Papers

Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable crystals remains challenging. Oxidation states are widely used to assess the compositional validity of crystals and guide inorganic materials discovery. While existing generative models for crystals can generate materials with charge-neutral oxidation-state assignments, they poorly reproduce the distributions of oxidation states observed in synthesised materials. To address this limitation, we propose OxiGen, an oxidation-state-aware crystal diffusion model that explicitly represents oxidation states during generation. OxiGen enforces global charge neutrality by construction using a structured output layer with exact inference over a finite-state automaton. Empirically, OxiGen substantially improves oxidation-state fidelity, generates the highest rate of stable, unique, and novel crystals among evaluated methods, and maintains high compositional validity even under property conditioning.

Machine-learning interatomic potentials enable large-scale atomistic simulations of vanadium alloys relevant to fusion applications, but reliable training and validation remain challenging in multicomponent systems. Here, we develop a descriptor-based DeepMD-DPA1 potential for the V-Ti-Cr system using a two-stage workflow: broad configuration sampling driven by the MatterSim foundation model followed by fine-tuning to density-functional-theory data computed with VASP. The model achieves root-mean-square errors of 9.2 meV/atom for energies and 0.23 eV/angstrom for force components. Using this potential in large-cell LAMMPS simulations, we compute Young's modulus, bulk modulus, and Poisson's ratio for V-4Ti-xCr and V-xTi-4Cr alloys at T = 300 K and T = 1073 K. We find that increasing Cr fraction increases the elastic moduli of the alloy, while increasing Ti fraction decreases them; all compositions are softer at 1073 K than at 300 K. Two-phase simulations give a melting temperature for pure V of 1950 K, in good agreement with experimental data.

We derive the local pressure tensor generated by the original Rosenfeld fundamental measure theory (FMT) for hard-sphere fluids from an arbitrary infinitesimal deformation. The variation of a finite-distance FMT kernel is written via the line integrals of the local deformation gradient along the straight segment connecting its endpoints. The resulting symmetric pressure tensor consists of a local term expressed through the standard FMT weighted densities and a finite-range chord term. For a monodisperse hard-sphere fluid, differential relations between the Rosenfeld measures reduce the tensor to a closed one-body form. We demonstrate that no independent pair density or inhomogeneous Ornstein–Zernike equation is required. In the limit of homogeneous fluid the tensor reproduces the Percus–Yevick compressibility equation of state. In planar confinement the normal stress satisfies the hard-wall contact relation, the mechanical surface tension agrees with the surface excess grand potential, and the disjoining pressure agrees with the derivative of the confined grand potential with respect to slit width. The finite-range chord term extends the deformation method from local field theories to nonlocal weighted-density functionals.

We investigated the microscopic magnetic state of the itinerant-electron ferromagnet LuInCo4 with a cobalt pyrochlore lattice by means of powder and single-crystal neutron diffraction measurements. The powder neutron diffraction profiles at zero field are well described by a simple q = 0 collinear ferromagnetic structure with an ordered Co moment of 0.9 uB. Single-crystal neutron diffraction experiments further suggest the absence of both antiferromagnetic order and long-period helical magnetic modulation. In addition, diffuse magnetic scattering was observed around q = 0, with its intensity distribution gradually broadened in reciprocal space upon cooling. Based on these observations, we propose a possible microscopic origin of the low-temperature metamagnetic transition in terms of a ferromagnetic instability enhanced by gradual localization of the itinerant Co-3d electrons. These results suggest that LuInCo4 provides a model system for investigating the interplay between electron localization and itinerant-electron ferromagnetism in cobalt-based metallic pyrochlore compounds.

The hard disk model is a fundamental reference system for excluded volume physics, liquid structure, and 2D ordering. Yet, despite its apparent simplicity, an analytical formulation of its thermodynamics has remained difficult because the free volume available to disks centers has a highly nontrivial many-body geometry. Here we show that this geometry admits a closed and exact representation: for any given hard disk configuration, the free volume can be analytically expressed through intersection areas of up to five exclusion disks. This provides a direct geometrical route from particle coordinates to the configurational partition function and entropy. We prove that the N disk partition function factorizes into a product of conditional free volumes and identify its two limiting asymptotic forms: the low density fluid regime controlled by extensive cavities and the high density fluid regime controlled by intensive private cells. Using the geometric measures computed from representative equilibrium particle configurations, the resulting theory reproduces the established hard disk equation of state over almost the entire density range up to close packing. At intermediate densities, the analysis reveals a mixed fluid regime in which localized caged defects provide an additional configurational entropy within the density interval preceding liquid hexatic coexistence. Finally, the five disk intersection area is shown to be a sensitive local scalar measure of hexagonal ordering, complementing the orientational order parameter and emphasizing the central role of many-body correlations.

Accurate transport properties calculations in transition metals require treating inter-orbital hybridization, which is often poorly captured by simplified single-band models. We propose an ab initio approach combining maximally localized Wannier functions and the Allen's method to correctly account for this mixing, overcoming the artifacts inherent to the Souza-Marzari-Vanderbilt scheme. Our method enables efficient orbital-resolved analysis of transport spectral functions. Applied to face-centered cubic Pd and body-centered cubic Mo, we reveal universal scattering mechanisms despite distinct electronic structures in these metals. Crucially, the parallel addition of distinct scattering channels perfectly reproduces full Allen's method calculations, rigorously validating this decomposition. We show that the absence of a $d$-peak at Fermi level in Mo enhances $s$-$s$ scattering, while the same contribution to resistivity and electron thermal conductivity of Pd is close to the experimental data for Ag. Ultimately, the proposed method provides a universal and efficient tool for the microscopic analysis of electron-phonon scattering mechanisms in systems with strong hybridization between localized and delocalized electron states.

Nonlinear neural networks develop structured internal representations, yet how their geometry is determined by the tasks being learned remains poorly understood. Here, we develop an analytical framework for piecewise-linear feedforward and recurrent networks that links learning, activation-region structure, and neural selectivity. We show that, at any stationary point of gradient-aligned learning, a nonlinear network decomposes into local linear regressions over its activation regions. Deviations from the corresponding least-squares solutions are jointly constrained by network weights shared across regions and vanish in the low-error regime, yielding an approximate piecewise least-squares decomposition of the task. This structure admits a decision-tree interpretation, which we recover numerically by fitting trees to predict network activation patterns from the input. We further derive how the regions in which neurons are active determine the task statistics they capture, thereby organizing neural selectivity into distinct subpopulations. The resulting predictions closely match the selectivity geometry observed in simulated networks and two empirical neural datasets. Finally, we show that neural baseline regulates activation pattern diversity, placing networks along a continuum between coarse, generalizing representations and fine-grained, expressive representations. Together, these results establish activation regions as a unifying framework for describing how nonlinear networks decompose task structure into local computations and how those computations can be revealed through neural activity.

In this study, we address the seemingly open question of disparity between the experimentally and theoretically observed Raman spectra for the altermagnetic $\alpha$-MnTe. We establish that the consideration of long range (LR) dipole-dipole interaction is crucial to accurately estimate the phonon band structure and thereby capturing each of the experimentally observed Raman signatures. The LR correction brings about substantial changes to the high-frequency optical modes and their group-theoretical representations. Specifically, for the highly debated Raman peak around 175 cm$^{-1}$, alongside the Raman silent B$_{1u}$ mode, the LR corrections introduce Raman-active longitudinal optical A$^{\prime}$ and A$_1$, and transverse optical A$_1$ modes. Overall, the calculated Raman spectra suggest that the intensity of these modes is weak in the pristine system, while certain prescribed distortions can increase their intensity. The Raman peak around 140 cm$^{-1}$, as observed experimentally, is identified to be emerging from a higher-order Raman process due to zone off-center contributions, while the other debated 120 cm$^{-1}$ peak has both the zone center and off-center contributions.

Caloric solid-state refrigeration offers an energy-efficient alternative to vapor-compression cooling, but many of the most promising materials, including the magneto- and elastocaloric Heusler alloys, are intrinsically brittle and fail under the cyclic loading required in operation. Here, we introduce mechanical confinement via powder-in-tube (PIT) processing as a general strategy to overcome this limitation. By embedding a granular Heusler alloy core of 87 % packing density within a ductile steel sheath (2.8 mm outer diameter, 0.3 mm wall thickness), the composite confines the brittle powder and enables effective load transfer between particles. While the core remains inherently brittle, the surrounding sheath prevents catastrophic fragmentation and turns brittleness from a disqualifying property into a manageable one. We demonstrate that this approach enhances mechanical durability by several orders of magnitude, with the composites sustaining 100,000 load cycles at 250 MPa without structural failure and tolerating overload stresses up to 700 MPa. Confinement also alters the transformation itself, and to resolve this behavior in these mechanically complex systems, we developed a simultaneous in-situ measurement technique combining strain and AC magnetic susceptibility under load. It reveals that the internal stress field in the core is strongly heterogeneous. As a consequence, the macroscopic strain decouples from the transforming phase fraction, and the martensitic transition cannot be tracked by mechanical data alone. Mechanical confinement thus emerges as a design principle for fatigue-resistant caloric materials, and the combined in-situ method as a characterization tool for composite, porous, and other mechanically heterogeneous caloric systems.

The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their crystals. That structure is in turn set by a recipe, the alloy composition together with the processing parameters. Alloy development runs this chain forwards, tuning the structure until a target property is met. Running it backwards, from an optimized structure to the recipe that would produce it, still relies on expert knowledge. We ask whether this backwards step can be learned. On an in-house dataset of 107 magnesium alloy extrusion conditions across 14 alloys, each with optical micrographs and an X-ray texture measurement, we compare three descriptors of microstructure and texture: conventional grain and texture statistics, a vision embedding from a pretrained image encoder, and a graph neural network on the grain network. Each is paired with prediction heads for two tasks: the alloy composition given the process (Task A), and the process parameters given the composition (Task B). Under 5-fold cross-validation, the conventional descriptors identify the correct alloy for 65% of held-out conditions, against 17% for always guessing the most common alloy, while the learned embeddings stay below 30%. The process parameters are recoverable but noisier: compared with using the composition alone, the microstructure roughly halves the temperature error. Because only a few alloys were cast and only a few press settings were used, both answers are discrete, and heads that pick from these known options, while respecting their order, worked better than heads that predict a free value.

We study size fluctuations of chemically active droplets in the regime of weak reactions and strong phase separation. By combining a thin-interface model for the deterministic droplet growth with stochastic noise based on fluctuation-dissipation arguments, we report excellent quantitative agreement between our theory and numerical field simulations. Finally, we use our model to infer the effective non-equilibrium potential that governs droplet growth, and show that active reactions that enable size control suppress nucleation.

In this study, we propose a kinetic trap–diffusion model to describe the emergence of Pareto distributions in money-exchange systems. Using kinetic Monte Carlo simulations, we show that the Pareto exponent depends explicitly on temperature and takes values in the range $0.5 \leq \nu(T) \leq 1.5$. In the present framework, the temperature $T$ acts as a control parameter that regulates the exchange dynamics through thermally activated diffusion. Unlike conventional kinetic exchange models, where the Pareto exponent is fixed by microscopic rules, the proposed model generates a range of Pareto exponents as a function of $T$. The temperature-dependent Pareto exponent constitutes the central novelty of the proposed kinetic trap–diffusion framework. This feature provides a natural explanation for the empirically observed variations in Pareto exponents across different countries and economic conditions. In addition, the fraction of zero-wealth agents and the Gini index exhibit a non-monotonic dependence on temperature, revealing distinct dynamical regimes arising from the competition between trapping and money mobility. The persistence of the Pareto-like stationary distribution under strongly nonuniform initial conditions further supports the robustness of the proposed mechanism. These results show that different Pareto-tail and inequality regimes can emerge from the same trap–diffusion dynamics through changes in a single control parameter

We propose an algorithm for identifying low-energy minima in systems containing thousands of particles, at moderate computational cost. We focus on spherical crystals, in which particles form ordered structures on the surface of a sphere. Our method uses symmetric seed configurations, or known structures, to initialize searches across contiguous ranges of system sizes. Newly identified minima are then used to guide searches at neighbouring sizes, allowing information to propagate efficiently through configuration space. We have tested this approach on the Thomson problem, one of the few interacting-particle systems for which global optimization has been considered for systems of this size. We substantially improve on many previous solutions, particularly at larger system sizes, where systematic global optimization is computationally expensive. Since favourable packings for the Thomson problem are reflected in systems spanning atomistic to mesoscopic length scales, the newly characterized defect patterns may inform structure prediction for a broad range of problems with spherical topology.

A machine learning (ML) framework for phase-diagram construction in condensed matter systems with unknown phase structure is presented. The method combines physically motivated descriptors with contrastive representation learning and a two-stage data-generation workflow. First, a sparse set of configurations is generated by Monte Carlo simulated annealing in order to train a contrastive neural network that produces a low-dimensional embedding. Consequently, representative phase configurations are used as initial conditions for gradient-based optimization on a dense parameter grid, enabling efficient generation of refined datasets for high-resolution phase-diagram construction. In order to optimize the workflow, a variant of the method, in which derived descriptors are used as soft labels in the contrastive objective, is examined as well. The framework is demonstrated on a PbZrO3-inspired model potential formulated in terms of coupled order-parameter fields and used as a physically motivated testbed for exploring configuration diversity and phase behavior. The proposed approach provides a physically informed route to ML-assisted phase-diagram construction that integrates data analysis with adaptive dataset refinement.

Magnonics investigates the dynamic excitations of magnetically ordered media, known as spin waves, and their application in functionalized devices. To efficiently control spin wave based devices minimizing losses and interferences from unwanted reflections, a clear picture of the scattering mechanism is needed. Here, we focus on the reflection of spin waves produced at the interface between two ferromagnetic media. We show that it is possible to understand the reflection mechanism based on the continuity of the angular momentum density flux. This constraint allows us to analytically compute the reflection coefficient and the stationary wave envelope in the first medium, as well as the damped propagation in the second. In addition, the analytical expressions are compared with full micromagnetic simulations, showing excellent agreement.

High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$ highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.

Hematite ($\alpha$-Fe$_2$O$_3$) is a prototypical antiferromagnetic platform for high-speed spintronics and magnonics, yet its finite-momentum magnon dynamics and spin-reorientation mechanism remain incompletely understood. Here we use low-wavenumber magneto-Raman spectroscopy to resolve both $k=0$ and finite-$k$ sub-terahertz magnons in $\alpha$-Fe$_2$O$_3$ across the Morin transition. We find that the limiting group velocity $v_0$ remains nearly field independent, whereas the finite-$k$ group velocity $v_{\mathrm g}$ is strongly modified near the Morin and spin-flop transitions. By combining parallel- and transverse-field measurements with spin-wave modeling, we further extract the temperature-dependent Dzyaloshinskii–Moriya field $H_{\mathrm D}$ and uniaxial anisotropy fields $H_{\mathrm{K1}}$ and $H_{\mathrm{K2}}$. We find that $H_{\mathrm D}$ is weakly temperature dependent at approximately 2.0–2.3~T, while $H_{\mathrm{K1}}$ decreases rapidly and $H_{\mathrm{K2}}$ changes only weakly. This contrast drives the sign reversal of the effective anisotropy field and quantitatively accounts for the Morin transition. Our results establish low-wavenumber Raman spectroscopy as a quantitative probe of finite-momentum antiferromagnetic magnons and provide key material parameters for hematite-based magnonics.

We study the dynamics of a run and tumble particle (RTP) in a bistable potential, in contact with a heat bath. We find the exact form of the steady state position distribution as well as its time-dependent form in Laplace space. We use this result to study relaxation properties of the spatial probability density and find that it converges to the steady state from the center outwards, with the relaxation front moving ballistically in time. Physically this ballistic behavior arises when observing relaxation properties at the tails of the density, where the distance of the particle from the origin is larger than the length-scale describing the steady state of the RTP. We also find the exact form of the mean first passage time, and the long-time behavior of the first passage time distribution. This allows us to coarse-grain the dynamics as a dichotomous Markov process, a paradigm for a wide class of bistable processes. Coarse-graining, however, comes at the cost of a loss of information: while the RTP possesses a finite entropy production even at steady state, it identically vanishes for its coarse-grained description.

We study a heterogeneous diffusion process with a position-dependent diffusion coefficient in the presence of independent recruitment and mortality. This framework generalises heterogeneous diffusion under stochastic resetting, which is recovered as the special case of equal recruitment and mortality rates. At long times, the system approaches a non-equilibrium stationary state with a mean squared displacement that saturates to a value independent of the recruitment rate. Crucially, this saturation limit directly encodes the underlying transport regime via an effective exponent applied to the diffusion coefficient, and we characterise the non-trivial transition to this stationary regime. We further derive the mean first-passage time to a target and show that, for a fixed recruitment-to-mortality ratio, an optimal mortality rate minimises the search time. This "mortality-assisted search" effect is confirmed by numerical evaluation. Reinterpreting recruitment and mortality as infection and removal processes, we apply the model to epidemic spreading and show that a moderate removal rate can counter-intuitively accelerate, rather than delay, the arrival of infection at a new spatial region. Beyond epidemiology, our framework offers broad relevance to systems featuring concurrent particle creation and destruction, such as bacterial growth, tracer diffusion in reactive fluids, or cellular population dynamics in crowded environments.

Aluminum nitride (AlN) is an important thermal management material whose high lattice thermal conductivity is strongly suppressed by oxygen impurities. We investigate phonon scattering by oxygen-related defects using pretrained universal machine-learning interatomic potentials (MLIPs), molecular dynamics (MD), and phonon Boltzmann transport calculations. Several pretrained MLIPs are benchmarked against density functional theory for phonon dispersions and pristine thermal conductivity. To balance accuracy and computational speed, we use a fine-tuned version of the compact SevenNet-Nano model for MD simulations. Monte Carlo annealing supports the formation of bound $V_{\mathrm{Al}}(\mathrm{O_N})_3$ complexes, whose scattering differs from that of their isolated constituents. Defect scattering rates extracted from excess spectral energy density (SED) linewidths agree reasonably with harmonic $T$-matrix predictions at low oxygen contents, supporting the independent-scatterer approximation. Incorporating these rates into an iterative Boltzmann transport equation with Bose–Einstein statistics yields thermal conductivities comparable to experimental values and captures their observed decrease with oxygen content. At high concentrations, however, the scattering rate deviates from linear scaling with oxygen content, suggesting limitations of independent-defect and pristine-phonon descriptions. These results demonstrate an efficient approach using pretrained MLIPs to quantify defect-limited thermal transport and provide insights into impurity effects beyond the dilute-defect approximation.

A local measurement of a critical many-body state produces exponentially many microscopic outcomes, each defining a different conditioned quantum state. We ask how much information from one such outcome is actually needed to predict the entanglement that remains between two unmeasured regions. We show that, in critical quantum chains, the universal outcome-resolved Rényi entropy is asymptotically determined by a single compact conformal coordinate extracted directly from the measurement record. Compact-boson theory explains this compression: the universal entropy is insensitive to all spatially varying components of the boundary profile, retaining only a single relative compact zero mode. At the free-fermion XX point, exact lattice identities expose the microscopic structure underlying the decoder, while large-scale outcome-by-outcome calculations confirm its scaling prediction across different subsystem geometries and Rényi indices. Numerical calculations in interacting XXZ chains further show that the same microscopic coordinate continues to decode the conditional entropy without interaction-dependent retuning. Thus an exponentially large space of measurement outcomes is reduced, for this universal observable, to a single conformal degree of freedom, revealing an emergent Luttinger-liquid mechanism for outcome-resolved postmeasurement entanglement.

High-temperature creep in metals remains challenging due to the complex interplay of deformation and failure mechanisms across multiple length scales. The trade-off between inter- and trans-granular cracking governs the transition of creep rupture behaviour in metallic materials. However, the micromechanical basis of the transition and its linkage to the macroscopic brittle to ductile transition in creep rupture behaviour remain unresolved. Here, the stress-dependent competition between grain-boundary cavitation and slip-induced intragranular cracking is revealed via a unified crystal plasticity fracture framework. The framework incorporates crystal plasticity, grain-boundary cavitation, and crystallographic crack propagation within an explicit polycrystalline microstructure. The microscale computation reveals the transition from trans-granular-dominated cracking at high stress levels to mixed inter- and trans-granular cracking at intermediate stresses and inter-granular-dominated cracking at lower stresses, accompanied by distinct damage localization modes and the evolution of crack paths from grain boundary to grain-interior propagation. The results further quantify the linkage between microscopic fracture mechanisms and macroscopic creep rupture behaviour, including changes in rupture ductility and creep life. Under high-stress conditions, deformation-driven trans-granular damage localization promotes ductile rupture through extensive strain accumulation, whereas under lower-stress conditions, time-dependent grain-boundary damage accumulation leads to quasi-brittle rupture through localized inter-granular fracture.

Despite purely local microscopic dynamics, nonequilibrium steady states can develop long-range correlations. We investigate this phenomenon in the boundary-driven symmetric simple exclusion process by relating its exact nonlocal large-deviation functional to finite-time thermodynamic irreversibility under weak time-dependent driving. Decomposing the long-range correlations into hydrodynamic modes, we find that the lag associated with the slowest mode satisfies an upper bound determined by the excess entropy production rate at each driving frequency. This bound is approached in the quasistatic limit, whereas progressively shorter-wavelength modes do not generally satisfy the corresponding bound. These results provide a quantitative connection between the curvature of the large-deviation functional, long-range correlations in the nonequilibrium steady state, and finite-time dissipation. Within the linear-response regime considered here, they indicate that the longest-wavelength hydrodynamic mode has a distinct relation between slow relaxation and thermodynamic irreversibility.

How a mobile impurity exchanges momentum and energy with a many-body environment is a central question in nonequilibrium quantum physics. This exchange becomes particularly complex when the impurity moves rapidly and is strongly coupled to its bath. Here, we realize an interaction-tunable cold-atom collider with fermionic $^{40}$K impurities immersed in a $^{23}$Na Bose–Einstein condensate. A species-selective Raman pulse simultaneously launches the condensate and quenches the interspecies scattering length, producing an initial relative speed 36 times the condensate speed of sound. We track the ensuing relative motion as the interspecies interaction is tuned from weak coupling to resonance. At weak and intermediate coupling, the impurity dynamics are well described by a finite-energy two-body collision model. Near resonance, however, the early-time impurity acceleration is markedly suppressed relative to the two-body prediction, corresponding to a strong enhancement of the apparent dynamical inertia. The enhancement is observed near two distinct Feshbach resonances. These observations provide evidence for a polaronic response in the strongly coupled, supersonic regime of a degenerate Bose–Fermi mixture.

Accurate modeling of p-type dopants in 4H-SiC is essential for understanding the mechanisms governing doping efficiency and carrier transport. In this work, we revisit the electronic structure of boron- and aluminum-related acceptors using hybrid density functional methods. Besides defect formation energies and thermodynamic transition levels, we present a quantitative look into the carrier capture kinetics within the multi-phonon emission framework. Our results reveal striking differences between the two most relevant p-type dopants. While $\textrm{B}_{\textrm{Si}}$ and $\textrm{B}_{\textrm{C}}$ exhibit nearly identical formation energies, consistent with the occurrence of both defects, the formation energy of $\textrm{Al}_{\textrm{C}}$ under intrinsic conditions is approximately 6.5 eV higher than that of $\textrm{Al}_{\textrm{Si}}$, confirming previous findings that the former is unlikely to occur. We further find that $\textrm{B}_{\textrm{Si}}$ possesses large electron and hole capture cross sections, identifying it as a plausible source of minority-carrier lifetime degradation of n-type material contaminated with boron. In addition, we predict a previously unexplored donor transition for $\textrm{B}_{\textrm{C}}$. If experimentally confirmed, this defect would represent additional problems to both p-type doped and boron contaminated 4H-SiC, not only because of its ineffectiveness as an electric dopant, but also due to trapping of up to two free holes, reducing the free-hole concentration and increasing scattering effects.

Metal-organic frameworks (MOFs) are widely explored for gas separation, catalysis or energy storage, yet their integration with other electronically functional materials remains largely unexplored. Here, we integrate the layered Hofmann-type spin-crossover (SCO) MOF {FeII(pyS2Et)2[PtII(CN)4]} into electrical nanodevices to control the properties of two-dimensional (2D) quantum materials. Our molecular approach exploits the stimuli-responsive nature of SCO layered materials as switchable building blocks. We demonstrate selective modulation of the electronic transport in van der Waals heterostructures interfacing SCO with 2D quantum materials (few-layer graphene, magnetic CrSBr, superconducting NbSe2) through the strain induced via thermal and light-induced spin transitions. In graphene, the conductivity is selectively switched by light. In spin-valves based on CrSBr bilayers, the MOF triggers magnetic hysteresis (absent in pristine CrSBr) and enables tunable non-volatile zero-field memory. In NbSe2, light modulates the superconducting critical current and transition temperature. These results establish layered stimuli-responsive SCO MOFs as active molecular control elements for 2D quantum materials, providing a route to strain-mediated control of electronic, magnetic, and superconducting functionalities and extending MOF-based architectures beyond traditional porous-matter applications towards multifunctional electronics and spintronics.

Understanding the formation and dynamics of two-exciton bound states (BSs) is crucial for harnessing multi-exciton phenomena and designing next-generation organic semiconductor materials. While various mechanisms such as intermolecular exciton-exciton interactions or multi-level intramolecular configurations can generate these BSs, a fundamental understanding of their underlying lattice dynamics remains a key challenge. Within this context, the Merrifield exciton model stands out as a premier framework for capturing the intricate quantum mixing between single-site doubly-excited states and two-particle scattering continuum. In this work, by constructing a set of exact two-exciton Bloch states and applying a plane-wave ansatz to treat the resulting inhomogeneous tridiagonal matrices, we provide a complete, rigorous solution to the two-exciton problem in a finite-size Merrifield chain. We analytically map the parameter regions that support two-exciton BSs, proving that at most two distinct types of BSs can emerge, located either above or below the scattering continuum. Furthermore, we provide a quantitative interpretation of how these two types of BSs evolve as a function of the total wave number within the whole Brillouin zone. The obtained closed-form expressions for the eigenenergies and wave functions offer valuable benchmarks for exploring exciton dynamics and optical responses in related molecular aggregates.

We propose a microscopic mechanism for the negative charging energy in LaAlO$_3$/SrTiO$_3$ quantum dots, arising from the electrostatic image charge associated with the paraelectric response of SrTiO$_3$. Using a self-consistent configuration-interaction approach that accounts for both Coulomb repulsion and image-charge interactions, we find a robust regime of electron pairing whose strength is tunable through the confinement potential and the effective image-charge distance, set by the thickness of the interfacial two-dimensional electron gas. Our results establish dielectric image-charge effects as a route to controllable electron pairing in oxide quantum dots and other materials with a strong dielectric response.

A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Gan Jiang, a self-learning agent for powder X-ray diffraction built on a diffraction-analysis ecosystem we developed: XMatcher, XQueryer, XDecomposer and WPEM. Together, these engines span phase identification, multiphase decomposition and physics-constrained whole-pattern modelling. Gan Jiang converts analytical experience into executable skills by diagnosing failures, revising skill instructions and code, and validating revisions before reuse, without retraining the language model or changing the underlying physical models. Skills selected using development data and frozen before held-out evaluation achieve higher refinement scores than the original expert-designed skills across FullProf, GSAS-II and PyWPEM. The agent resolves strongly overlapping reflections, quantifies a five-phase ancient Egyptian cosmetic, tracks lattice evolution in an operating battery and compares atomic configurations in a disordered oxide catalyst. On DeltaXRDbench, it leads the evaluated methods in single- and multiphase identification across simulated and experimental data. Without supplied composition, single-phase top-1 accuracies reach 96.30%, 81.78% and 40.83% on MP500, RRUFF and opXRD, respectively, compared with 58.00%, 58.47% and 26.45% for the strongest comparator. These results demonstrate how an integrated scientific tool ecosystem can support agents that extract structural knowledge from measurements while accumulating validated analytical expertise that transfers to new samples.

We investigate single-excitation quantum walks across the one-to-two-dimensional crossover in diluted power-law hopping models. Using arrays of $\ell$ coupled chains of length $L$, we identify the finite-size localization crossover from the longitudinal spreading of an initially localized excitation. The crossover filling is well described by an exponential approach to its two-dimensional value with increasing $\ell$, defining a characteristic transverse scale $\ell_e$, and effectively two-dimensional behavior emerges already for $\ell\ll L$. An independent spectral analysis shows that $\ell_e$ is set by the transverse extent of the eigenstates participating in the dynamics, providing a microscopic interpretation of the dimensional crossover. Our results reveal an intrinsic eigenstate scale governing emergent dimensionality in diluted systems with power-law hopping, a regime directly relevant to recently developed arrays of polar molecules, Rydberg atoms, and other quantum simulators with long-range interactions.