Papers

Interfacial orbital transport remains far less understood than its bulk counterpart despite its central role in orbitronic experiments. Here, we theoretically investigate the transmission and conversion of orbital angular momentum across a metallic interface using a model Hamiltonian incorporating crystal-field effects. We show that an injected orbital dipole moment undergoes pronounced oscillations driven by the crystal field and generates characteristic quadrupole moments determined by the orbital orientation relative to the interface. Unlike spin precession, the dipole relaxes toward a finite value away from the interface. We further quantify interfacial orbital memory loss and demonstrate that orbital absorption produces a sizable mechanical torque obtained from the orbital continuity equation.

Nonequilibrium manipulation of quantum materials via electromagnetic dressing provides an on-demand route to tailoring electronic band structures through Floquet engineering. Time- and angle-resolved photoemission spectroscopy offers a direct means to probe these light-dressed electronic states. In such photoemission experiments, dressing can also occur for quasi-free electrons outside the material, giving rise to Volkov states. In certain cases, strong surface screening reduces the penetration of the driving field into the solid, resulting in Volkov contributions that dominate over Floquet ones. In this work, we systematically investigate the influence of materials' dielectric properties on Floquet-Volkov dressing of semiconductors, focusing on bulk layered van der Waals materials GeS, SnS, and 2H-WSe$_2$. First, by combining a simple model based on Fresnel equations with an electron-scattering description of Volkov amplitudes, we use polarization-dependent Volkov sideband intensities to extract a lower bound for the real part of the materials' dielectric functions, which typically lie between the reported dielectric constants for monolayer and bulk crystals. We demonstrate that increasing the fluence of the pump laser enables the generation of high-order Volkov sidebands which exhibit clear signatures of nonlinear light-matter interactions. Finally, we show that for our experimental geometry, the quasi-transparent nature of semiconductors in below-band-gap driving regime allows the optical pump to propagate within the sample and undergo multiple total internal reflections, producing temporally delayed Volkov replicas in pump-probe measurements via dressing of photoelectrons by evanescent fields. These systematic studies uncover previously unexplored aspects of Floquet-Volkov dressing in solids, highlighting the role of dielectric screening of the driving field.

Two-dimensional semiconductors have attracted considerable interest for integration into emerging quantum photonic networks. Strain engineering of monolayer transition-metal dichalcogenides (ML-TMDs) enables the tuning of light-matter interactions and associated optoelectronic properties, and generates new functionalities, including the formation of quantum dots (QDs). Here, we combine spatially resolved micro-photoluminescence ($\mu$-PL) spectroscopy from cryogenic (4$\text{-}$94 K) to room temperature with micro-Raman spectroscopy at room temperature to investigate the strain-dependent emission energies of thousands of individual QDs in ML-WS$_2$ and ML-WSe$_2$, integrated across multiple heterostructures and a piezoelectric device. Compared with delocalized excitons, QDs in both materials exhibit enhanced strain sensitivities of their emission energies $-$ approximately fourfold in WS$_2$ and twofold in WSe$_2$ $-$ leading to pronounced broadening of the ensemble emission linewidth. Temperature-dependent $\mu$-PL spectroscopy combined with dynamic strain tuning experiments further reveal that the enhanced strain sensitivity of individual QDs originates from strengthened interactions with low-energy phonons induced by quantum confinement. Our results demonstrate a versatile strain-engineering approach with potential for spectral matching across solid-state, atomic, and hybrid quantum photonic networks, and provide new insights into phonon-QD interactions in two-dimensional semiconductors.

Correlated disorder is known to shape light scattering in ways uncorrelated disorder cannot, from hyperuniform transparency to the structural colors of naturally occurring structures. What has been missing in photonics is a direct link between the disorder and the scattering pattern it produces. Here we show that adding correlated noise to a periodic array splits the scattering pattern into three distinct components: diffraction peaks, a diffuse background, and correlation halos. Often mistaken for broadened diffraction peaks, these halos are in fact independent features: their positions are set by the correlation range, meaning that they can appear between Bragg peaks, and - crucially - they persist far beyond the regime where the diffraction peaks vanish. Shaping the disorder itself offers further control: tuning the noise distribution suppresses selected diffraction peaks, while tuning the correlation statistics moves the halos away from the Bragg positions. This approach reproduces the scattering signatures of natural photonic structures, such as Morpho butterfly wings, and reveals multiple pathways from order to disorder, each with distinct optical properties. It also offers a practical route to inverse design - finding the disorder that produces a desired scattering pattern. This establishes scattering as a designable quantity, expanding the toolkit for metasurfaces and structural colors.

The current-induced vibrational dynamics is a key factor determining the stability of molecular nanojunctions. Beyond conventional Joule heating, a different mechanism caused by nonconservative current-induced forces has been predicted for models with multiple vibrational modes, leading to vibrational instabilities already at low bias voltages. So far, this mechanism has only been investigated in models with harmonic nuclear potentials. Consequently, a natural question is whether this effect can also be observed in more realistic models containing anharmonic nuclear potentials, and, if so, whether it has a measurable impact on observables such as the junction dissociation probability. In this work, we apply a mixed quantum-classical approach based on electronic friction and Langevin dynamics to various anharmonic two-mode systems. By performing Langevin simulations of the vibrational dynamics, we investigate the influence of anharmonicity on instabilities arising from nonconservative forces and the corresponding dissociation dynamics of the junction, as well as steady-state observables, such as the electronic current.

We review the conditions that cause or prohibit non-relativistic spin splitting of the energy bands in antiferromagnets. We propose that the existence of spin splitting in magnetically ordered systems is the default scenario and outline the criteria that must be met to avoid it. We discuss some of the properties of those special antiferromagnets that succeed in preserving their spin degeneracy.

Reconstructing magnetization textures from nitrogen-vacancy (NV) magnetometry stray-field measurements is a challenging, fundamentally ill-posed inverse problem, further complicated by the unknown effective distance between sensor and magnetic material. Here we show that incorporating a micromagnetic energy functional directly into the inversion filters out unphysical, high-energy configurations, while Fourier-space upward continuation of the stray field allows us to simultaneously fit the distance. Applied to measurements of the van der Waals ferromagnet Fe$_{3-x}$GaTe$_2$, it recovers an effective distance estimate of approximately 81nm and low-energy configurations that reproduce the observed field. More broadly, embedding physics directly into the reconstruction turns ill-posed magnetic inverse problems into transparent, interpretable reconstructions, with applicability well beyond NV magnetometry.

Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. Crystal symmetry plays a crucial role in CSP, but given the composition in a unit cell, existing methods either struggle with the NP-hard combinatorial challenge of enforcing symmetry rigorously or rely on retrieving known templates, inherently limiting both physical fidelity and the discovery of genuinely new materials. To address this challenge, we introduce NextCrystal, a symmetry-driven generative framework that employs large language models to encode chemical semantics and directly generate fine-grained Wyckoff site patterns from atomic stoichiometry, eliminating reliance on database lookups. To overcome the combinatorial complexity of site assignments, we incorporate domain knowledge via an efficient, linear-complexity heuristic beam search, rigorously enforcing algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, the framework constrains the stochastic generative trajectory to a physically plausible geometric manifold. NextCrystal achieves state-of-the-art performance on stability, uniqueness, and novelty (SUN) benchmarks, as well as superior structural matching, establishing a rigorous paradigm for exploring previously unexplored crystallographic space without relying on prior structural templates. As a representative application, first-principles screening of HfO2 candidates generated by NextCrystal identifies a previously unreported dynamically stable Pnma phase, 0.056~eV/atom lower in energy than the conventional high-pressure Pnma phase.

We consider stationary states of an effectively one-dimensional Bose-Einstein condensate in a quasiperiodic lattice. We formulate sufficient conditions for a one-to-one correspondence between the stationary states with a fixed chemical potential and the set of bi-infinite sequences over a finite alphabet. These conditions can be checked numerically. A bi-infinite sequence can be interpreted as a code of the corresponding solution. A numerical example demonstrates the coding approach using an alphabet of three symbols.

Phenomenological rules play a central role in the design of chemical reactions and materials with targeted properties. Typically, these are formulated heuristically in terms of non-interacting orbitals and bands, yet show remarkable accuracy in predicting the complex behavior of intrinsically interacting many-body systems. While their non-interacting formulation makes them easy to interpret, it potentially hinders the development of new rules for systems governed by strong correlation, such as transition metal-based materials. In this work, we present a rigorous framework that allows bridging between fully interacting, even strongly correlated, systems and an effective one-body picture in terms of quasiparticles. Further, we present a computational strategy to efficiently and accurately access the main components of such a description: the embedding approximation of the ghost Gutzwiller Ansatz. We illustrate the capabilities of this quasiparticle formulation on the Woodward-Hoffmann rules, and apply their reformulated version to toy “reactions” which exemplify the main scenarios covered by them.

The stochastic dynamics of flagellar beating for micro-swimmers, such as flagellated cells, sperms and microalgae, is widely thought to include a feedback mechanism between flagellar shape and the rate of activation/de-activation of the $N \gg 1$ driving molecular motors. In the context of the so-called rigid filament models, where the axoneme is described by a single degree of freedom $X(t)$, we investigate the effect of direct coupling between the activity dynamics of adjacent motors, parametrized by $K \ge 0$. A functional Fokker-Planck equation for $X$ and the state of the $N$ motors is obtained. In the limit of small coupling $K \ll 1$, we derive a system of equations governing the dynamics of the Fourier modes of the active motor density, obtaining estimates for several observables and the fluctuations' quality factor $Q$. For larger $K$ we resort to numerical simulations. The effect of introducing the coupling $K>0$ is to increase characteristic times and the beating period. Moreover for large $K$s the limit cycle becomes bi-stable, with abrupt avalanches of the motor dynamics. Increasing $K$ is similar to what observed in the case $K=0$ when the confining elastic force is strongly reduced. The quality factor of fluctuations has a non-monotonic behavior: it first increases with $K$, then decreases. This is accompanied by the reduction and eventual disappearance of regions where the fraction of activated motor is nor $0$ neither $1$.

Many computational methods in ab initio quantum chemistry are formulated in terms of high-order tensor contractions, whose cost determines the size of system that can be studied. We introduce stochastic tensor contraction to perform such operations with greatly reduced cost, and present its application to the gold-standard quantum chemistry method, coupled cluster theory with up to perturbative triples. For total energy errors more stringent than chemical accuracy, we reduce the computational scaling to that of mean-field theory, while starting to approach the mean-field absolute cost, thereby challenging the existing cost-to-accuracy landscape. Benchmarks against state-of-the-art local correlation approximations further show that we achieve an order-of-magnitude improvement in both total computation time and error, with significantly reduced sensitivity to system dimensionality and electron delocalization. We conclude that stochastic tensor contraction is a powerful computational primitive to accelerate a wide range of quantum chemistry.

We establish that ultralow lattice thermal conductivity in halide perovskites and related octahedral framework materials arises from two distinct and complementary mechanisms: (i) halogen-halogen-enabled rotational soft modes that reshape the low-frequency spectrum and intensify phonon scattering, and (ii) static octahedral distortions that further enhance anharmonicity and reduce phonon lifetimes. Using first-principles calculations on CsPbBr3, we demonstrate that Br-Br interactions induce rotational soft modes that decongest the phonon spectrum and enhance three- and four-phonon scattering, strongly suppressing particle-like thermal conductivity (kappa_p). Independently, static octahedral distortions further reduce kappa_p by amplifying anharmonicity while leaving wave-like conductivity (kappa_c) intact. Based on these mechanistic insights, we introduce a geometric distortion factor rho and perform a high-throughput screening that first selects materials with halogen-coordinated octahedral building blocks-ensuring the presence of rotational soft modes-and then identifies those with pronounced distortion. This strategy uncovers TaGaI8 with an ultralow kappa_L = 0.11 W/mK at room temperature. This work establishes halogen-halogen-enabled rotational soft modes and octahedral distortions as transferable design principles for octahedra-containing halides, spanning both extended frameworks and molecular-cluster motifs, for discovering ultralow-kappa_L materials.

Controlling flow-induced segregation in a granular mixture is highly relevant to many industrial settings. To enhance mixing or promote segregation, the continuous gravity flow of a bidisperse granular mixture through a series of narrow vertical channels with exit slots is investigated. The bidisperse mixture is composed of two different sizes of particles, but of the same density. In dense flow, segregation occurs, leading to formation of bands. The bands of large particles appear at a distance away from the walls. This finding is in contrast to that in shear-driven segregation in a dense flow where large particles segregate towards the walls. Using a phenomenological model, it has been shown that rolling and bouncing induced segregation is the dominant mechanism. When cylindrical inserts are placed to modify flow patterns, that significantly influences segregation patterns. The symmetrical placement of a cylindrical insert close to the exit slot vanishes the bands and enhances mixing. However, with two inserts placed symmetrically and close to the exit slot, the degree of segregation in the reverse direction is greatly enhanced compared to that without insert. In the former, small particles accumulate in thin regions adjacent to the walls, and large particles comprise the bulk of the domain and the flowing stream. The heap formation above the insert in a narrow channel, when the insert is close to the exit, enhances mixing in one configuration, whereas it amplifies reverse segregation in the other.

The maturity of the chemical vapor deposition graphene-based device processing has increased from chip level demonstrations to wafer-scale fabrication in the past few years. Due to this wafer-scale, electrical characterization and analysis of the fabricated devices has become increasingly important to enable extraction of multiple parameters with minimal number of measurements for the quality control purposes critical for industrial uptake of 2D materials-based devices. As a crucial step, we demonstrate optimization of complementary metal-oxide semiconductor (CMOS) back-end-of-line (BEOL) compatible graphene field-effect transistor (GFET) fabrication and analysis including the gate stack, bottom contact, graphene patterning and encapsulation process steps. The analysis methods include atomic force microscopy, scanning electron microscopy and most importantly electrical characterization. The electrical characterization focuses on comparing different test structures and extraction methods for mobility, contact resistance, IV-curve hysteresis and doping parameters. The comparison shows that the selected measurement test structures and analysis methods can have a large impact on the extracted values and should thus be considered when comparing data sets between different sources. The analysis shows that the optimized process offers high device yield of 98 % with good doping uniformity, contact resistance and mobility as well as low IV-curve hysteresis values on 200 mm wafers.

The electronic band structure of Na$_{2}$KSb was studied by a combination of low-energy photoemission spectroscopy and density functional theory (DFT) calculations. The optical and photoemission quantum efficiency (QE) spectra, along with longitudinal energy distribution curves (EDCs) of multialkali Na$_{2}$KSb(Cs,Sb) photocathodes were measured in the temperature range of 80–295 K. The thresholds of various band-to-band transition in Na$_{2}$KSb were observed in the optical and QE spectra of Na$_{2}$KSb(Cs,Sb) photocathodes. The evolution of EDC derivatives with varying photon energy reveals a fine structure related to the emission of two types of electrons: (i) ballistic electrons, which are excited from heavy hole, light hole and split-off valence bands, and (ii) photoelectrons, that are captured in the side valleys of Na$_{2}$KSb conduction band. The analysis of EDCs and QE spectra allowed us to determine the band structure parameters of Na$_{2}$KSb at $T = 80$ K, including the band gap $E_{\text{g}} = 1.52 \pm 0.02$ eV, spin-orbit splitting $\Delta_{\text{SO}} = 0.59 \pm 0.04$ eV and the energy separations between $\Gamma$ and side valleys of the conduction band: $\Delta_{\Gamma-\text{X}1} = 0.41 \pm 0.05$ eV and $\Delta_{\Gamma-\text{X}2} = 0.65 \pm 0.05$ eV. The experimentally determined band gaps and side valley positions, as well as the energies of the final electronic states of optical transitions are in good agreement with the DFT calculations. The obtained data on the hot electron dynamics and electronic band structure of Na$_{2}$KSb are crucial to improve the understanding of the photoemission processes in this material and will contribute to the development of the robust spin-polarized electron sources with multialkali photocathodes.

Harnessing dipolar spin environments as controllable quantum resources is a central challenge in solid-state quantum technologies. Here, we report the observation of a coherent mesoscopic spin state in a disordered ensemble of substitutional nitrogen (P1) centers in diamond. An iterative Hartmann-Hahn protocol transfers polarization from dense nitrogen-vacancy (NV) centers to a P1 ensemble, yielding a 740-fold enhancement over room-temperature thermal equilibrium as revealed by differential readout. The resulting mesoscopic P1 spin ensemble exhibits collective Rabi oscillations and long-lived spin-lock and Hahn-echo coherences. We identify a crossover in the saturation polarization arising from the competition between coherent driving and local disorder, providing a quantitative measure of the system's intrinsic disorder. These results establish a foundation for utilizing dark electron spin ensembles as robust resources for quantum sensing and quantum many-body simulation.

Several factors may be responsible for disorder and frustration in a magnetic nanoparticle, including atomic vacancies on the surface and inside, impurity atoms, long-range magnetic exchange coupling, etc. We use Monte-Carlo simulations within the Heisenberg model to examine the role of randomly distributed atomic vacancies and long-range magnetic-exchange coupling on the temperature-dependent magnetic properties of ferromagnetic nanoparticles. In particular, we study the role of the second-neighbor antiferromagnetic exchange coupling and missing atoms inside the particle resulting in broken nearby bonds. We find that both factors may enhance the superparamagnetic behaviors of such particles.

We investigate the nature of quantum phase transitions in a (1+1)-dimensional field theory composed of $N$ copies of the Ising conformal field theory interacting via competing relevant perturbations. The field theory governs the competition between a mass term and an interaction involving the product of $N$ order-parameter fields, which is realized, e.g. in coupled Ising chains, two-leg spin ladders, and SO($N$)-symmetric spin chains. By combining a perturbative renormalization group analysis and large-scale matrix-product state simulations, we systematically determine the nature of the phase transition as a function of $N$. For $N=2$ and $N=3$, we confirm that the transition is continuous, belonging to the Ising and four-state Potts universality classes, respectively. In contrast, for $N \ge 4$, our results provide compelling evidence that the transition becomes first order. We further apply these findings to specific lattice models with SO($N$) symmetry, including spin-$1/2$ and spin-$1$ two-leg ladders, that realize a direct transition between an SO($N$) symmetry-protected topological phase and a trivial phase. Our results refine a recent conjecture regarding the criticality of transitions between SPT phases.

Shifting the point dipole from the center of a Stockmayer particle is a simple geometric modification that has been explored previously, yet its implications for liquid structure, dielectric response, and phase behavior remain incompletely understood. Here, we combine molecular dynamics simulations with analytical theory to provide a unified physical interpretation of how dipole displacement reshapes microscopic correlations and propagates to macroscopic thermodynamic properties. We show that dipole shifting breaks the fore-aft symmetry of the local electrostatic field, producing only modest changes in radial packing but strong alterations in angular structure within the first solvation shell. Enhanced alignment near the dipole head is accompanied by frustrated orientational correlations near the tail, leading to broader angular distributions and a shift away from axial configurations at strong coupling. These structural asymmetries weaken cooperative ordering and result in a systematic reduction of the dielectric constant, despite locally stronger interactions. For large shifts, the dielectric response approaches the Debye limit, indicating effective suppression of dipole-dipole correlations. The same geometric frustration governs vapor-liquid equilibria: while increasing dipole strength raises the critical temperature, even modest shifts disrupt the highly polarized liquid states that emerge at strong coupling and can suppress ferroelectric-like ordering. Predictions from a reparameterized COFFEE theory capture these trends within its domain of validity, highlighting the direct connection between local orientational structure and macroscopic observables. Overall, this work demonstrates that dipole location, not only magnitude, provides a powerful control parameter in dipolar fluids and offers a clear framework for understanding geometric frustration in electrostatic liquids.

Photoluminescence intensity is widely used to infer exciton populations, yet the detected signal inherently convolves occupancy with radiative-rate modification and collection efficiency, making quantitative inversion vulnerable to pump and system drifts. Here we realize a dual-channel self-referenced scheme enabled by two nearly degenerate quasinormal modes in a hybrid microcavity. Their shared optical path provides common-mode observables (i.e., overall spectral and intensity drift) that track global thermo-optic and pump fluctuations, while their differential-mode observables (i.e., spectral splitting and mode-contrasted emission) remain highly sensitive to local gap dielectric perturbations and dipole-dependent radiative weights. Using temperature as a control parameter in monolayer WSe$ _2 $, we exploit this common/differential-mode framework to robustly invert the relative populations of excitons with out-of-plane ($ \perp $) and in-plane ($ \parallel $) dipole transitions without external absolute calibration. At the temperature of $\sim$50 K, we obtain $ N_\perp/N_\parallel \approx 200 $, coincident with the expected accumulation in the out-of-plane-emitting dark manifold. This internally referenced approach provides a practical route to drift-tolerant, dipole-resolved population metrology in nanogap photonic systems.

X-ray absorption near-edge structure (XANES) provides element-specific insight into local electronic and structural environments, but quantitative interpretation of molecular XANES under periodic boundary conditions (PBC) remains challenging due to finite-size effects and core-hole treatments. In this work, we systematically investigate how core-hole approximations and charge compensation schemes affect transition energies, energy alignment, and chemical-shift reproducibility in PBC-DFT-based molecular XANES calculations. Using ethane as a model system, we show that the full core-hole (FCH) approach exhibits pronounced supercell-size dependence originating from interactions between background charge and charged molecules, with transition energies largely changed by leading-order finite-size terms. In contrast, the excited core-hole (XCH) method rapidly converges owing to its neutral final state. We further demonstrate that most finite-size effects in FCH can be removed by Makov-Payne corrections based on multipole expansion of the electrostatic energy of charged supercells under PBC. Furthermore, we propose a simple Fermi-level-based energy correction (EF/2) that provides comparable improvement using only a single supercell. Extending the analysis to an n-alkane series reveals that while intrinsic electronic-structure changes govern peak shifts for small molecules, systematic energy drifts persist in FCH for larger molecules, whereas XCH and FCH+EF/2 remain stable. Finally, for small molecules at the C and N K-edges, XCH and FCH+EF/2 accurately reproduce experimental chemical shifts, whereas uncorrected FCH fails. These results provide practical guidelines for reliable energy alignment and chemical-shift analysis in molecular XANES under PBC, supporting robust applications to molecular, adsorption, and interfacial systems.

Superconducting waveguides are a promising platform for ultralow-loss transmission in the millimeter-wave to terahertz band under cryogenic conditions, with potential applications in astronomical instrumentation and emerging quantum technologies. We develop a framework, based on microscopic superconductivity theory, to evaluate the power-flow attenuation constant $\alpha$ of superconducting rectangular waveguides in the $100~\mathrm{GHz}$–THz range, applicable to arbitrary electronic mean free paths $\ell$ from the dirty limit $\ell\ll\xi_0$ to the clean limit $\ell\gg\xi_0$. We also derive an analytical expression for two-level-system (TLS)-induced attenuation $\alpha_{\rm TLS}$ in thin native oxide layers within the standard TLS model. Using this framework, we perform numerical evaluations of $\alpha$ for representative materials over standard waveguide sizes from WR15 to WR1. In the high-frequency regime $f \gtrsim 0.5 \Delta/h$, low attenuation favors the clean regime $\ell\gtrsim\xi_0$, indicating that high-purity materials can achieve very low attenuation below their gap frequency. For the TLS contribution, using parameter values representative of native Nb oxides, we find that $\alpha_{\rm TLS}$ can become relevant at sufficiently low temperatures $T/T_c\lesssim 0.1$-0.2, where quasiparticle dissipation is exponentially suppressed. Finally, we extend the discussion to the strong-excitation regime using a recently developed nonlinear-response theory within the Keldysh–Usadel framework of nonequilibrium superconductivity and show that nonlinear dissipation produces a Higgs-mode peak in $\alpha$ near $f\simeq \Delta/h$ via a Kerr-type nonlinearity of the dissipative conductivity. This peak provides a distinct hallmark of the Higgs mode that has been largely overlooked so far.

Thin-walled structures capable of large, reversible deformation are key to multistable structures, origami, kirigami, and soft robotics. However, conventional fabrication techniques, including 3D printing, casting, and laser cutting, suffer from poor surface quality, low durability, complex processing steps, and restricted geometric freedom, hindering the repeatable production of thin-walled, continuous structures. Here, an additive manufacturing-facilitated blow molding (AM-BM) approach is introduced, combining the design flexibility of additive manufacturing with the robustness of blow molding. By replacing metal molds with 3D-printed resin ones, AM-BM enables rapid, low-cost fabrication of thin-walled polymeric components with tunable geometry and controllable wall thickness across diverse thermoplastic materials. The thickness control allows thin-walled components to function either as rigid load-bearing elements or as compliant hinges that permit reversible deformation. The versatility of AM-BM is demonstrated through representative examples: multistable structures with geometry-controlled buckling and rich reconfigurability; origami and kirigami structures with extensive design freedom, scalable complexity, and uniform mechanical properties; and soft actuators and robots with ultrahigh load-to-weight ratios, rapid response, and scalable design. Altogether, AM-BM provides an efficient and versatile method for creating thin-walled structures that combine geometric freedom, mechanical functionality, and scalable production.

Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemistry. I test a concrete alternative hypothesis: that property prediction can be driven by bibliographic confounding. Across five tasks spanning MOFs (thermal and solvent stability), perovskite solar cells (efficiency), batteries (capacity), and TADF emitters (emission wavelength), models trained on standard chemical descriptors predict author, journal, and publication year well above chance. When these predicted metadata ("bibliographic fingerprints") are used as the sole input to a second model, performance is sometimes competitive with conventional descriptor-based predictors. These results show that many datasets do not rule out non-chemical explanations of success. Progress requires routine falsification tests (e.g., group/time splits and metadata ablations), datasets designed to resist spurious correlations, and explicit separation of two goals: predictive utility versus evidence of chemical understanding.

Pinning magnetization in a ferromagnetic thin film is commonly realized through exchange biasing with an adjacent antiferromagnet. Field-cooling from above the Néel temperature is a reliable yet slow re-pinning method in exchange-biased systems. For on-demand reprogrammable devices, localized and rapid exchange bias repinning methods are essential. Recent work has shown that femtosecond laser pulses enable field-free reversal of exchange bias in tailored multilayer stacks. Contrary to field-cooling, our experiments with ultrafast excitation reach hitherto unexplored regimes in the exchange bias setting process. Here, we unravel these observations by considering both ultrafast magnetization dynamics on the femto- to picosecond timescale and slow heat-driven dynamics on millisecond timescales and upwards. We develop a microscopic framework of exchange bias setting in a polycrystalline antiferromagnetic thin film like IrMn that provides a complete description of the observations in our present experiments and those found in literature. We expand the use of our model by identifying material platforms and stack designs that lead to optimized performance, aiding further development of optically reprogrammable devices.

We demonstrate that graph-based models are fully capable of representing higher-order interactions, and have a long history of being used for precisely this purpose. This stands in contrast to a common claim in the recent literature on "higher-order networks" that graph-based representations are fundamentally limited to "pairwise" interactions, requiring hypergraph formulations to capture richer dependencies. We clarify this issue by emphasizing two frequently overlooked facts. First, graph-based models are not restricted to pairwise interactions, as they easily accommodate interactions that depend simultaneously on multiple adjacent nodes. Second, hypergraph formulations are strict special cases of more general graph-based representations, as they impose additional constraints on the allowable interactions between adjacent elements rather than expanding the space of possibilities. We show that key phenomenology commonly attributed to hypergraphs – such as abrupt transitions – can, in general, be recovered exactly using graph models, even locally tree-like ones, and thus do not constitute a class of phenomena that is inherently contingent on hypergraphs models. Finally, we argue that the broad relevance of hypergraphs for applications that is sometimes claimed in the literature is not supported by evidence. Instead it is likely grounded in misconceptions that network models cannot accommodate multibody interactions or that certain phenomena can only be captured with hypergraphs. We argue that clearly distinguishing between multivariate interactions, parametrized by graphs, and the functions that define them enables a more unified and flexible foundation for modeling interacting systems.

We study once-excited random walks on general trees, modeled by placing a single "cookie" at each vertex. Each cookie acts as a metaphorical reward that is consumed upon the first visit to the vertex where the cookie is placed. On that initial visit, the walk is in an excited state and behaves like a biased random walk. Once the cookie is consumed, the process reverts to a symmetric random walk on all subsequent visits. We consider a random environment in which the bias parameters are independent random variables. We prove that the process exhibits a sharp phase transition between transience and recurrence on general trees with polynomial growth, where the critical threshold is determined by the branching-ruin number of the tree.

This note provides a simple derivation of the overdamped approximation for kinetic (or underdamped) equilibrium Langevin dynamics, in cases where certain coefficients depend on the position variable. The equivalent small-mass limit of these dynamics, known as the Kramers–Smoluchowski approximation, in the case of a state-dependent friction coefficient, has been previously studied by a variety of approaches. Our new approach uses hypocoercivity estimates, which may be of interest in their own right, and lead to a very direct derivation, providing in particular a clear explanation of the “noise-induced drift” term in the overdamped equation in the case of a state-dependent friction term. Using the same approach, we also treat effective kinetic dynamical models derived from a coarse-graining approximation of a high-dimensional system, as well as a class of kinetic dynamics with position-dependent mass matrices. All of these models are relevant to applications in computational chemistry. We finally identify a mistake in a related work, and suggest a solution.

Chirality in polymeric systems enables a wide range of emergent optical, mechanical, and transport phenomena, yet a unified framework that quantitatively connects molecular-scale geometry to chiral behavior remains lacking. Existing theoretical descriptions typically emphasize either continuum models, such as the helical wormlike chain (HWLC), which neglect intermolecular interactions, or mesophase-level theories, which obscure the role of molecular geometry. In this work, we introduce a comprehensive framework for quantifying chirality in helical polymers by extending the Kremer-Grest bead-spring model to explicitly map intrinsic curvature and torsion onto bond angle and dihedral potentials. We establish direct theoretical relationships between helical parameters such as pitch and radius, and connect them to a normalized, dimensionless chirality characteristic, $\chi$ that captures local geometric correlations absent from conventional HWLC descriptions. Furthermore, using molecular dynamics simulations, we systematically quantify the influence of excluded volume interactions and thermal fluctuations on helical geometry and chirality, dispelling the common assumption that monotonic increases in chirality are associated only with decreasing pitch. Finally, we present a coarse-graining procedure that facilitates a direct comparison between experimental helical polymers and the Kremer-Grest helical chain, demonstrating quantitative agreement across a diverse set of polymer classes. This unified geometric and particle-based description provides a predictive roadmap for selecting and engineering chiral Kremer-Grest models and offers a general platform for designing polymeric materials with controlled and tunable chirality.