Papers

Quadratic Unconstrained Boolean Optimization (QUBO) problems are widespread in both industrial applications and scientific studies. A QUBO problem corresponds to the optimization of a system of Ising spins defined on a generally sparse and heterogeneous graph. When the QUBO problem contains conflicting requests, the corresponding Ising system is frustrated, generating a complex energy landscape, which is hard to explore and optimize. Despite extensive algorithmic and hardware developments, finding low-energy configurations in these systems remains challenging (e.g., local-update heuristics typically become trapped in metastable states), especially when the (possibly frustrated) interactions generate extended correlated domains. We introduce CluMP (Cluster-based Message-Passing), an algorithm that performs collective updates on connected clusters of spins using information from Belief Propagation (BP). By controlling the amount of frustration within clusters, CluMP enables BP convergence on large subgraphs and proposes nonlocal rearrangements involving up to hundreds of spins in a single move. We benchmark CluMP against state-of-the-art local-update heuristics on spin-glass models defined on several graph topologies, including random regular graphs and lattice regular graphs in two and three dimensions. Cluster moves consistently bypass local trapping and reach lower energies with fewer effective operations than single-spin dynamics. These results demonstrate that frustration-tolerant cluster updates can be implemented efficiently on sparse graphs. The CluMP framework provides a scalable strategy for large-scale combinatorial optimization and inference problems, where exploiting medium- and long-range correlations is key to navigating complex energy landscapes.

Liquid crystal elastomers (LCEs) exhibit unique viscoelastic behavior arising from reversible liquid-crystalline ordering, making them attractive candidates for switchable pressure-sensitive adhesives (PSAs). However, methacrylate-based LCEs are typically highly crosslinked, leading to elevated glass-transition temperatures ($T_g$) and storage moduli ($E'$) that limit adhesive performance. Here, we demonstrate that catalytic chain-transfer polymerization provides an effective strategy for engineering soft methacrylate nematic elastomers through systematic control of network architecture. Incorporation of parts-per-million concentrations of bis(boron difluorodimethylglyoximate)cobalt(II) (CoBF) during photopolymerization reduced the effective crosslink density and increased the molecular weight between crosslinks, producing substantial decreases in $T_g$ and $E'$ while preserving nematic order. Dynamic mechanical analysis revealed that increasing CoBF concentration enhanced viscoelastic dissipation and broadened the accessible nematic temperature window. To further optimize rheological properties for pressure-sensitive adhesion, monofunctional methacrylates and flexible poly(ethylene glycol) dimethacrylate (PEGDMA) were incorporated into the network. The optimized formulation exhibited a $T_g$ near 0~$^\circ$C, a room-temperature storage modulus of approximately 0.3 MPa, and high damping behavior, approaching the Dahlquist criterion for pressure-sensitive adhesion. As a result, the resulting nematic elastomers displayed strong tack, peel, and lap-shear adhesion in the nematic state, together with rapid, reversible, and residue-free debonding upon heating above the nematic-to-isotropic transition temperature.

The topological magnetoelectric effect (TME) is a condensed-matter realization of the four-dimensional quantum Hall effect (4D-QHE), manifesting as quantized surface charge accumulation proportional to an applied magnetic field. To date, however, no optical technique has been developed to directly probe this charge accumulation. Here, we demonstrate a terahertz time-domain spectroscopy method for direct detection of surface charge accumulation—a physical quantity relevant to both the 4D-QHE and 2D-QHE, in sharp contrast to the previous optical measurements, which focused on the Hall conductivity $\sigma_{xy}$ of the 2D-QHE. Using a chromium-doped (Bi,Sb)$_2$Te$_3$ thin film, we achieve sub-milliradian Faraday rotation precision. Extending this scheme to axion insulators, we predict that the TME gives rise to an imaginary Faraday rotation linear in frequency, whose slope directly reflects the single-surface charge density. With further improvements in sample thickness and precision, this approach offers a viable pathway toward direct verification of the TME and 4D-QHE.

Many problems in modern scientific computing are challenging because of a curse of dimension, where their mathematical formulation involves objects whose dimension is exponential in the nominal "size" of the problem. Tensor networks can provide a compact representation for exponentially large vectors and matrices that arise in applications, but these representations do not always lead to reliable algorithms. This paper develops and analyzes techniques for randomized dimension reduction of tensor network data. These techniques support a suite of efficient algorithms for provably solving exponential-scale linear algebra problems, including trace estimation and eigenvalue approximation. The paper includes several stylized illustrations from quantum many-body physics with ambient dimension up to $2^{200}$.

Generative modelling requires efficient stochastic nonlinear transformations and physical platforms that can naturally realise them. We experimentally demonstrate that nonlinear optical systems operating in the strong light-matter coupling regime can serve as physical transformation layers for conditional generative modelling. Specifically, we develop a workflow in which room-temperature exciton-polariton condensates formed in organic dye microcavities act as a physical stochastic transform within a generative adversarial network and enable conditional digit-to-image translation. By using the nonlinear many-body dynamics and intrinsic stochasticity of polariton condensates, the workflow outperforms baseline approaches based on digitally injected perturbations. We find that polariton-enabled sampling via generative adversarial network (Polariton GAN) yields improved inception score, digit preservation accuracy and structural similarity compared with both digital sampling and laser-based systems. We further show that spatially correlated output variations can naturally regularise adversarial training and enhance output diversity. Our results establish polariton condensation as a new computational resource for generative modelling, opening a pathway towards physics-enhanced machine learning systems.

Predicting fundamental band gaps across material classes and dimensionalities remains a central challenge in electronic-structure theory. Here, we show that intrinsic dielectric screening provides a unified control parameter for nonlocal exchange from bulk to low-dimensional and heterogeneous materials. We introduce a geometry-independent dielectric response and incorporate it self-consistently into a nonempirical screened-dielectric-dependent hybrid functional. Benchmarks for 100 materials spanning bulk, two-dimensional, one-dimensional, and mixed-dimensional systems show near-GW accuracy at the computational cost of generalized Kohn-Sham theory. These results reveal a screening-exchange-gap relation in which reduced dimensionality weakens intrinsic dielectric screening, strengthens nonlocal exchange, and drives the opening of fundamental gaps.

The quasi-linear scaling of orbital-free density functional theory (OF-DFT) with system size makes it a computationally efficient alternative to conventional Kohn–Sham density functional theory for many condensed-matter applications. However, its applicability remains limited, particularly for finite systems such as molecular clusters, due to the lack of accurate kinetic energy density functionals. In this context, the development of nonlocal kinetic energy density functionals (NL-KEDFs) has significantly advanced the practical utility of OF-DFT. Here, following an alternative formulation based on the linear-response kernel derived from the jellium-with-gap model (JGM), we develop an NL-KEDF capable of accurately describing the diverse density regimes characteristic of finite systems, including molecular clusters. Benchmark calculations, together with an analysis of the corresponding Pauli potentials, demonstrate that the proposed functional achieves higher accuracy than state-of-the-art orbital-free approaches for finite systems. Furthermore, the optical properties computed using the present method show good agreement with reference results, highlighting its reliability. These results indicate that the proposed NL-KEDF provides a robust and efficient framework for extending OF-DFT to finite systems, with potential implications for nanomaterial design and a deeper understanding of nanoscale phenomena.

Flat electronic bands underlie a range of strongly correlated and topological phenomena, whose design in real materials has so far relied on a small catalogue of named geometric motifs such as kagome, Lieb, and pyrochlore nets. This discrete catalogue is by no means to exhaust the geometries that support flat bands in real compounds, as band flatness is a property of network connectivity. Here we combine a continuous geometric representation of crystal sublattices, with a symmetry-constrained generative model, to access a broader design space for materials hosting flat bands. The key step is to choose sublattice motifs that are outside the known geometric clusters, ensuring the novelty of the generated structures. We then introduce SkeleGen, which pins these unconventional skeletons to symmetry-compatible Wyckoff positions while denoising the surrounding chemistry, resulting in 9,352 crystal candidates that survive stability and flatnessscreening. Band flatness is confirmed using high throughput full DFT calculations, which agree well also with the tight-binding spectra of the isolated skeletons, supporting a geometric origin of the band flatness. We demonstrate "out-of-distribution" motifs as a new design principle to dramatically expand geometric repertoire for materials discovery, potentially beyond flat bands.

A common approach to studying elastic localization is to pose the problem on an infinite domain, avoiding explicit treatment of the end conditions. For a finite structure, this approach raises a natural question: how long must the structure be for the infinite-domain approximation to become accurate, and how large is the resulting error? To address this question, we develop a constructive method that provides an explicit length threshold beyond which the approximation error is small and explicitly bounded. Applied to a prototypical model arising from localized buckling of a twisted rod, the method yields the threshold and error bounds analytically. The threshold is found to vary with loading, showing that whether the infinite-domain approximation is appropriate depends on both structural length and loading state. The load-dependent length threshold also clarifies the validity of weakly nonlinear analysis: the finite-domain expansion is appropriate only over a limited range near bifurcation, beyond which the infinite-domain expansion is more appropriate. The method is then applied to the fully nonlinear problem of localized bulging in cylindrical membrane tubes. Numerical evaluation of the model-dependent estimates yields a length threshold and an upper bound on the approximation error, while direct comparisons of the finite- and infinite-domain solutions support these predictions. Together, these results characterize the applicability of the infinite-domain approximation and weakly nonlinear description to elastic localization in finite structures.

Warm-blooded vertebrates accumulate approximately conserved numbers of physiological cycles over a natural lifetime: of order $10^9$ heartbeats and $10^8$–$3\times10^8$ breaths. These regularities are not exact constants, but their persistence across orders-of-magnitude variation in body mass, metabolic power, physiological frequency, and lifespan suggests that biological time is not measured by chronological duration alone. We develop the Principle of Biological Time Equivalence (PBTE), a thermodynamic framework in which lifetime cycle count is determined by the ratio between total lifetime entropy production and the entropy cost of one physiological cycle. Starting from the open-system entropy balance $\dot S=\dot e_p-\dot h_d$, we define the entropy cost per cycle as $\sigma_0=d\Sigma/dN$, where $d\Sigma$ is the entropy produced as the physiological clock advances by $dN$ cycles. For an adult homeostatic regime, this gives the cycle-count relation $N_\star=\Sigma/\langle\sigma_0\rangle$, with $\Sigma=\int_0^L \dot e_p(t)\,dt$, where $N_\star$ is the lifetime cycle count, $\Sigma$ is total lifetime entropy production, and $\langle\sigma_0\rangle$ is the lifetime-averaged entropy cost per cycle. In the homeostatic limit, $\dot e_p\simeq P/T$, so direct measurement of metabolic power $P$, body temperature $T$, and physiological frequency $f$ gives $\sigma_0\simeq P/(Tf)$. PBTE converts the empirical lifetime-cycle invariants into entropy-cost invariants. Under Kleiber metabolic scaling and quarter-power physiological-frequency scaling, the mass-specific entropy cost satisfies $\bar\sigma_0=P/(TfM)\propto M^{3/4+1/4-1}=M^0$, providing a thermodynamic interpretation of allometric mass cancellation.

The discovery of superconductivity in Ruddlesden-Popper bilayer nickelates under both high pressure and ambient conditions has opened a new paradigm for exploring unconventional superconductivity. This review provides a brief survey of theoretical progress on bilayer nickelate superconductors. Drawing from the key experimental observations, we summarize essential physical ingredients including the hybridized Ni-3$d_{x^2-y^2}$ and 3$d_{z^2}$ electronic structure, orbital-dependent electronic correlation, Hund's coupling, and strong interlayer magnetic coupling. The fundamental theoretical models including the bilayer two-orbital Hubbard model and its minimal $t$-$J$ variants are introduced. Starting from the atomic-limit interlayer valence bond picture of the half-filled $d_{z^2}$ orbital, we elaborate on strong correlation interlayer pairing mechanisms based on different limiting considerations. Specific emphasis is placed on the hybridization mechanism, where the $d_{z^2}$ local singlet pairs provide the pairing energy and their hybridization with itinerant $d_{x^2-y^2}$ promotes superconducting phase coherence. We further analyze the pairing symmetry, the dependence of $T_c$ on various internal and external parameters, the nontrivial normal state properties including the Fermi liquid, non-Fermi liquid, weakly insulating and pseudogap behaviors. Effects of pressure tuning, oxygen content, and Kondo scattering induced by oxygen vacancies are also discussed. Finally, weak correlation theories based on spin fluctuations associated with Fermi surface nesting are briefly covered.

The spectral form factor (SFF) is a measure of energy correlations and has been widely used to identify the transition from the ergodic to the localized phase in interacting many-body quantum systems. In this work, we show that in a disordered Heisenberg spin-$\frac{1}{2}$ model, the finite temperature SFF can be used to generate a canonical phase diagram exhibiting a critical temperature $T_\mathrm{MBL}$. Using simple ideas of statistical mechanics, we obtain the critical energy density $\epsilon_\mathrm{MBL}$ dual to $T_\mathrm{MBL}$. We show that the mobility edge, numerically estimated from the spread of local perturbations and the optical conductivity, indeed coincides with $\epsilon_\mathrm{MBL}$.

We study a finite Floquet chain in which a uniform nearest-neighbor hopping coexists with a periodically rotating, SU(2)-dictated spin-assisted hopping profile. The resulting coupling is spatially inhomogeneous – weakest at the chain boundaries and strongest in the bulk – and produces a frequency-dependent Floquet-Sambe bottleneck. In the closed system, the mean inverse participation ratio (MIPR) of the Floquet eigenstates exhibits a striking nonmonotonic dependence on the driving frequency $\omega $: the states remain extended at both low and high frequencies, but become maximally localized at an intermediate frequency. We demonstrate that this localization maximum occurs at $\omega _{\mathrm{peak}}\sim \mu _{-s}=\sqrt{% 2s}$, a scale controlled by the first boundary bottleneck. To connect these spectral properties to measurable transport, we construct an open-system Floquet-Sambe Green-function inverse participation ratio from the spatial density of the injected scattering state. This open-system diagnostic recovers the same nonmonotonic localization trend as its closed-system counterpart, with the peak shifted to higher frequencies by the static bandwidth and the lead self-energy. These findings establish the driven synthetic spin chain as a directly realizable, frequency-tunable platform for coherent information storage and retrieval, rooted in the interplay of Floquet-Sambe virtual channels, boundary-controlled localization, and frequency-selective transport in emerging multi-level superconducting circuit architectures.

Helical carbon coils exhibit unique three dimensional chiral architectures that can effectively interact with microwaves in the 2 to 18 GHz range. Inspired by recent experimental studies, we develop a coarse grained electrodynamic model for helical carbon coil arrays supported on quartz substrates, and examine their potential as microwave absorbers. Guided by the heuristic relation $f_{\mathrm{res}} = c / (n_{\mathrm{eff}} p)$, we compute absorption and reflection fractions for both bare helical carbon coil on substrate and helical carbon coil with anchoring layer on substrate configurations. The anchoring layer thickness is treated as a tunable parameter to improve absorption at selected frequencies. We find that the introduction of a carbon based anchoring layer of optimized thickness enhances impedance matching absorption. The parameters are chosen as representative values for demonstration purposes, and may vary within a certain range depending on preparation conditions. Our results serve to illustrate the potential of helical carbon coils as microwave absorbing devices, and to identify possible active tuning strategies. In the discussion section, we consider extensions of the present model to account for circular dichroism absorption, and examine the influence of actual interfacial reflection on microwave absorption and reflection.

This paper is a specialization of a broad and complicated continuum theory [arXiv:2403.07582] to a relatively simple and useful case so that it is more reader friendly. A continuum theory of the flow of charged particles in an elastic solid is presented. It can describe the behavior of soft solid electrolytes and elastic semiconductors. It is nonlinear and is valid for large deformation and strong fields. The theory is derived from a three-continuum mixture model including a charged lattice continuum, a bound charge continuum for electric polarization, and an ideal fluid for the flow of mobile charges. The basic electromechanical laws are applied systematically to the model. The electric fields are quasistatic and are in SI units.

We study the transport characteristics of an extended version of the Su-Schrieffer-Heeger (SSH) model with next-nearest-neighbor (NNN) interactions and non-Hermitian onsite energies. We observed that transport in such a system is significantly modified by the NNN interaction and the non-Hermitian terms. The transmission coefficient exhibits oscillatory behavior as the strength of the NNN interaction varies in a fixed-length chain. Moreover, the transmission coefficient also shows oscillation with system size for a fixed strength of the NNN interaction. We find that novel oscillatory behavior of the transmission coefficient, arising form the NNN interaction, is a unique feature of such a model and has not been reported previously. The presence of the non-Hermitian terms also enhances/reduces the transmission coefficient depending on the values of the other system parameters like intra-, inter- and NNN hopping. It appears from our study that both the NNN interaction and the non-Hermiticity introduce significant changes in the transport properties of the extended SSH chain, which are not observed in the standard Hermitian nearest-neighbour variant of the SSH model.

Magnetic racetrack architectures that use topological magnetic particles to store information are one of the most promising concepts for future storage applications. Antiferromagnetic racetracks are particularly appealing as they are not susceptible to external magnetic fields. State-of-the-art racetracks use magnetic fields, spin-transfer and spin-orbit torques caused by electric currents to move the bits across the entire circuit. However, the application of currents in many antiferromagnetic racetracks is limited because many of them are insulating. Recently, however, a concept for ferromagnetic racetrack memories that are free of global driving forces has been proposed. It has been demonstrated that various topological entities can be generated and transported over long distances solely through local magnetization rotation at the sample boundaries, independent of global driving forces. Here, we demonstrate that the local rotation of magnetization at the boundary of an antiferromagnetic sample can be exploited in racetracks to efficiently generate and transmit antiferromagnetic skyrmions. Additionally, we demonstrate that local switching of staggered magnetization at the edge of an antiferromagnetic racetrack can be even more successful than the rotational procedure. A comparison of ferromagnetic and antiferromagnetic processing of skyrmionic bits, together with energy considerations, shows that this procedure is fairly efficient in antiferromagnets.

Many-body integrable systems can be understood as a gas of quasiparticles. They propagate ballistically and drive large-scale transport. However, with the exception of the hard rods system, no tools have been available to numerically track such quasiparticles. Focusing on the Toda fluid, whose integrability relies on the availability of a Lax pair, we present a numerical scheme to track quasiparticle trajectories as determined by the time-dependent eigenvectors of the Lax matrix. Simulating the Toda fluid in thermal equilibrium, this tracking scheme is used to numerical confirm Brownian motion of a quasiparticle. Simulated is also the motion of a tagged particle. Our numerical results for the diffusion constant matches with a novel TBA prediction. We believe our numerical scheme can be extended to other classical many-particle models possessing a Lax matrix.

Oxidation engineering provides a route to control orbital degrees of freedom, yet its role in spin-to-orbital conversion remains largely unexplored. Here, we report an efficient spin-to-orbital conversion mechanism driven by interfacial oxidation at heavy-metal interfaces. In W/Co/SiO2 heterostructures, terahertz emission exhibits a time delay that scales linearly with the W thickness, identifying orbital-current transport as the dominant origin. The emission amplitude is approximately three times larger than that of Co/Pt bilayers, indicating highly efficient conversion from spin to orbital angular momentum. Systematic variation of Co thickness, stoichiometry, and interface configuration reveals that the effect originates from oxidation of the W layer at the W/Co interface, which modulates the interfacial orbital texture. We further show that this mechanism is generic across different heavy metals and scales with their spin-orbit coupling strength. These results establish oxidation as an effective handle to engineer spin-to-orbital conversion and provide a general route toward orbitronic terahertz emitters.

Topological pumping is a paradigmatic realization of quantized transport in band systems, yet its fate in strongly correlated regimes, especially with long-range interactions, remains largely unexplored. Here we report the experimental observation of interaction-enabled topological pumping of correlated Rydberg electrons in a synthetic lattice. We show that dipolar exchange interactions induce a controllable shift of the underlying topological singularity in parameter space, such that a fixed pumping trajectory can be driven through successive topological transitions by tuning the interaction strength alone. This leads to the emergence and breakdown of quantized transport. The observations are consistent with an effective Rice-Mele description with interaction-renormalized onsite potentials and are supported by characterizing the adiabaticity and robustness to control trajectory imperfections. Our results establish a platform for exploring interaction-controlled topological transport beyond perturbative regimes and open a route toward engineering correlated topological matter in synthetic quantum systems.

Synthetic gauge fields with ultracold atoms offer a route to quantum matter in which electromagnetic environments can be designed rather than merely imposed. While the Harper-Hofstadter model has been realized in several cold-atom systems, existing implementations are largely limited to spatially uniform magnetic fluxes. Here we experimentally realize a highly programmable two-dimensional momentum-state lattice of ultracold atoms with local control over the Peierls phase pattern, enabling direct implementation of Harper-Hofstadter Hamiltonians with tunable and spatially structured synthetic gauge fields. We observe a crossover from ballistic to strongly flux-modified bulk dynamics with suppressed transport. By introducing a synthetic electric field through site-dependent energy gradients, we further demonstrate Hall-type transverse drift arising from the interplay between electric and magnetic fields. In addition, we engineer a synthetic flux domain wall separating regions with opposite magnetic fluxes and observe anisotropic propagation guided along the interface. These results move cold-atom gauge-field engineering from uniform magnetic backgrounds toward designer gauge textures, providing an experimental setting for transport across programmable topological interfaces.

Biological tissues must dynamically transition between rigid and fluid-like states during processes like morphogenesis and collective migration, often while simultaneously resisting physiological shear stresses. It remains unclear whether these tissue dynamics are governed by the same non-equilibrium critical phenomena that control conventional disordered matter. Here we show that model cell monolayers under constant stress display a rich phase diagram of nonlinear rheology. In rigid regimes, small internal fluctuations maintain a solid-like state up to a finite yield stress, above which the tissue shear-thins; conversely, fluid-like regimes exhibit robust continuous and discontinuous shear thickening, culminating in structural arrest via shear jamming. This space-filling shear-jamming transition is accompanied by structural changes including the formation of system-spanning force chains and the emergence of orientational ordering. We demonstrate that the macroscopic viscosity across these disparate regimes is described by universal scaling behavior controlled by the same underlying physical parameters. These results establish confluent tissues as a distinct class of disordered matter, demonstrating that universal jamming phenomena can emerge entirely through shape-driven topological constraints to regulate biological mechanics.

We investigate the nonequilibrium dynamics of strong-to-weak spontaneous symmetry breaking in many-body quantum systems undergoing decoherence from thermal pure states. For generic initial pure states with volume-law entanglement entropy, we show that the system undergoes a discontinuous dynamical phase transition at a critical time. This transition is accompanied by a singularity in the entropy of the system, which saturates to its maximum value at the same critical time. Through numerical simulations of the dephasing Ising and hard-core boson models, we establish the universality of this transition across different symmetries. Our results reveal that the dynamical emergence of a decohered mixed state from a highly entangled state is not a gradual asymptotic relaxation, but rather a sharp phase transition driven by a sudden collapse of global coherence.

Quantum Phase Estimation (QPE) is the quantum algorithmic workhorse for computing ground state energies of quantum Hamiltonians with quantum computers. Ground state energy calculation of physical systems is perhaps the most promising use case for quantum computing in terms of scientific and commercial value with a plausible path to outperformance of classical alternatives. This path, however, hinges on the availability of initial states for QPE with significant overlap with the true ground state. Using extensive (classical) numerical computations, we study whether the NISQ-era algorithm VQE (Variational Quantum Eigensolver) could be used to efficiently prepare high-overlap states of disordered fully-connected anisotropic Heisenberg spin glass quantum Hamiltonians with up to $15$ qubits. We find that (i) – consistent with widely held, but rarely numerically illustrated beliefs – VQE is generally unable to efficiently converge to the ground state for our Hamiltonians, which is a well-known issue with VQE due to a variety of factors including vanishing gradients and local minima; (ii) low energy states do not necessarily have large ground-state overlap, but there is typically a correlation between the two measures; (iii) adding more than three layers to the VQE ansatz neither improves overlap nor the energies found; and (iv) the best-found overlap scaling as a function of the Hamiltonian system size is not strongly exponentially decreasing, suggesting potential for VQE to be a heuristic state preparation algorithm for QPE.

We report composition tuning of the anomalous Nernst effect in amorphous-like Gd-Co-Pt ternary films. Pt incorporation into Gd-Co films modifies the anomalous Nernst coefficient and induces a sign reversal of S_ANE, whereas Gd incorporation into Co-Pt films suppresses S_ANE but reduces thermal conductivity. Owing to the balance between transverse thermoelectric response and thermal transport, the heat-flux sensitivity reaches approximately 0.24 {\mu}m/A. Composition maps reveal that the magnitude and heat-flux sensitivity of the anomalous Nernst effect can be systematically tuned in the Gd-Co-Pt ternary composition space. This work extends ANE material design from binary-alloy optimization to ternary-composition engineering.

Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agent-based system embedded within the URSA (Universal Research and Scientific Agent) framework that automates the design, execution, and validation of atomistic simulations, demonstrated using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) tool. Our system autonomously selects interatomic potentials, constructs and runs simulations, and performs iterative error recovery within a closed-loop workflow. We evaluate the scientific reliability of the agent by benchmarking its outputs against LAVA, a high-throughput toolkit for LAMMPS and the Vienna Ab initio Simulation Package (VASP) calculations. Our framework reduces manual intervention and trial-and-error, thereby improving the rigor, reproducibility, and scalability of atomistic modeling.

Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge. In this study, we propose a hybrid GNN–FEM framework for efficient and generalizable phase-field fracture modeling. While phase-field approaches provide a robust variational framework for simulating complex crack evolution, their high computational cost limits practical applications because they require solving coupled, nonlinear, and history-dependent systems within an incremental finite element procedure. To address this challenge, a graph neural network surrogate is integrated into the conventional staggered scheme, replacing the phase-field update at each load increment while retaining the FEM-based displacement solver to enforce mechanical equilibrium and boundary conditions. By preserving the incremental solution structure, the framework remains consistent with history-dependent fracture evolution without requiring the surrogate to approximate the full solution trajectory. This selective surrogate strategy emphasizes the identification of a physically meaningful and incrementally structured learning target, rather than relying on brute-force data generation to learn the full fracture process. The proposed framework achieves strong generalization across varying geometries, loading conditions, material properties, and discretizations through dimensionless feature design, a graph-based formulation on mesh-based domains, and a physics-informed loss derived from the governing phase-field equation. Numerical experiments demonstrate that the hybrid approach reduces computational cost while maintaining accuracy compared with conventional FEM, and exhibits robust predictive performance across diverse problem settings.

Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data, without stress observations, plastic strain measurements, direct yield surface data, or a prescribed parametric yield function. The framework identifies the yield function as a mechanically constrained constitutive component inside elastoplastic stress integration, rather than through direct stress-space supervision. The yield function is represented by a convex neural network that enforces convexity and positive homogeneity of degree one while imposing the assumed tension-compression symmetry, and this neural yield function is trained with a differentiable stress update and a physics-informed force equilibrium loss across multiple loading cases. The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions, assessing yield contour agreement, displacement-noise sensitivity, identifiability through plastically active stress states, epistemic uncertainty, and polynomial-surrogate deployment. This study provides a mechanics-constrained pathway for discovering anisotropic yield functions from displacement and force data while keeping the identified component within the structure of elastoplastic stress integration.

Open-ended evolution (OEE) in artificial life is typically driven by uninterpretable, black-box neural-network complexity metrics, leaving life-like systems disconnected from physical theories of complexity. We introduce MSPD (Multi-Scale Path Divergence, denoted $D_P$), a renormalization-group-inspired scalar that quantifies how the heterogeneity of a system's local transition laws is organized across temporal and spatial scales. MSPD is defined at the population level as a functional of the realised trajectory and is computed as a windowed finite-resolution estimator, with consistency between the two stated as a proposition. The metric is an explicit formula and plays a dual role: as a gradient-free fitness function and as a post-hoc analytical lens on any simulation that exposes local transition laws. On a Flow-Lenia substrate we establish three claims. (C1) Under fixed-context replay of the exact pathwise objective, MSPD-optimized parameters score higher than matched random parameters. (C2) Along optimized trajectories, states whose local transition laws are more heterogeneous yield larger future divergence than matched, less-heterogeneous states under exact-state stochastic continuations, so the metric tracks intrinsic dynamics rather than injected noise. (C5) Higher MSPD corresponds to stronger scale-dependent frustration — larger differences between the dynamics expressed at different spatial extents — linking MSPD to the frustration criterion of biological complexity in the sense of Vanchurin et al. All three transfer to Life-like cellular automata and Particle Life++, indicating that MSPD is not specific to a single substrate. A single explicit formula thus both directs open-ended evolution and provides a principled bridge to the physics of complexity that black-box drivers do not.

We study a mass-asymmetric one-dimensional electron-hole biwire system at zero temperature using the diffusion quantum Monte Carlo method. Pair correlation functions and condensate fraction are obtained over a wide range of carrier densities $r_{\rm s}$ and interwire separations $d$, allowing us to construct the phase diagram. We identify regimes corresponding to a two-component electron-hole plasma, an excitonic fluid with quasicondensation, and a Wigner-correlated phase at various densities. Owing to reduced dimensionality, strong electron-hole correlations favor excitonic quasicondensation even in the high-density limit, persisting down to $r_{\rm s} = 0.3$ a.u. These results provide a microscopic characterization of correlation-driven phases in electron-hole systems in one dimension.