii) spin-polarized training is essential only for precise static properties of ferromagnetic phases. These insights provide a roadmap for efficient development of transferable MLIPs for magnetic systems and clarify the role of foundation models and transfer learning in accelerating this process.

We propose a quantum-inspired classical algorithm for combinatorial optimization problems, named the counterdiabaticity-assisted classical algorithm for optimization (CACAO). In this algorithm, a solution of a given combinatorial optimization problem is heuristically searched with classical spin dynamics based on quantum Lyapunov control of local counterdiabatic driving. We compare the performance of CACAO with that of quantum time-evolution algorithms, i.e., quantum annealing, the feedback-based algorithm for quantum optimization (known as FALQON), and the counterdiabatic feedback-based quantum algorithm (known as CD-FQA). We also study the performance of CACAO applied to large systems up to $10000$ spins.

Understanding the evolution of influence in dynamic networks is crucial for revealing the underlying mechanisms of complex interactions in social, biological, and information systems. Despite its importance, the temporal patterns of influence in such networks remain largely unexplored. A significant challenge in these investigations lies in the perception that networks, often consisting of thousands of nodes, entail too many evolving influence processes to be feasibly analyzed. In this study, we uncover a participant-invariant characteristic within the influence dynamics of real-world networks. Specifically, we demonstrate that a small number of influence patterns (often just one) can effectively capture the overall behavior of a network, regardless of its size. Through extensive experiments on 50 dynamic network datasets from diverse domains, we identify these patterns and quantify node participation levels via associated weights. Our findings further reveal that influence patterns within networks of the same category exhibit striking similarities, and the distribution of node weights follows a power law, indicating a high degree of heterogeneity in node participation levels. These insights streamline the representation of dynamic networks and provide a framework for understanding the evolution of influence in complex systems.

The Liang-Kleeman (LK) information flow provides a powerful framework for quantifying causality among variables, while linear inverse modeling (LIM) offers an effective empirical approach to studying the dynamical evolution of system states from input data. In this study, we unify these two concepts by proposing the LIM-LK framework—a data-driven method for estimating LK information flow from input data using LIM. Beyond capturing causality among state variables, the proposed framework establishes a direct connection between causality and system dynamics, and also enables the quantification of entropy transfer from the ambient environment to the system through both memoryless and persistent stochastic forcing. The effectiveness of this unified approach is demonstrated through an application to the interaction between the Pacific and Indian Oceans, offering both causal and dynamical insight into ocean variability and its seasonal modulation.

This study investigates the quantum effects in transverse-field Ising spin glass models with rotationally invariant random interactions. The primary aim is to evaluate the validity of a quasistatic approach that captures the imaginary-time dependence of the order parameters beyond the conventional static approximation. Using the replica method combined with the Suzuki–Trotter decomposition, we established a stability condition for the replica symmetric solution, which is analogous to the de Almeida–Thouless criterion. Numerical analysis of the Sherrington–Kirkpatrick model estimates a value of the critical transverse field, ${\mathrm{\Gamma }}_{\mathrm{c}}$, which is consistent with previous Monte Carlo-based estimations. For the Hopfield model, it provides an estimate of ${\mathrm{\Gamma }}_{\mathrm{c}}$, which has not been previously evaluated. For the random orthogonal model, our analysis suggests that quantum effects alter the random first-order transition scenario in the low-temperature limit. This study supports a quasistatic treatment for analyzing quantum spin glasses and may offer useful insights into the analysis of quantum optimization algorithms.

While traditional thermodynamic equilibrium requires uniform temperature across coexisting phases, underdamped active matter systems can sustain nonequilibrium hot-cold coexistence through motility-induced phase separation. We investigate particle demixing and emergent temperature gradients in binary mixtures of inertial active and passive particles. Remarkably, within specific parameter ranges of intermediate particle inertia and self-propulsion strength, the system simultaneously achieves pronounced particle demixing and sustains significant hot-cold coexistence. Activity differences drive rapid species separation, which is further enhanced over time by persistent rotational diffusion. The synergy between inertia and activity significantly amplifies temperature differences both between particle species and across coexisting gas-liquid phases. These temperature disparities originate from inertia-enabled energy storage, collision-mediated energy transfer, propulsion-driven acceleration, and weakened liquid-phase cohesion. Unlike equilibrium systems, active-passive mixtures circumvent thermal homogenization by maintaining kinetic temperature gradients through continuous energy injection and dissipation. These findings elucidate fundamental principles of nonequilibrium self-organization in hybrid systems, with implications for bio-inspired materials, microbial ecology, and energy transport in active composites.

We show that the degree of symmetry of the diversity distribution is the key determinant of global oscillations in coupled networks of FitzHugh-Nagumo units, used as prototypical examples of excitable systems. In these ensembles, symmetric diversity reliably yields resonant collective oscillations—even when all units are individually excitable—whereas asymmetric diversity suppresses them. Two symmetry-based metrics predict the presence or absence of global oscillations from the distribution alone. A simple mean-field mechanism, corroborated by a minimal two-unit analysis, explains how symmetry creates a landscape that supports limit cycles. These results identify diversity distribution symmetry as a key mechanism for emergent synchronization in excitable media.

In this paper, we consider a class of interacting particle systems on dynamic random networks, in which the joint dynamics of vertices and edges acts as one-way feedback, i.e., edges appear and disappear over time depending on the state of the two connected vertices, while the vertex dynamics does not depend on the edge process. Our goal is to estimate the underlying dynamics from partial information of the process, specifically from snapshots of the total number of edges present. We showcase the effectiveness of our inference method through various numerical results.

We use a three-dimensional formulation of the cell vertex model to describe the mechanical properties of a confluent planar monolayer of prismatic cells. Treating cell height as a degree of freedom, we reduce the model to a two-dimensional form. We show how bulk effects, associated with cell volume and total surface area, lead to coupling between energy variations arising from changes in the cell apical area and the apical perimeter, a feature missing from standard implementations of the two-dimensional vertex model. The model identifies five independent mechanisms by which cells can lose in-plane rigidity, relating to variations in total cell surface area, the strength of lateral adhesion, and constrictive forces at the apical cortex. The model distinguishes bulk from in-plane stresses, and it identifies two primary measures of cell shear stress. In the rigid regime, the model shows how lateral crowding in a disordered isolated monolayer can lead to cell elongation towards the monolayer center. We examine the loss of in-plane rigidity in a disordered monolayer and connect isolated patches of stiffness that persist during the rigidity transition to the spectrum of a Laplacian matrix. This approach enables bulk mechanical effects in an epithelium to be captured within a two-dimensional framework.

Evolution is often viewed as a competition among agents for resources, effectively the harnessing of negative entropy or free energy in nonequilibrium states. In this work, we examine how constraints on entropic waste elimination shape evolutionary outcomes and affect the choices made by competing agents. We build on the Babajanyan–Koonin–Allahverdyan model that represents agents as heat engines in a game-theoretic setting. We introduce a constraint on the system's total cooling rate, arising from its interaction with a low-temperature heat bath that represents the system's elimination of entropic waste. We show that imposing such a constraint modifies the Nash equilibrium solutions of the game. We identify scenarios where the cap on the combined waste-removal drives the agents toward different optimal strategies than in the unconstrained case.

Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the protein data bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicrystals. As we show, these numerous shells have cubic nets cut from short-period approximants of an octagonal tiling composed of square and rhombic tiles. The approximants and parent tiling are easily obtained within the Landau density wave approach, while the nonequilibrium assembly of them can be simulated using the pair potentials derived from critical density waves. Gluing a polyhedron net and mapping it onto a spherical surface induces tile distortions, and to reduce them, we introduce and minimize the effective elastic energy of the system. Thus, we return quasiequivalence to previously equivalent tiles. Possible cubic faceting of the octagonal spherical tilings is discussed in terms of the topological charge distribution over the tiling vertices. The proposed structural models describe numerous proteinaceous shells including about half of the known symmetrical enzymes. Our results constitute a fundamental basis for further applications of identified octagonal assemblies and can help to discover and study similar systems in the future.

Active matter concerns many-body systems comproed of living or self-driven agents that collectively exhibit macroscopic phenomena distinct from conventional passive matter. Using Schwinger-Keldysh effective field theory, we develop a hydrodynamic framework for thermal active matter that accounts for energy balance, local temperature variations, and the ensuing stochastic effects. By modeling active matter as a driven open system, we show that the source of active contributions to hydrodynamics, violations of fluctuation-dissipation theorems, and detailed balance is rooted in the breaking of time-translation symmetry due to the presence of fuel consumption and an external environmental bath. In addition, our framework allows for nonequilibrium steady states that produce entropy, with a well-defined notion of steady-state temperature. We use our framework of active hydrodynamics to develop effective field theory actions for active superfluids and active nematics that offer a first-principle derivation of various active transport coefficients and feature activity-induced phase transitions. We also show how to incorporate temperature, energy, and noise in fluctuating hydrodynamics for active matter. Our work suggests a broader perspective on active matter that can leave an imprint across scales.

Sparsely coupled Kuramoto oscillators offer a fertile playground for exploring high-dimensional basins of attraction due to their simple yet multistable dynamics. For $n$ identical Kuramoto oscillators on cycle graphs, it is well known that the only attractors are twisted states, whose phases wind around the circle with a constant gap between neighboring oscillators (${\theta }_j=2\pi qj/n$). It was conjectured in 2006 that basin sizes of the twisted states scale as $e^{-k{q}^2}$ with the winding number $q$. Here, we provide numerical and analytical evidence supporting the conjecture and uncover the dynamical mechanism behind the Gaussian scaling. The key idea is that, when starting with a random initial condition, the winding number of the solution stabilizes rapidly at $t\propto logn$, before long-range correlation can develop among oscillators. This timescale separation allows us to calculate the winding number as a sum of weakly dependent random variables, leading to a Central Limit Theorem derivation of the basin scaling.

We investigate an SIR model of epidemic propagation on networks in the context of mean-field games. In a real epidemic, individuals adjust their behavior depending on the epidemic level and the impact it might have on them in the future. These individual behaviors, in turn, affect the epidemic dynamics. Mean-field games are a framework in which these retroaction effects can be captured. We derive dynamical equations for the epidemic quantities in terms of individual contact rates, and via mean-field approximations, we obtain the Nash equilibrium associated with the minimization of a certain cost function. We first consider homogeneous networks, where all individuals have the same number of neighbors, and discuss how the individual behaviors are modified when that number is varied. We then investigate the case of a realistic heterogeneous network based on real data from a social contact network. Our results allow us to assess the potential of such an approach for epidemic mitigation in real-world implementations.

We analyze the deviations from local thermodynamic equilibrium (LTE) of the ion velocity distribution function (iVDF) in collisionless plasma turbulence. Using data from the magnetospheric multiscale (MMS) mission, we examine the non-Maxwellianity of 439 685 iVDFs in the Earth's magnetosheath. We find that the iVDFs' anisotropies and the high-order non-bi-Maxwellian features are widespread and can be significant. Our results show that the complexity of the iVDFs is strongly influenced by the ion plasma beta and turbulence intensity, with high-order non-LTE features emerging in the presence of large-amplitude magnetic field fluctuations. Furthermore, our analysis indicates that turbulence-driven magnetic curvature contributes to the isotropization of the iVDFs by scattering the ions, emphasizing the complex interaction between turbulence and the velocity distribution of charged particles in collisionless plasmas.

A quantum thermal diode, similar to an electronic diode, allows for unidirectional heat transmission. In this paper, we study a quantum thermal diode composed of two two-level atoms coupled to auxiliary two-level atoms. We find that while excited (ground-state) auxiliary atoms weaken (enhance) the heat current, the rectification is dictated by the left-right atomic frequency relationship. The more auxiliary atoms are coupled, the stronger the enhancing or weakening impact is. If the auxiliary atom is in a superposition state, we find that only the fraction that projects onto the excited state plays a significant role. In particular, if we properly design the coupling of the auxiliary atoms, the rectification effect can be eliminated. This provides the potential to control the heat current and the rectification performance by the states of the auxiliary atoms.

Notch signaling is a ubiquitous and versatile intercellular signaling system that drives collective behaviors and pattern formation in biological tissues. During embryonic development, Notch is involved in generation of collective biochemical oscillations that form the vertebrate body segments, and its failure results in embryonic defects. Notch ligands of the Delta family are key components of this collective rhythm, but it is unclear how different Delta ligands with distinct properties contribute to relaying information among cells. Motivated by the zebrafish segmentation clock, in this work we propose a theory describing interactions between biochemical oscillators, where Notch receptor is bound by both oscillatory and nonoscillatory Delta ligands. Based on previous in vitro binding studies, we first consider Notch activation by Delta dimers. This hypothesis is consistent with experimental observations in conditions of perturbed Notch signaling. Then we test an alternative hypothesis where Delta monomers directly bind and activate Notch and show that this second model can also describe the experimental observations. We next consider the effects of cis-inhibition in a model motivated by evidence from the mouse segmentation clock and find that this alternative is compatible with only part of the experimental data. We show how the dimer and monomer hypotheses assign different roles for a nonoscillatory ligand, as a binding partner or as a baseline signal. Finally, we discuss experiments to distinguish between the two scenarios. Broadly, this work highlights how a multiplicity of ligands may be harnessed by a signaling system to generate versatile responses.