Papers

Higher-order networks exhibit rich critical phenomena that cannot be captured by traditional pairwise models. Here, we develop an analytical dimension-reduction framework that maps higher-order networked dynamics onto an effective low-dimensional system, allowing accurate prediction of tipping boundaries, bistability regions, and the nature of phase transitions. We demonstrate the power of this framework across a range of dynamical processes, revealing distinct effects of higher-order interactions on transition continuity and hysteresis. Furthermore, we find that system resilience exhibits a profound dependence on the alignment between pairwise and higher-order connectivity, with assortative mixing enhancing tipping toward active states. Our findings establish a general theory for understanding the critical transitions in higher-order networks, offering new insights for anticipating and managing systemic risk in complex systems.

The overdamped approximation is widely used to describe Brownian motion in complex environments, but its validity becomes nontrivial when the friction coefficient itself fluctuates in time. We investigate this problem by comparing underdamped and overdamped Langevin dynamics subject to the same fluctuating friction and satisfying the fluctuation–dissipation relation at a common temperature. We show that environmental averaging and inertial elimination generally lead to different long-time transport when environmental fluctuations are fast compared with velocity relaxation. For rapidly fluctuating friction, the underdamped dynamics is governed by the arithmetic mean friction and yields $D_{\rm eff}^{\rm under}=k_{\rm B}T/\langle\gamma\rangle$, whereas the overdamped dynamics gives $D_{\rm eff}^{\rm over}=k_{\rm B}T\langle\gamma^{-1}\rangle$. For a two-state Markov friction, we derive the finite-time effective diffusion coefficient exactly and identify the full crossover between these two regimes, controlled by the competition between velocity relaxation and environmental switching. We further establish the fast-fluctuation result for general stationary friction processes and verify it for a continuous log-Ornstein–Uhlenbeck environment, for which the characteristic crossover time can also be determined independently. Our results reveal a noncommutativity between environmental averaging and inertial elimination, establish a timescale-dependent criterion for the validity of overdamped dynamics in temporally heterogeneous environments, and show that rapid environmental fluctuations can enhance, rather than suppress, the consequences of inertia.

We report on the utilization of a low-damage e-beam NiO$_x$ deposition process to realize field-plated heterojunction diodes (FP-HJDs) on (010) $\beta$-Ga$_2$O$_3$ films with high critical breakdown field strengths beyond 4 MV/cm and power figure of merits (PFOM) of >1 GW/cm$^2$. Diodes were fabricated on a 2.67 $\times$ 10$^{16}$ cm$^{-3}$ intentionally doped 6.2 $\mu$m thick epitaxial layer grown by metalorganic chemical vapor deposition (MOCVD) on a conductive Sn-doped $\beta$-Ga$_2$O$_3$ (010) substrate using TMGa, O$_2$, Ar carrier gas, and SiH$_4$ as the silicon dopant source. The mesa etched FP-HJD devices utilized a thin 7 nm e-beam NiO$_x$ interlayer before the sputtered NiO$_x$ layers to eliminate the effects of sputter-induced ion damage on the (010) epilayers. Current-Voltage measurements resulted in a forward current density of 700 A/cm$^2$ at 4 V, HJD ideality factor of 1.29, a V$_{bi}$ of 2 V, rectification ratio of 10$^{11}$, and a differential specific on resistance (R$_{on,sp}$) value of 2.36 m$\Omega$ $\times$ cm$^2$. Breakdown of the (010) FP-HJDs was 1.64 kV, leading to a parallel plane electric field at breakdown (E$_{||,max}$) of 4.02 MV/cm and a PFOM of 1.14 GW/cm$^2$, which is a state-of-the-art result for diodes on MOCVD-grown (010) drift layers.

Electrical generation of orbital angular momentum provides a promising route to current-induced torques, yet effective control of orbital Rashba textures still remains challenging, particularly because the role of the interfacial crystal field remains largely unexplored. Here, we report experimental evidence for a crystal-field-mediated interfacial anisotropic orbital Rashba effect (AORE) in epitaxial Ru/ferromagnet heterostructures. Total orbital torque was disentangled into an isotropic bulk contribution and an in-plane anisotropic interfacial contribution. The latter was strongly suppressed by degrading the crystallinity of either constituent and by inserting a Cu spacer, highlighting the essential roles of coherent interfacial orbital hybridization and direct Ru/ferromagnet contact. These results identify interfacial crystal-field coherency as a key ingredient in manipulating orbital Rashba textures and establish a route toward engineering the symmetry and directionality of orbital torques.

We investigate the interplay between geometric dilution and non-Hermitian disorder in the two-dimensional quantum site-percolation model. Non-Hermiticity is introduced through random imaginary on-site potentials, representing spatially uncorrelated gain and loss, while the hopping amplitudes remain reciprocal. By combining complex level-spacing statistics, participation entropy, and multifractal analysis, we characterize the localization properties of the eigenstates as functions of the disorder and the non-Hermiticity strength. Our finite-size scaling results show that non-Hermitian disorder shifts the quantum percolation threshold ($p_q$) toward larger occupation probabilities. Consequently, the fully delocalized phase is progressively suppressed and disappears at sufficiently strong disorder. This suppression is not a simple consequence of adding on-site disorder of a given strength, but is specifically enhanced by its imaginary character, as an equally strong real (Hermitian) on-site potential produces a weaker shift of $p_q$. Nevertheless, the intermediate region between the classical ($p_c$) and quantum percolation thresholds presents a genuine multifractal critical phase, while the localization-length exponent $\nu$ remains the same, relative to its Hermitian value. Altogether, our results demonstrate that random gain and loss enhance the multifractal regime while preserving the universality class of the quantum percolation transition.

Recently synthesized carbon nanoribbons (CNRs) were investigated for hydrogen (H2) storage using first-principles density functional theory calculations. Pristine CNRs exhibited weak H2 adsorption; therefore, the host structure was modified by substituting carbon atoms at the C-H edges with 12 nitrogen atoms, followed by Mn and Y doping to enhance H2 binding. A maximum of five metal atoms could be accommodated on the 12N-CNRs. Electronic structure analysis revealed strong orbital hybridization between the metal atoms and the CNRs, while binding energy calculations confirmed the structural stability of the doped systems. Bader charge analysis further quantified the charge transfer between the metal atoms and the host structure. The average H2 adsorption energies were calculated to be -0.40 eV/H2 for the Mn-doped system and -0.25 eV/H2 for the Y-doped system, which are within the desirable range for reversible hydrogen storage. The maximum theoretical gravimetric storage capacities at 0 K reached 7.48 wt% for the Mn-doped system and 6.55 wt% for the Y-doped system. Under practical conditions of 30 atm and 298.15 K, the storage capacity of the Y-doped system decreased to 6.04 wt%, whereas the Mn-doped system maintained its full capacity of 7.48 wt%. Thermodynamic analysis indicated that H2 adsorption is favored at low temperatures and high pressures, while desorption becomes feasible at elevated temperatures and lower pressures. These results demonstrate that Mn- and Y-doped CNRs satisfy key U.S. Department of Energy requirements for reversible H2 storage and show promise as potential hydrogen storage materials under near-ambient conditions.

Elastic turbulence (ET) in straight geometries has been extensively studied in idealised configurations such as periodic shear and planar channel flow, yet experiments are typically restricted to confined systems like ducts and pipes. While chaotic elastic fluctuations are observed in ducts, their onset in pipes is debated due to geometric constraints. Here we report sustained chaotic dynamics in both geometries and show they correspond to the same ET state identified in idealised setups. Our results establish ET as a geometry-independent attractor and point to a distinct transition mechanism in pipes.

This work presents a density functional theory (DFT) investigation of a two-dimensional (2D) C5N monolayer functionalized with Li, Na, and K for hydrogen storage. Pristine C5N exhibits weak H2 adsorption, while alkali-metal functionalization significantly enhances its storage capability. The C5N monolayer can stably accommodate up to six metal dopants, with binding energies stronger than the corresponding cohesive energies, indicating resistance to metal aggregation. Ab initio molecular dynamics simulations further confirm the thermal stability of the functionalized systems at 300 K. Charge transfer from the metal dopants to C5N enhances polarization and strengthens H2 adsorption. Each dopant can adsorb up to eight H2 molecules, yielding a maximum of 48 H2 molecules per unit cell and gravimetric storage capacities of 9.42, 8.61, and 7.93 wt% for Li-, Na-, and K-functionalized C5N, respectively. The average H2 adsorption energies of -0.16 to -0.17 eV/H2 indicate moderate interactions suitable for reversible storage. Thermodynamic analysis further demonstrates favourable H2 adsorption/desorption under practical operating conditions, while desorption-temperature, recovery-time, and volumetric analyses support the potential reversibility and storage performance of these systems. Overall, alkali-metal-functionalized C5N emerges as a promising 2D material for efficient and reversible H2 storage.

Van der Waals magnets have received blooming interest in material science and spintronics for the advantages of low magnetization, flexible stacking, strong tunability, and dangling-bond-free interfaces. However, there has been no report of room-temperature magnetic-field-free spin logic device based on a van der Waals magnet. Here, we demonstrate a room-temperature, field-free, low-power, scalable spin-orbit torque in-memory computing logic device utilizing a van der Waals magnet Fe3GaTe2 bit with strong perpendicular magnetic anisotropy and a Pt channel with strong spin Hall effect, high electrical conductivity, and electric asymmetries. Current pulse width-dependent switching measurement reveals a low intrinsic critical magnetic-field-free switching current of 1.8e7 A cm-2, a high thermal stability factor of 50, and the potential of an ultralow write power of 1.5 fJ per bit at 1 ns pulse width for a typical industrial-level device dimensions (40 times lower than the industrially optimized W/FeCoB device). These results suggest a great potential of van der Waals magnets in dense, ultralow-power, scalable in-memory computing technologies.

Spin logic is of great interest for the development of high-performance, non-Von Neumann artificial intelligence chips. Of particular advantage is the programmable spin-orbit torque logic that can achieve the complete set of the 16 Boolean logic operations within a single device in an initialization-free, low-power, and scalable manner, which is, however, a challenge despite remarkable efforts over more than two decades. Here, utilizing chirally asymmetric switching, we demonstrate an initialization-free, low-power, programmable spin-orbit torque logic device that is capable of the complete set of 16 Boolean logic operations within a single device with only three inputs and ultralow power of < 1 fJ/bit. We also propose the first initialization-free, all-electrical spin-based cascading computing devices, including a half adder, a three-level full adder, a two-level full adder, and a Transfer/NOT selector. These compact and scalable computing devices are enabled by the chirally asymmetric spin-orbit torque switching of a Ta/FeCoB bilayer with a significant perpendicular Dzyaloshinskii-Moriya interaction field. These results pave an intriguing way for the development of next-generation high-performance large-scale in-memory computing chips based on chirally asymmetric spin-orbit torque switching.

We develop analytical criteria for surface electromagnetic waves at interfaces between hyperbolic materials and topological insulators. The analysis identifies propagation conditions, field penetration depths, and spectral band gaps. In the lossless model, the band edge is governed primarily by dielectric contrast, while the bulk topological coupling produces only a small shift and a weak correction to the dispersion. For structures combining hexagonal boron nitride with bismuth selenide, a thin topological insulator film on a dielectric substrate with low permittivity can substantially broaden the accessible propagation window. We also analyze a dissipative titanium and silicon hyperbolic effective medium interfaced with bismuth selenide. To make the complex branch structure visible, this lossy calculation uses an intentionally enhanced interface response and is therefore illustrative rather than a quantitative prediction for a conventional topological insulator. In the lossy case, a root of the dispersion relation represents a physical guided mode only when the fields decay away from the interface, attenuate consistently along the propagation direction, have finite penetration depths, and exhibit a consistent energy flow profile. Apparent gaps and jumps can occur when the complex quantities controlling transverse decay approach square root branch cuts. These results provide design guidelines for controlling dispersion, confinement, band gaps, and propagation loss in mid-infrared photonic structures.

One-dimensional carbon allotropes combining sp and sp2 hybridizations provide a versatile platform for atomically precise quantum wires with tunable electronic, vibrational, and mechanical responses. Recent on-surface synthesis has realized the $\gamma$-graphdiyne molecular wire on Au(100) in two interconvertible phases sharing an identical aromatic backbone but distinct linkage chemistry: an organometallic intermediate with C-Au-C bridges and a fully covalent product with diacetylenic linkages. While experimental techniques characterize the on-surface periodicity and C$\equiv$C Raman signatures, the intrinsic properties of the free-standing wires remain inaccessible. Here, we address this gap using hybrid-functional first-principles calculations. The covalent phase is a semiconductor with a direct band gap of 2.02 eV, a polyynic bond-length alternation of 0.117 Å, and a chain periodicity reproducing experiment within -0.5%. Conversely, the organometallic phase exhibits partial cumulenization and 1D metallic behavior, with spin channels crossing the Fermi level. Both isolated polymers demonstrate dynamic stability with Young's moduli of 995 nN and 861 nN, respectively, where the C-Au-C bridge acts as a soft link absorbing 9.2% of axial deformation at +6% macroscopic strain. Species-resolved vibrational analysis identifies an effective-conjugation-coordinate band at 1408 cm$^{-1}$ for the covalent phase and Au-projected modes below 400 cm$^{-1}$ for the intermediate, offering diagnostic fingerprints for phase discrimination during thermal or chemical conversion.

Von Neumann proposed two quantum entropies, one in 1927 and the other in 1929. He intended to define thermodynamic entropy for quantum states in both cases. The 1927 entropy is the well-known von Neumann entropy, which is zero for all pure states. The 1929 entropy, which is nonzero for almost all pure states, has largely been ignored. After reviewing the history and properties of both entropies, we argue that the 1927 entropy is not thermodynamic entropy and should be used simply as a measure of entanglement, whereas the 1929 entropy is the true thermodynamic entropy for quantum states.

Energy systems face converging pressures for security, affordability, resilience, and sustainability, creating a need for faster discovery of deployable energy materials. Here we introduce SCALE (Simulation-Calibrated Amortized Learning for Energy Materials), a physics-grounded, real-world-data-calibrated learning architecture that connects deterministic scientific operators, experimental calibration, expanded calibrated label generation, and transformer-scale inference. SCALE converts selected high-cost mechanistic computation and measured evidence into reusable models for rapid screening, ranking, inverse design, and active learning. We formulate the framework, identify ten method-based application regimes, and demonstrate SCALE for solid-state metal-hydride hydrogen-storage capacity prediction. In this implementation, a hydride phase-equilibrium capacity operator is calibrated against 381 measured ML-HydPARK capacity anchors and used to generate 5,000 candidate-condition-prototype teacher labels. A crystallographically anchored periodic-graph representation preserves atomic sites, periodic neighbor relationships, and local metal environments absent from formula-only encodings. An edge-biased graph transformer with 2.90 million parameters reproduces calibrated teacher labels with five-fold surrogate fidelity of MAE 0.0582 wt% H2, RMSE 0.0833 wt% H2, R2 = 0.9927, and Pearson r = 0.9963. Post hoc attention analysis suggests that SCALE learns chemically organized element groupings and metal-metal relationships consistent with established hydride chemistry, without chemistry-group labels as supervision. Once trained, SCALE shifts million-candidate evaluation from repeated deterministic workflow execution to batched learned inference, reducing per-candidate screening cost by approximately 10^7-10^8 while retaining links to simulation and experimental evidence.

Universal scaling generated when a strongly interacting quantum many-body system is driven across a continuous phase transition provides a dynamical probe of equilibrium criticality. Accessing this regime numerically in two dimensions is challenging because it requires accurate real-time evolution of correlated many-body states over many system sizes and driving rates. We introduce a Neural Foundation-state Subspace (NFS) method for near-adiabatic dynamics. A foundation neural-network quantum state represents the ground-state manifold along the driving path, and a small fidelity-selected subset defines a fixed variational subspace. The many-body Schrödinger equation then reduces to the evolution of a few linear coefficients, with projected operators reusable across ramp times. We validate the method on the two-dimensional transverse-field Ising model, recovering the expected Kibble-Zurek scaling and critical exponents in quantitative agreement with ground-state quantum Monte Carlo estimates. Applied to the frustrated square-lattice $J_1$-$J_2$ Heisenberg model up to $16 \times 16$ clusters, our approach provides strong numerical evidence of Kibble-Zurek mechanism across the Néel-to-spin-liquid transition at $J_2/J_1=0.49$, yielding ${\nu=1.23(15)}$ and ${\eta=0.409(19)}$ at fixed $z=1$, consistent with static estimates and supporting the proposed continuous critical behavior.

Crack-tip fields govern deformation localization and failure initiation. Classical continuum fracture mechanics describes these fields through theories such as the Hutchinson-Rice-Rosengren (HRR) field for nonlinear power-law solids. However, continuum descriptions break down near cracks in soft and architected materials, where load is transmitted through discrete chains, fibers, or struts. Here, we develop a discrete crack-tip theory for lattice networks with nonlinear chains. The theory has two central components. First, at large deformation, the strain of a representative chain in layer $i$ depends approximately linearly on the applied macroscopic strain, $\varepsilon_i\approx k_i(\lambda-1)$, defining a layer-dependent strain-amplification factor $k_i$. Second, along topology-selected chain directions, termed discrete HRR lines, the layer-to-layer ratios of $k_i$ follow a two-regime scaling law. Together, for a power-law chain force-strain relation with exponent $p$, the inner discrete regime predicts $\varepsilon_i\sim i^{-1/p}$ and $f_i\sim i^{-1}$, which differs from the classical continuum HRR prediction. The theory also explains why the intrinsic fracture energy approaches a size-independent limit as the network size increases. Photoelastic hydrogel experiments further validate our theory. These results reveal a two-regime crack-tip scaling law in nonlinear lattice networks and provide a framework for predicting chain deformation and intrinsic fracture energy.

Reduced exciton mass ($\mu$) was recently reported to correlate strongly with a framework distortion in a series of nine single-layer (2D) metal-halide perovskite (HOIP) compounds. Specifically, $\mu$ was observed to increase in tandem with an alternating PbI4 octahedral tilt about an in-plane axis. In this work, we use group representation theory to decompose the observed framework distortions into displacive symmetry modes of a common high-symmetry parent framework. We find that all nine distorted frameworks involve linear combinations of the same six symmetry modes, which have been reported to contribute to the framework distortions of a wide range of HOIP compounds. We show that these modes have highly correlated impacts on the band structure. To differentiate causation from correlation, we vary the amplitude of each mode independently and use density-functional theory to determine the resulting electronic band structures, from which $\mu$ is extracted. We find that bond-transverse displacements of the equatorial halide atoms increase $\mu$, while bond-transverse displacements of the apical halide atoms decrease it. Bond-axis displacements appear to have little or no effect on $\mu$. Our results demonstrate three new structure-property relationships, revealing a promising new avenue for exciton engineering in layered perovskite materials.

Dynamic self-consistent field theory (DSCFT) provides an efficient continuum framework for studying structure formation in inhomogeneous polymer systems, but its predictive accuracy depends on the choice of the nonlocal mobilities. Here, we construct mobility functions for moderately and strongly entangled homopolymer and diblock copolymer systems from the relaxation dynamics of single-chain structure factors, based on molecular dynamics (MD) simulations of the Kremer-Grest model and analytical reptation theory. Single- chain mobilities are combined such that the resulting DSCFT accounts for the dependence of fluxes on local chain densities. The theory is then applied to the spinodal decomposition of symmetric homopolymer blends and diblock copolymer melts following a quench into the (micro)phase-separation regime. Predictions of DSCFT are systematically compared with MD simulations. Mobility functions derived from single-chain dynamics are found to reproduce the kinetics of structure formation more accurately than conventional Debye-type mobilities. We additionally investigate the influence of adding stochastic currents (noise) that are correlated according to the fluctuation-dissipation relation. At low noise levels, they enable the generation of equilibrium initial states and facilitate defect annealing. At high noise levels, however, nonolinear effects lead to discrepancies between DSCFT and MD simulations.

Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for equilibrium and non-equilibrium stresses as well as evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we present a data-driven multi-material constitutive modeling framework that generalizes a formulation by Bergström and Boyce. The proposed framework retains the structure of the classical model, namely multiplicative kinematics, invariant-based strain-energy functions, and a scalar dissipative evolution law directed along the normalized nonequilibrium deviatoric stress. For the equilibrium branch, the data-driven discovery framework either directly predicts closed-form model parameters as functions of composition or automatically constructs a polyconvex strain-energy function using neural ordinary differential equations (NODEs). The nonequilibrium branch kinetics are learned similarly, either by directly identifying closed-form parameters across compositions or by using appropriately constrained artificial neural networks. Using multi-rate uniaxial compression data across multiple material compositions, we show that the proposed formulation captures rate-dependent stiffness and hysteresis across compositions while preserving thermodynamic consistency.

The discovery of superconductivity in Ruddlesden-Popper nickelates has raised a central question: how does layer architecture shape the electronic, magnetic, and lattice interactions relevant to pairing? Here, we report a detailed comparative study of the two polymorphs of La3Ni2O7–the alternating monolayer-trilayer (LNO-1313) and bilayer (LNO-2222) structures–and the related trilayer compound La4Ni3O10, using both Ni L3- and O K-edge RIXS. We find that LNO-1313 and La4Ni3O10 share strikingly similar electronic, magnetic, and lattice excitations, whereas bilayer LNO-2222 exhibits distinct features. Compared to LNO-2222, LNO-1313 and La4Ni3O10 have weaker orbital polarization, enhanced 3d8L character, a reduced out-of-plane magnetic-exchange scale, and stronger EPC. Within an effective local-moment framework, an entangled-dimer scenario provides a natural description of the spin excitations generated by strong antiferromagnetic interlayer coupling. Its advantage over conventional spin-wave theory is clearest in bilayer LNO-2222, where the interlayer coupling dominates the intralayer interactions. These findings provide critical experimental constraints for future theoretical models for the low-energy physics relevant to superconductivity in these layered nickelates.

This work describes the calculation of spin-orbit spillage from a crystal structure. Spin-orbit spillage provides a measure of the likelihood that a material has topological character. The spillage also provides the reference quantity for the machine-learning classifier of Choudhary et al., which predicts whether the spillage exceeds a specified threshold rather than the calculation of its numerical value directly. The complete computation workflow was applied to the insulating compound BaMg2Bi2, yielding a spillage of 2.094 compared with the published VASP spillage of 2.075, corresponding to a difference of 0.9%. The calculation is described in terms of two Quantum ESPRESSO (QE) calculations of spillage performed with and without spin-orbit coupling, the role of relativistic pseudopotentials, and the subsequent wavefunction-overlap analysis. The limitations of the same calculation procedure for semimetals are also examined.

Triboelectric nanogenerators (TENGs) incorporating metal-organic frameworks (MOFs) have largely been designed around porous architectures, based on the assumption that high internal surface area is the primary driver for triboelectric enhancement. Herein, we demonstrate that a dense, nominally nonporous MOF called ZIF-zni can instead function as an effective high-loading filler within a tribopositive polyurethane (PU) matrix, offering a design strategy for harnessing interfacial electromechanical effects. A 20 wt% ZIF-zni@PU composite delivers an output voltage of 470+/-15 V and a peak power density of 1.31+/-0.03 W m-2 against polydimethylsiloxane (PDMS). The device exhibits stable performance over ~94,000 cycles under a contact force of ~100 N, and it remains operational under higher impact beyond 500 N. This concept enables the demonstration of a proof-of-concept triboelectric floor tile. Combined experimental and theoretical studies indicate that the performance enhancement arises from favourable interfacial polarization, reduced dielectric screening, and surface accessible ZIF-zni rich domains, rather than porosity alone. These features are accompanied by reduced work of adhesion and modified surface roughness, hence improving contact electrification. These findings establish dense MOFs as an effective triboelectric filler and identify interfacial electronic structure and polarization as key design parameters for engineering mechanical energy harvesters and self-powered sensors.

Electrical conductivity (EC) provides important constraints on the composition and volatile distribution of Earth's deep mantle, yet the EC of davemaoite (CaSiO3 perovskite), a major lower-mantle phase, remains poorly constrained. We measured the EC of nominally dry CaSiO3 perovskite at pressures up to 89 GPa and temperatures up to 2200 K using impedance spectroscopy in a laser-heated diamond anvil cell. Conductivity increases with temperature but decreases systematically with pressure and it is substantially lower than previously reported, yet broadly consistent with recent theoretical predictions for oxygen-vacancy-mediated ionic transport. A distinct change in the temperature dependence coincides with the tetragonal-to-cubic phase boundary, revealing a modest enhancement of ionic transport across the structural transition. Along a normal lower-mantle geotherm, dry davemaoite is comparable in conductivity to bridgmanite near the top of the lower mantle but becomes progressively less conductive with depth. Under cold-slab conditions, dry davemaoite is substantially less conductive than dry subducted MORB and cannot account for the observed high-conductivity anomalies. Dry davemaoite therefore contributes little to bulk lower-mantle conductivity.

What controls heat transport when liquid water interacts with ice? Answering this question is essential for understanding water bodies undergoing phase change. Yet experimental progress remains limited by the lack of a minimally invasive methodology for simultaneously resolving temperature fields in coupled liquid water and non-isothermal ice systems. Here, we introduce a physics-based data-assimilation method for reconstructing temperature fields in buoyancy-driven flows interacting with non-isothermal ice. Particle tracking velocimetry provides the liquid velocity field, while thermal and kinematic boundary conditions constrain the inverse problem. The method couples advection–diffusion in liquid water with conduction in ice to reconstruct simultaneous mean temperature fields and quantify heat transport across the water–ice interface, while remaining minimally invasive and compatible with free-surface systems. We demonstrate the method in laboratory experiments in which the temperature range across the liquid water in contact with ice drives cabbeling-induced convection. This framework enables investigation of coupled thermo-fluid dynamics in cryospheric aquatic systems, including heat exchange at the ice-water interface and liquid-phase energetics, with broader applications to phase-change processes in food and energy industries.

We study the stability of collective neutrino oscillations using a distributional approach motivated by the statistical mechanics of Kuramoto synchronization. Treating the ensemble of neutrino flavor polarization vectors in the thermodynamic limit $N\to\infty$, we derive an exact nonlinear Fokker–Planck (continuity) equation for the one-body distribution $F(\vec{\mathbf{S}},\omega,t)$ on the flavor sphere. This equation admits a two-parameter family of azimuthally symmetric stationary solutions, whose stability we analyze by linearizing around them. The resulting eigenvalue condition determines the growth or decay rate of small perturbations from any initial distribution – not merely from a state close to full flavor coherence – thereby going significantly beyond the conventional linear stability analysis of collective modes. In special limits the condition reproduces known synchronization thresholds in the two-beam model, providing a non-trivial check of the framework. We present analytical results for the eigenvalue equation and explore stability phase diagrams for physically relevant frequency distributions.

Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of distinguishable compounds by roughly three orders of magnitude, making high-throughput screening of thermodynamic properties tractable across soft matter, with drug–membrane permeability as a well-developed example. The compression is lossy and, so far, one-way: a screen returns a combination of beads, with no established route back to the compounds it stands for. Recovering those compounds–compositional backmapping–is a one-to-many inverse map, distinct from the better-studied conformational problem of rebuilding atomic coordinates from a known mapping. Here we formulate compositional backmapping as conditional graph generation by introducing juniper, a discrete denoising diffusion model over molecular graphs conditioned on the octanol–water partition free energy $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$, the principal driver of MARTINI bead type assignment and hence a proxy for bead identity. Trained on molecules of up to 9 heavy atoms mapped onto one or two beads, juniper generates molecules that are 93% valid and 92% unique for two-bead targets, and whose $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$ distributions track the target $\Delta G^{\mathrm{CG}}_{\mathrm{W} \mapsto \mathrm{O}}$ linearly ($r^{2} \geq 0.96$), departing only in the hydrophobic and hydrophilic tails. Although the model receives no chemical information beyond a single scalar, the functional groups shift systematically with the imposed free energy, from branched hydrocarbons at the apolar end to amides, imides, and isocyanates at the polar end. A bead combination flagged by a CG screen can therefore be turned into candidate molecules for atomistic study or synthesis.

Multi-material PolyJet printing produces voxel-scale digital mixtures of an elastomeric photopolymer (Agilus) and a glassy photopolymer (Vero), giving a material family whose room-temperature response ranges from elastomeric to glassy; a unified description spanning the family has remained challenging. Large-deformation uniaxial compression over nearly three orders of magnitude in strain rate reveals a nonlinear, rate-dependent load-unload response that evolves continuously with composition, from recoverable elastomeric hysteresis to glassy yield with post-yield softening, hardening, and substantial residual strain. Dynamic mechanical analysis (DMA) shows that each mixture has a single glass transition temperature ($T_g$) that shifts to higher temperature with both Vero fraction and frequency, so composition acts much as temperature or loading rate does. This time-composition equivalence motivates one constitutive structure for the whole family rather than a separate property set per mixture: an equilibrium hyperelastic network with three non-equilibrium, rate-dependent branches carrying reptational, intermolecular, and glassy resistance. Its properties are anchored at the two endpoints and interpolated by smooth composition scaling laws. The model captures the compression response of all seven calibrated compositions and predicts a withheld mixture from the scaling laws alone. Resolving the predicted stress into its branches shows the load passing from the elastomeric to the glassy mechanism, and the work from elastic storage to dissipation, as Vero content and rate rise. The glass transition that DMA maps at small strain therefore governs the large-strain deformation mechanisms. The framework gives a compact, physically based description of digitally mixed polymers and a predictive basis for designing functionally graded, architected multi-material structures.

Modern atomistic simulations increasingly employ localized orbitals to represent quantum operators, yielding sparse block matrices whose block shapes vary with chemical species and basis choice. Conventional scalar sparse formats store the entries of each block individually, obscuring this local structure and limiting the use of efficient block algorithms. We present VBCSR, a distributed sparse matrix library that preserves variable-size atomic blocks and accelerates the core linear algebra of large-scale atomistic simulations. A unified interface automatically maps scalar, uniform-basis, and multispecies operators to compressed sparse row (CSR), block sparse row (BSR), or variable-block compressed sparse row (VBCSR). Our advanced acceleration method groups blocks of equal shape and dispatches them to optimized dense kernels. In the reported benchmarks, VBCSR outperforms the tested Python-accessible reference implementations for several block-sparse benchmarks. We further demonstrate VBCSR in an InP nanoparticle application containing more than \(10^6\) atoms.

Eukaryotic cells rely on membrane-mediated processes to compartmentalize biomolecules, counteracting diffusion-driven homogenization. These processes involve the selective sorting and packing of molecules into lipid vesicles, which are then dispatched to appropriate intracellular destinations. Previous works introduced an abstract statistical physics framework for studying this molecular distillation process, where the membrane was treated as static and the focus was on molecular aggregation and extraction. Here, we extend this framework to explicitly incorporate dynamic membrane behavior, including bending, curvature generation, and changes in membrane size due to vesicle fusion and fission events. Sorting domains drive membrane curvature, leading to vesicle formation, detachment, and a molecular distillation process that alters membrane size. Using mesoscopic modeling and numerical tools, we investigate the resulting interplay between membrane mechanics and molecular sorting. We determine a well-defined parameter region where vesicle fission and efficient molecular sorting occur, controlled by membrane rigidity, spontaneous curvature, and pressure difference across the membrane. We further identify a trade-off between speed and quality of molecular distillation: parameters that accelerate vesicle formation tend to reduce the quality of distillation, and vice versa. We propose maximization of the rate of negative entropy production as a natural criterion to optimally balance these competing effects. In this context, optimal parameters emerge naturally from the coupled dynamics of molecular aggregation and membrane mechanics, suggesting a possible strategy for cellular sorting systems to strike an efficient balance between rapid vesicle formation and high sorting quality.

Fukui and Kawakami showed that the trigonometric Haldane–Shastry (HS) spin chain can be deformed by introducing twisted boundary conditions. In this work we revisit the resulting twisted HS chains, which we call Fukui–Kawakami (FK) chains. We analyse their spectra in dependence of the twist parameter, utilising a direct connection with the (untwisted) HS chain. This allows us to explain why part of the spectrum can be described by Fukui and Kawakami's twisted Bethe equations, and can be constructed from Yangian highest weight states. These states can be labelled by combinatorial data called `motifs', as in the HS chain, which cover some of the (deformed) descendants. We furthermore show that there are other descendants which do not follow from these Bethe equations, but whose energy can be described as a sum of two single-particle energies, suggesting additional hidden symmetries. We then focus on the special case of antiperiodic boundary conditions, and show that this chain coincides with the `minimally polarised' long-range model recently introduced by Basu-Mallick, Finkel, and González-López. Remarkably, this connection implies that the antiperiodic chain has a hidden $\mathfrak{gl}(1|1)$-symmetry, which we use to relate the HS motifs to their `supersymmetric' $\mathfrak{gl}(1|1)$ counterparts.