Liquid-liquid phase separation underlies phenomena ranging from protein condensate formation to the phase coexistence of synthetic polymers. Although the random phase approximation (RPA) is widely used to predict such phase behavior, its quantitative accuracy for binodals of polymer solutions, particularly outside the high-density regime, remains incompletely characterized. Here, we develop a field-theoretic loop expansion in homopolymer systems by identifying the inverse polymer density ${\rho }^{-1}$ as the Planck constant $\hslash$ in quantum field theory. We calculate the leading-order and next-to-leading-order corrections to the RPA free energy, denoted as $\mathrm{RPA}+$ and $\mathrm{RPA}++$, respectively. Testing the binodal predicted by the $\mathrm{RPA}+$ against molecular dynamics simulations of bead-spring chains with Gaussian pair interactions, we find that the $\mathrm{RPA}+$ qualitatively improves the dilute-phase coexistence density over the RPA, while the critical point error remains comparable to that of the RPA. Our results establish the loop expansion as a systematic route for refining the RPA-based binodal predictions for polymer phase separation.

We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide $\left({\mathrm{VO}}_2\right)$, including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of ${\mathrm{VO}}_2$, and the structural transitions between them, using one consistent approach with moderate computational cost and without prepattering of the structure into dimerized and undimerized V-V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet insulator on the dimerized chains and a Mott insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of ${\mathrm{VO}}_2$, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1- or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, as a function of both distortion and on-site interaction.

Hole-spin qubits enable fast, all-electrical spin manipulation through electric-dipole spin resonance (EDSR), arising from two microscopic mechanisms rooted in their intrinsically strong spin-orbit interaction. Depending on how the electric field acts on the quantum dot, the spin can be driven either by a modulation of its $g$ factor or by a displacement of the wavefunction. Here, we demonstrate in situ control over the dominant EDSR driving mechanism of a hole-spin qubit in a silicon fin field-effect transistor by applying microwave signals to two different gate electrodes, thereby tuning the orientation of the local electric field. We measure the effective $g$ factor, its electrical tunability, and the Rabi frequency as functions of magnetic-field orientation. Their distinct angular dependencies, analyzed using a $g$-matrix formalism, allow us to identify the underlying driving processes and track their relative contributions for different drive configurations. By selecting the drive electrode, we can switch from a regime dominated by $g$-factor modulation to one with a strong contribution from wavefunction displacement. This in situ tunability provides direct experimental access to both spin-driving mechanisms and offers a route toward optimized spin-qubit performance.

Optimal processes in stochastic thermodynamics represent a frontier for understanding the control and design of nonequilibrium systems, with broad practical applications in biology, chemistry, and nanoscale/mesoscale systems. Optimal transport theory and thermodynamic geometry have emerged as leading optimal control methodologies, but both rely on slow-driving and close-to-equilibrium assumptions. An optimal control framework in stochastic thermodynamics for finite-time driving remains elusive. Here, we solve an optimal control problem for driving the control parameters of a discrete-state far-from-equilibrium process from an initial to a final value in finite time. Optimal driving protocols are derived that minimize the total finite-time dissipation cost of the driving process. Our framework reveals that discontinuous end-point jumps are a generic, model-independent physical mechanism that minimizes the optimal driving entropy production—“geometric thermodynamic far-from-equilibrium shortcuts in swift state-to-state transformations”—whose importance is further amplified in far-from-equilibrium systems. The thermodynamic and dynamical interpretation of discontinuous end-point jumps is formulated. An exact mapping between the finite-time and slow-driving optimal control formulations is elucidated, advancing the state of the art in optimal transport theory and thermodynamic geometry, which has been the prevailing paradigm for studying optimal processes in stochastic thermodynamics under slow-driving assumptions. Our framework opens up a broad range of applications to the thermodynamically efficient control of far-from-equilibrium systems in finite time, and thereby a route toward their efficient design principles.

Berry monopoles are point degeneracies in parameter space where the Berry curvature emanates radially, analogous to magnetic monopoles. Here, we report the adiabatic merging and splitting of Berry monopoles in a bilayer photonic crystal slab combining one genuine and one synthetic momentum. Two monopoles approach, merge, and split within this hybrid parameter space. The process is described by an effective coupled-mode model and confirmed by full-wave simulations. We further propose a scheme to experimentally probe the Berry monopole merging and splitting by monitoring the evolution of chiral edge states localized at the interface between two topological phases with inverted band structures.

We investigated the size dependence of the metal-insulator transition (MIT) in Cr-doped ${\mathrm{V}}_2{\mathrm{O}}_3$ nanocrystals by photoemission spectroscopy using complementary probing depths, together with magnetic susceptibility measurements. Photoemission spectra show that MIT signatures persist down to an average particle size of 5.6 nm, and magnetic susceptibility measurements exhibit a nearly size-invariant transition onset. The contrast between surface-sensitive and deeper-probing photoemission spectra reveals that the transition survives in the nanocrystal interior. At the same time, the spectra indicate a systematic suppression of coherent quasiparticle weight with decreasing size, pointing to the growth of an insulating surface dead layer. These results demonstrate that nanoscaling does not intrinsically eliminate the MIT itself, but progressively enhances the influence of surface-driven insulating behavior, thereby providing insight into the practical limits of miniaturizing Mott-based devices.

Data-driven machine learning has established a robust foundation for reconstructing nonlinear dynamical systems from observations, primarily for the purposes of forecasting and control. However, most existing efforts focus on recovering specific observed dynamics rather than the generative synthesis of new ones. Inspired by image fusion and style transfer, we introduce a neural network framework termed neural dynamical transfer learning (NDTL) to create new systems with prescribed dynamics from pairs of parent nonlinear dynamical systems. By computing fundamental dynamical signatures, including the intrinsic dimension, the Kaplan-Yorke dimension, the invariant measure statistics, and the Lyapunov spectrum, we demonstrate that NDTL preserves key features inherited from the parent models while simultaneously generating novel dynamics. Beyond these validation examples, NDTL induces a criterion for dynamics classification, creates stable oscillatory coexistence in the Hastings-Powell food chain model, produces interpretable epidemiological models, and provides a chaotic source for image encryption.

Percolation of fine particles (fines) in a static bed of larger particles is central to many industrial and natural processes. Noncohesive fines either pass through the bed or become trapped depending on multiple factors including particle sizes, friction and restitution coefficients, and size polydispersity. Here, we consider the additional factor of cohesion in the single-particle limit. We use the discrete element method to simulate gravity-driven percolation of non-self-interacting cohesive fine particles through a static bed of randomly packed large particles; fines interact with bed particles but not with each other. A large-to-fine particle diameter ratio of seven geometrically permits noncohesive fines to pass the narrowest pore throats formed by the large particles so they can freely percolate. However, sufficiently large cohesion and friction lead to nongeometric trapping. Fines are trapped when they fail to rebound after a collision, due to large cohesion, low restitution, and low collision velocity, and any subsequent rolling or sliding is insufficient to cause detachment. This establishes a sequence of local interactions—collision, adhesion, and postcontact motion—that governs the ultimate fate of a fine particle. A collisional model that incorporates a trapping probability per collision and a collision frequency predicts the trapping distance in the regime dominated by collision-induced trapping. For nonrebounding collisions, frictional effects are enhanced by cohesion and, when large enough, prevent the fine particle from subsequently detaching. A static equilibrium condition based on force balance predicts whether a fine particle remains stationary after contact. These results show that percolation of cohesive fine particles in the single-particle limit is not determined by geometric accessibility alone, but also by particle-scale interaction dynamics that can override geometric expectations.

Neural-network quantum states have been developed as an efficient method for solving quantum many-body problems, not only in lattice systems but also in systems of particles in continuous space. Here, we apply this approach to strongly interacting few-body problems in continuous space at unitarity: the Efimov states and associated few-body bound states. We extend the previous work [J. Phys. Soc. Jpn. 87, 074002 (2018)], in which the ground states of few-boson systems were obtained, to the first excited states with a projection method, and also to a mass-imbalanced fermionic system consisting of two identical fermions and a third particle. The obtained energies of the ground and first excited states of these systems agree well with previously reported results. Furthermore, the proposed approach also reproduces the discrete scale invariance between the ground and first excited states and the critical-mass behavior in mass-imbalanced fermionic systems.

We extend the concept of polygonal resonators to curved spaces through a unified model governed by a joint curvature parameter. This framework uncovers unique dynamics, including dissipative states defined by hyperbolic fixed points that yield phase diagrams in the curvature space. We find that stability transitions across geometric boundaries are characterized by sharp declines in the quality factor, a manifestation of chaotic instability that is overcome in the wave regime, highlighting the wave nature of wave chaos. A key discovery is the symmetry-driven asymptotic behavior in geodesic-sided cavities, revealing a profound connection between physical dynamics and underlying geometry. We also introduce a “non-Euclidean unit circle” as a general shape-invariant platform for optical simulation. These findings provide a robust framework for studying non-Euclidean wave chaos and point to strategies for designing integrated photonics with enhanced degrees of freedom.

X-ray free-electron lasers (XFELs) provide ultrashort, high-brightness pulses for probing matter on femtosecond and attosecond timescales, but their stochastic shot-to-shot fluctuations make x-ray absorption spectroscopy (XAS) challenging, as both incident and transmitted spectra are required to be measured on the same shot. Conventional approaches that rely on averaged reference spectra have limited accuracy, and full-shot characterizations are impractical. Here, we demonstrate a machine learning approach to bypass these limitations. An artificial neural network predicts the incident spectral shape from recorded diagnostics, effectively providing the equivalent of simultaneous incident spectrum measurements based on recorded machine diagnostics. Applied to XAS of Mylar at the oxygen K-edge, the method improves the accuracy in the pre-edge region ($<530.5\text{eV}$), reducing the root-mean-squared error from $0.0415$ to $0.0082\text{OD}$ (optical density). This approach remains highly robust with small target datasets and reduces experimental acquisition time, offering a strategy for more accurate XAS measurements in XFEL experiments where noninvasive, single-shot monitoring of the incident spectrum is not feasible.

We reveal interference patterns in photo-double-ionization spectra, produced by the interaction of argon atoms with an extreme-ultraviolet light wave packet in the form of a double pulse with variable pulse separation. These interference features provide direct evidence for nonseparable two-electron wavefunctions, an insight previously deemed beyond the reach of electron spectroscopy. This advance leverages the recently uncovered longitudinal coherence of undulator radiation, opening new avenues for attosecond-scale quantum interference experiments. The present undulator-based methodology is straightforwardly extendable into the x-ray regime, potentially offering a platform for investigating multielectron correlations associated with core-hole creation and decay.

Predicting the nematic alignment of nonspherical particles in sheared granular flows is essential for understanding the rheology, packing, and constitutive response of dense particulate media. While macroscopic fabric is typically attributed to complex multibody interactions, stress transmission, and dissipative collisions, empirical observations reveal that the steady-state nematic order parameter $S_2$ depends primarily on particle aspect ratio and remains remarkably insensitive to shear rate and interparticle friction. Here, we show that this leading-order alignment emerges directly from single-particle boundary geometry without resolving dynamical equations of motion. Assuming uniform contact probability along a particle perimeter, we derive an analytical transform linking local boundary curvature $\kappa \left(\theta \right)$ to the distribution of contact normals, $P\left(\theta \right)\propto 1/\kappa \left(\theta \right)$, which in turn geometrically constrains the phase space of admissible particle orientations. This minimal framework accurately predicts the magnitude of $S_2$ across the full continuum of aspect ratios for smooth ellipsoids as well as the singular limit of faceted rectangles and cylinders. Our analytical predictions capture the envelope of three-dimensional discrete element simulations and match laboratory measurements on sheared rice grains and glass cylinders across decadal variations in shear rate. By identifying particle geometry as the primary control parameter for granular alignment, this work provides a first-principles physical foundation for geometric saturation at the critical state, establishing a universal baseline upon which dynamical and frictional effects act as secondary modulations.

Nonreciprocal interactions are known to produce distinctive dynamics in active matter. To shed light on how the stationary state of such systems is affected by breaking reciprocity, we consider interacting particles propelled by persistent noise, where reciprocity is broken by a transverse force perpendicular to the gradient of the interaction energy. Focusing on the steady-state distribution of positions, we show that the nonreciprocal coupling helps keep the system at its stable configurations. Specifically, we demonstrate this effect for a variety of active systems whose stable configurations are energy minima, finding that the nonreciprocal coupling stiffens springs, aligns spins, and improves associative memory. In contrast, the transverse force does not change the stationary distribution of positions at all when the noise is thermal. Preliminary simulations suggest that this nonreciprocal coupling plays a similar stabilizing role in other active systems, such as those exhibiting motility-induced phase separation, whose stable configurations are not energy minima.

Photoinduced phase transition (PIPT) involves substantial changes in both electronic and crystallographic structures caused by electron excitations. Because PIPT is ultrafast and exhibits inhomogeneous behavior, its fundamental processes, in particular, how the ultrafast dynamics of photoexcited electrons trigger lattice deformation and the timescales involved, remain poorly understood. In this study, phase transition and its spatial separation in organic charge-transfer complexes was investigated using photoemission electron microscopy with femtosecond pulsed laser excitation. First, the thermally induced phase transition was confirmed by imaging the morphology change and also by observing the change of spectral shape around the Fermi level. Second, the escape time of photoexcited electrons or the heat generated by the laser pulses is estimated by observing the morphology change depending on the laser repetition rate. Finally, the dynamics of the photoexcited electrons and the subsequent morphology change are directly visualized. The results show that an anisotropic morphology change is initiated following the accumulation of electrons in unoccupied states over approximately a picosecond.

Time-varying external fields can program dipolar interactions in soft matter systems. Continuously rotating fields are widely used to promote isotropic self-assembly, but finite relaxation of an induced dipole generically introduces a phase lag that can drive the spinning of both individual particles and clusters. Alternating rotating fields, which reverse the rotation direction every half cycle, suppress net spinning, but need not yield a truly isotropic time-averaged interaction when the dipole relaxes with a finite timescale $\tau$. Here, we propose an extended alternating rotating field in which the field sweeps beyond $2\pi$ within each half cycle to compensate for relaxation-induced undersampling of dipole orientations near the reversal direction. Using paramagnetic colloids under time-varying magnetic fields as a representative realization of a generic first-order relaxation model, we determine the half-cycle sweep angle ${\varphi }_{\mathrm{iso}}$ required to obtain an isotropic cycle-averaged pair potential. The isotropy condition collapses onto a master curve controlled by the dimensionless parameter $\tau \omega$, and we derive accurate analytical approximations in the experimentally important small-$\tau \omega$ regime. We also analyze the residual anisotropy under conventional alternating rotating-field protocols and show that its nonmonotonic dependence on $\tau \omega$ underlies the single-peaked ${\varphi }_{\mathrm{iso}}\left(\tau \omega \right)$. Because the theory assumes only first-order relaxation, the framework applies broadly to field-responsive soft matter beyond magnetic colloids. It enables switching between isotropic and anisotropic cycle-averaged pair interactions while suppressing net rotation and provides a route to infer relaxation times.

We show that anisotropic spin interactions do not merely break spin-space group (SSG) symmetries, but instead twist them through cohomology invariants, yielding symmetry classes beyond subgroups of $\mathrm{O}\left(3\right)\times Isom\left({\mathbb{R}}^3\right)$. This requires redefining the spin-only group ${\mathcal{S}}_0$ in terms of proper spin rotations. Based on this unitary ${\mathcal{S}}_0$, we formulate a twisted SSG (tSSG) theory that captures the complete set of spin-space symmetries. We then study a spin-1 model with tSSG symmetry using linear flavor wave theory and find ${\mathbb{Z}}_2$ topological quadrupolar excitations defined on a spin Brillouin Klein bottle. Specifically, the quadrupolar excitations possess a momentum-space glide-reflection symmetry and the edge states exhibit a nonlocal momentum twist. These results establish the symmetry language required for interacting spin systems, whether realized in magnetic materials or on programmable quantum simulators, and open a route to unconventional magnetism.

Efficient optomechanical cooling typically requires high photon occupancy to maximize cooling power, a constraint that generally limits the degree of coherent quantum control available in the few-photon regime. Here, we investigate this trade-off by considering a strongly nonlinear cavity operated as a discrete quantum system. In the weak-coupling limit, we derive a general connection between the optomechanical damping rate and the cavity's dressed-state manifold. This framework reveals that the damping rate (determined by the population imbalance across dressed states) is directly tunable via the coherent manipulation tools, which are standard in circuit quantum electrodynamics. We illustrate this framework using a Josephson photonics architecture, where a dc-biased junction induces a photon blockade that truncates the cavity to an $N$-level system. By sacrificing raw cooling (or heating) power, this platform enables full quantum mechanical control over optomechanical properties, offering a versatile avenue for the quantum manipulation of mechanical modes.

The Eigen model is a prototypical toy model of evolution that is synonymous with the so-called error catastrophe: When mutation rates are sufficiently high, the genetic variant with the largest replication rate does not occupy the largest fraction of the total population because it acts as a source for the other variants. Here, we show that, in the stochastic version of the Eigen model, there is also a fidelity catastrophe. This arises due to the state-dependence of fluctuations and occurs when rates of mutation fall beneath a certain threshold, which we calculate. The result is a type of noise-induced multistability in which the system stochastically switches between short-lived regimes of effectively clonal behavior by different genetic variants. Most notably, when the number of possible variants—potentially $\sim 4^L$, with $L\gg 1$ the length of the genome—is significantly larger than the population size, there is only a vanishingly small interval of mutation rates for which the Eigen model is neither in the fidelity- nor error-catastrophe regimes.