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Physics Condensed Matter

cond-mat Watch Condensed Matter

Friday 8 May

Quantum Gases

Rabi-coupling-induced three-component quantum droplet in ultracold Bose gases

Xiao Ding, Dajun Wang and Xiaoling Cui

We uncover a new mechanism for realizing three-component quantum droplets in ultracold Bose gases, where only one inter-species interaction is attractive. In this scheme, the inter-species attraction leads to a self-bound binary droplet, and the third component joins through Rabi coupling with one component of the binary droplet. We find that a stronger Rabi coupling leads to a larger fraction of the third component, but also destabilizes the entire droplet due to the involvement of more repulsive forces. Such instability can be remedied by a finite detuning between the Rabi-coupled components. We demonstrate these results in realistic Na-Rb mixtures, using both thermodynamic analyses and numerical simulations based on extended Gross-Pitaevskii equations. Our work outlines a general route for stabilizing multi-component droplets by bridging an existing binary droplet with additional components via suitable single-particle fields.
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Materials Science

Water adsorption on a model silicate surface: wollastonite (100)

Luca Lezuo, Andrea Conti, Alexander Hoheneder et al. (+7 more)

Water adsorption on silicate surfaces is a critical yet poorly understood process relevant to, e.g., mineral weathering and cement hydration. This study investigates the structure of water overlayers on a model calcium silicate, the lowest-energy (100) surface of wollastonite (CaSiO3). It combines atomically resolved non-contact atomic force microscopy (nc-AFM), acquired with qPlus sensors and functionalized tips in ultrahigh vacuum (UHV), with density functional theory (DFT) calculations employing the metaGGA r2SCAN+rVV10 functional. Adding incremental doses of water to the sample at cryogenic temperatures produces distinct structures governed by the competition between water-surface and water-water interactions. With two water molecules per surface unit cell, water-surface interactions dominate: In line with previous theoretical predictions, adsorbates follow the surface lattice. As the coverage increases, intermolecular hydrogen bonding competes with bonding to the surface, leading to the emergence of complex, coexisting patterns. While their small energy differences prevent an unambiguous identification of the most stable structure by DFT, the experimentally observed symmetries help constrain plausible structural models. Above a critical density of four water molecules per unit cell, water-water interactions prevail, and water clusters are formed. The results provide an atomic-scale framework for understanding water interactions with calcium silicate surfaces.
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All authors Luca Lezuo, Andrea Conti, Alexander Hoheneder, Elena Vaníčková, Domitilla Alessandra Aloi, Rainer Abart, Florian Mittendorfer, Michael Schmid, Ulrike Diebold, Giada Franceschi

Superconductivity

Breaking the Trade-off: Bulk 2D Ising Superconductivity with High Tc and Giant Interlayer Spacing via a Unique Chain Intercalation in (BaS)1/3TaS2

Ziyi Zhu, Leiming Chen, Xiangqi Liu et al. (+11 more)

Two-dimensional (2D) transition metal dichalcogenides (TMDs) are promising platforms for low dimensional superconductivity. However, in conventional intercalated systems, achieving a high superconducting transition temperature (Tc) often comes at the expense of reduced interlayer spacing and weakened 2D character. Here, we overcome this long-standing compromise through a unique chain-like intercalation strategy. We report the synthesis and properties of a new polymorph, (BaS)1/3TaS2, in which a distinctive Ba-S-S-Ba chain structure is inserted between TaS2 bilayers. This unique configuration breaks the bulk c axis mirror symmetry while achieving exceptional interlayer decoupling, with an inter-bilayer spacing of 12.75 Å-more than three times that of pristine 2H-TaS2. By suppressing interlayer electronic coupling, this structural evolution allows local inversion symmetry breaking within individual TaS2 layers to dominate. This prevents compensation of the Ising spin-orbit fields typical of centrosymmetric bulk phases, enabling robust 2D Ising superconductivity. Remarkably, the compound exhibits an enhanced Tc without sacrificing its large interlayer spacing, thereby breaking the conventional trade-off between large spacing/high anisotropy and high Tc. Comprehensive transport, magnetic, and thermodynamic measurements confirm its robust superconducting state. Our work establishes a versatile intercalation framework for designing bulk-like 2D Ising superconductors, providing a new route to reconcile competing material demands and expanding the scope of Ising superconductivity research.
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All authors Ziyi Zhu, Leiming Chen, Xiangqi Liu, Haonan Wang, Chen Xu, Ze Yan, Zhengyang Li, Wei Xia, Jiawei Luo, Na Yu, Xia Wang, Ke Qu, Zhenzhong Yang, Yanfeng Guo

Strongly Correlated Electrons

Frustration of harmonic and solitonic helimagnetism on the body-centered tetragonal lattice of GdAlSi

Ryota Nakano, Rinsuke Yamada, Oleg I. Utesov et al. (+9 more)

The triangular lattice antiferromagnet (TLAF) with nearest-neighbor exchange interaction is a model platform in the field of frustrated magnetism. Here, anharmonic (`up-up-down') and harmonic (`120 degree') magnetic states compete, because the fundamental helimagnetic wave and its higher harmonic are degenerate in energy. We show that a body-centered tetragonal lattice (BCTL) can realize a similar frustration of harmonic and anharmonic helimagnetic states, and that the tetragonal magnetic Weyl semimetal GdAlSi realizes this scenario. In an applied magnetic field, resonant elastic X-ray scattering reveals a competition of harmonic cycloidal and solitonic double-Q states, well consistent with mean-field calculations. Our work provides a new paradigm for frustration physics in BCTL materials.
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All authors Ryota Nakano, Rinsuke Yamada, Oleg I. Utesov, Masaki Gen, Akiko Kikkawa, Hajime Sagayama, Hironori Nakao, Masashi Tokunaga, Taka-hisa Arima, Yoshinori Tokura, Se Kwon Kim, Max Hirschberger

Materials Science

Physics-informed operator learning for transferable energy-dissipative microstructure dynamics

Jie Xiong, Yue Wu, Xuewei Zhou et al. (+2 more)

Phase-field simulations provide mechanistic descriptions of microstructure evolution, but repeated high-fidelity integration over long horizons and broad parameter spaces remains computationally expensive. We present PFNet, a physics-informed neural operator framework that advances microstructural states by learning conditional evolution operators rather than direct correlations. PFNet combines a diffusion-inspired U-Net with periodic padding, entropy-based state conditioning and thermodynamic-parameter modulation to encode boundary consistency, instantaneous ordering state and changes in the free-energy landscape. For Cahn-Hilliard coarsening, PFNet achieves accurate one-step prediction and stable autoregressive rollouts across composition, gradient-energy coefficient, coarsening stage and morphology class, with errors concentrated near diffuse interfaces and topology-changing regions. The same framework extends to a four-channel martensitic-transformation benchmark without martensite-specific redesign. These results indicate that physics-informed operator learning can provide transferable surrogates for phase-field dynamics and broader energy-dissipative dynamical systems.
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All authors Jie Xiong, Yue Wu, Xuewei Zhou, Peishuo Zhao, Jiaming Zhu

Mesoscale and Nanoscale Physics

Topological Characterization of Discrete-Time Classical Stochastic Processes: Dual Role of Point-Gap Topology

Masaya Nakagawa and Masahito Ueda

We present topological characterization of classical stochastic processes described by discrete-time Markov chains on lattices. We point out that point-gap topology of stochastic matrices entails two distinct physical consequences that hinge on the choice of the reference point. The point-gap topology around a generic reference point is related to the direction of transport, and nontrivial topology around the origin of the complex spectrum of a stochastic matrix implies non-Markovianity caused by, e.g., feedback control. On the basis of this characterization, we identify the topological origin of directed transport in a classic experiment of Maxwell's demon [S. Toyabe et al., Nat. Phys. 6, 988 (2010)] and find the topological nature of feedback control beyond thermodynamic interpretation. We demonstrate that a topologically enforced non-Markovian classical stochastic process can be simulated by a Markovian quantum master equation, indicating a topological form of quantum advantage.
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Materials Science

Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction

Kammampati Sai Kumar, Albert Linda, Shubham Kumar Maurya et al. (+1 more)

The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high dimensionality. Here, we present a physics-informed deep learning framework that directly predicts mechanical and thermodynamic properties from the three-dimensional electronic charge density derived from density functional theory (DFT). The proposed approach first utilizes a three-dimensional convolutional autoencoder for unsupervised dimensionality reduction, compressing a high-resolution charge-density grid (128 x 128 x 128) into a compact latent representation (16 x 16 x 16 x 16) while preserving physically meaningful features, as confirmed by negligible reconstruction errors across diverse crystal systems. The compressed latent-space representation of charge density is then used by two different regression models for property prediction: Light Gradient Boosting Machine (LightGBM) and Attention-based 3D Convolutional Neural Networks (Att CNN), and their performance is compared. Combining composition-based descriptors (Material Agnostic Platform for Informatics and Exploration or MAGPIE) with electronic charge density data further improves the model accuracy. Using a dataset of about 6059 inorganic compounds spanning multiple crystal symmetries, the models achieve strong predictive performance for bulk modulus K (R2 = 0.94), Young's modulus E (R2 = 0.88), shear modulus G (R2 = 0.87), formation energy Eform (R2 = 0.96), and Debye temperature {\Theta} (R2 = 0.89). This work establishes electronic charge density as a transferable, physics-grounded descriptor for materials property prediction, requiring ~ 1/25 the computational resources of full-fledged DFT calculations.
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All authors Kammampati Sai Kumar, Albert Linda, Shubham Kumar Maurya, Somnath Bhowmick

Disordered Systems and Neural Networks

A Hardware-aware Hopfield Network with a Nonlinear Memristor Array for Robust Associative Memory with Superlinear Capacity

Younghyun Lee, Hakseung Rhee, Unhyeon Kang et al. (+9 more)

Associative memory retrieves complete patterns from partial or corrupted inputs and constitutes a primitive form of generative inference. Classical Hopfield networks (CHN) provide a canonical framework for associative memory but suffer from limited memory capacity. Recently, modern Hopfield networks (MHN) were introduced to achieve higher capacity by using explicit pattern-wise storage and neurons with the softmax activation function, which makes the MHN vulnerable to noise and the hardware implementation complicated due to its network size varying with the number of stored patterns. Here, we introduce a hardware-aware Hopfield network (HHN), in which the intrinsic nonlinear current-voltage characteristics of a charge-trap memristor are leveraged to engineer the energy landscape of the HN, increasing the memory capacity. Using a 25 x 25 nonlinear memristor array, we demonstrate reliable reconstruction of corrupted patterns with memory capacity far exceeding the classical limit (K ~ 0.14N, where N is the number of neurons). The HHN preserves Hopfield-type energy-minimization dynamics and remains robust to synaptic conductance noise. Large-scale simulations on high-dimensional image data reveal an empirical memory capacity scaling of K ~ 0.3 x N^1.2 under a fixed synaptic budget. These results establish HHN as a scalable hardware-native architecture for low-power associative memory and generative inference.
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All authors Younghyun Lee, Hakseung Rhee, Unhyeon Kang, Seungmin Oh, Kyungmin Lee, Hyun Jae Jang, Seongsik Park, YeonJoo Jeong, Inho Kim, Jong Keuk Park, Kyung Min Kim, Suyoun Lee

Mesoscale and Nanoscale Physics

Hybrid-order topology in two-dimensional nonsymmorphic antiferromagnets

Wei Xiong, Zi-Ming Wang, Xin-Mei Wei et al. (+2 more)

We theoretically demonstrate hybrid-order topology in a two-dimensional nonsymmorphic antiferromagnet. Utilizing a generic antiferromagnetic Dirac model with a symmetry-allowed, momentum-dependent spin-density-wave (SDW) mass, we show that a single bulk insulating phase exhibits distinct topological boundary manifestations governed solely by the termination geometry. For screw-compatible edges, nonsymmorphic screw symmetry protects gapless first-order edge states. In contrast, for a $45^\circ$ diamond-shaped termination, the screw symmetry is broken at the boundary, resulting in gapped edges. However, the finite geometry still preserves magnetic mirror symmetries $\mathcal{M}_x\mathcal{T}$ and $\mathcal{M}_y\mathcal{T}$, which enforce an alternating pattern of edge masses, thereby binding zero-dimensional corner states. This second-order phase is characterized by a quantized quadrupole moment, with corner states pinned to zero energy by the chiral symmetry. We further demonstrate that explicit lattice perturbations can selectively gap the first-order edge modes while robustly preserving the corner states. Our work establishes a symmetry-based route to a termination-controlled duality between first- and second-order topology in magnetic nonsymmorphic systems.
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All authors Wei Xiong, Zi-Ming Wang, Xin-Mei Wei, Rui Wang, Dong-Hui Xu

Mesoscale and Nanoscale Physics

Floquet second-order topological insulator in strained graphene

Yu-Wen Xu, Xiaolin Wan, Zi-Ming Wang et al. (+2 more)

Graphene provides a canonical setting for Floquet band engineering, where circularly polarized light can dynamically open topological gaps at Dirac points and generate nonequilibrium Hall responses. Here we show that uniaxial strain and off-resonant circularly polarized light with tunable incidence angle enable a controllable route to Floquet higher-order topology in graphene. Using a strained honeycomb tight-binding model with Peierls coupling and a high-frequency expansion for the effective Floquet Hamiltonian, we find that strain drives the Dirac cones toward the Dirac-merging (semi-Dirac) critical regime, where the light-induced mass becomes strongly anisotropic. For oblique incidence, the projected drive is effectively elliptically polarized and, in combination with strain, stabilizes a phase with gapped edges but robust in-gap corner modes in finite geometries, realizing a Floquet second-order topological insulator. We characterize the phase diagram via the Chern number and a crystalline-symmetry-quantized polarization invariant. Finally, first-principles-informed tight-binding calculations corroborate the predicted topological evolution in strained graphene nanostructures. Our results identify driven strained graphene as a realistic and tunable platform for realizing and diagnosing Floquet higher-order topological phases.
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All authors Yu-Wen Xu, Xiaolin Wan, Zi-Ming Wang, Rui Wang, Dong-Hui Xu

Mesoscale and Nanoscale Physics

Coherent direction-dependent valley transport in Dirac/Weyl semimetals

Can Yesilyurt

We investigate the coherent transmission of collimated wave packets through an inversion-asymmetric electrostatic barrier in a Dirac or Weyl channel. For a barrier consisting of a vertical and an oblique interface, we show that forward- and backward-moving wave packets experience different Fermi-surface mismatch sequences, causing direction-dependent transport properties in channels with both isotropic and anisotropic energy dispersion. Furthermore, the simulation results reveal that the same barrier structure leads to valley-resolved direction-dependent transmission in tilted anisotropic systems. The proposed device is fully electrostatic, gate-tunable, and, in principle, compatible with a wide range of materials hosting Dirac/Weyl fermions.
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Materials Science

Fine-tuning a vision-language model for fracture-surface morphology recognition

Quanliang Liu, Jungtaek Kim, Kangwook Lee et al. (+1 more)

Vision-language models (VLMs) have shown strong potential for scientific image understanding, but general-purpose models often lack the domain-specific visual knowledge required for reliable materials characterization. In this work, we fine-tuned an open-source VLM (Qwen3-VL-32B-Instruct) for fracture-surface image analysis using a curated dataset of 13,168 open-source, literature-mined fracture-surface images. Morphology annotations were generated by GPT-5.2-Reasoning (high) from both the images and relevant excerpts of their source papers, and the dataset was further enriched with targeted manual collection and rotation-based augmentation. The resulting specialist model outperforms flagship proprietary multimodal models on a benchmark of 100 manually annotated images. It achieves a precision of 0.92, compared to 0.35 for the base Qwen3-VL-32B-Instruct, 0.58 for GPT-5.5-Reasoning (high), and 0.78 for Gemini 3.1 Pro-Reasoning (high). Dataset ablations show that manual collection of rare-feature images and augmentation via image rotation are both beneficial to improve recognition of less common fracture morphology features. We further discuss integrated use of the fine-tuned model with proprietary models to combine fracture-specific visual accuracy with broader multimodal reasoning for autonomous fractography. Although focused on fracture-surface images, this work demonstrates how VLMs can be adapted through targeted collection and fine-tuning on novel feature images to recognize those features and support downstream decision-making in autonomous microscopy workflows.
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All authors Quanliang Liu, Jungtaek Kim, Kangwook Lee, Hyunseok Oh

Strongly Correlated Electrons

Revisiting magnetoelectric response in collinear antiferromagnetic zigzag chains: A downfolding approach beyond conventional low-energy models

Shuhei Kanda and Satoru Hayami

Magnetoelectric (ME) effects in antiferromagnets provide a fertile platform for exploring symmetry-driven cross-correlated responses. However, their microscopic origin remains elusive and is often obscured in simplified low-energy descriptions. In this study, we revisit the microscopic mechanism of the ME effect in a collinear antiferromagnetic zigzag chain by employing a multi-orbital tight-binding model that explicitly includes both $s$- and $p$-orbital degrees of freedom. Using analytical and numerical calculations based on the Kubo formula, we demonstrate that the ME response is governed by orbital degrees of freedom activated through $s$–$p$ hybridization, while the spin contribution vanishes due to spin conservation. To elucidate the low-energy description, we derive an effective Hamiltonian projected onto the $s$-orbital subspace using the Schur complement. We show that a naive application of the Kubo formula within this effective model fails to capture the ME response. This issue is resolved by systematically incorporating vertex corrections in terms of orbital hybridization into the response functions. Furthermore, by introducing a quasiparticle renormalization scheme, we formulate a renormalized Kubo formula that preserves conservation laws and accurately reproduces the full multi-orbital results. Our analysis revisits the conventional low-energy perspective and reveals that the ME effect originates from virtual interorbital processes encoded in vertex corrections, rather than from the bare low-energy Hamiltonian. The effective framework developed here provides a unified microscopic understanding of orbital-driven ME responses and offers a systematic route to incorporate hybridization effects beyond simple low-energy models.
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Soft Condensed Matter · Cross-listed from physics.bio-ph

Cellular-scale mechanism of cell crawling responding to substrate stiffness

Sohei Nakamura and Mitsusuke Tarama

Biological cells are able to adapt their behaviour in response to environmental cues. Durotaxis is a phenomenon in which cells adjust their migration depending on the mechanical properties of a surrounding substrate. Although durotaxis has been studied more than two decades, basic cellular-scale mechanism of how cells regulate the motility responding to substrate stiffness remains to be elucidated. We address this issue by developing a theory utilising a mechanochemical model that integrates intracellular biochemical reactions with cellular deformation and substrate adhesion. Numerical analysis reveals that the characteristic speed and diffusion constant of cells change non-monotonically with respect to substrate stiffness, indicating the emergence of an optimal stiffness for migration. In addition, by introducing a memory effect that allows feedback from cell mechanics to the intracellular chemical reactions, the persistence time increases with substrate stiffness on a substrate softer than the optimal. We further investigate theoretically the origin of the non-monotonic dependence, that is comparable to the experimental observations, in terms of cell deformation and symmetry breaking in substrate adhesion. We believe that our study provides a unifying framework to understand complex durotactic cell migration.
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Disordered Systems and Neural Networks · Cross-listed from physics.optics

Fragility of Unidirectional Transport in Weakly Disordered Photonic Chern Insulators

Xiaoxuan Shi, Tiantao Qu, Xianbin Wu et al. (+3 more)

Photonic Chern insulators enable unidirectional light transport protected by nontrivial band topology – essential for robust photonic integrated circuits and error-free communication. However, disorder from impurities or defects inevitably exists in practical applications, yet how weak disorder affects topological chiral edge states remains insufficiently understood. Here, we reveal a previously unrecognized mechanism by which weak disorder can disrupt robust propagation of chiral edge states in photonic Chern insulators, despite the preservation of global topological invariants. By randomly replacing a small number of magnetized rods with nonmagnetized impurities in a magnetic photonic crystal, we find that when the excitation frequency approaches the single impurity defect state frequency, weak coupling between spatially extended defect states forms a topologically trivial impurity band inside the topological gap. This enables coexistence and coupling of defect states and chiral edge states. The reciprocal "necklace state" transport channels formed by coupled defect states break the expected unidirectional propagation in topological Chern insulators with weak disorder. Our work reveals that topological chiral edge state and disorder interactions are more intricate than previously understood and provides new insights into stability and control of topological transport in realistic applications.
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All authors Xiaoxuan Shi, Tiantao Qu, Xianbin Wu, Mudi Wang, Lei Zhang, Jun Chen

Strongly Correlated Electrons

Anomalous Phase-Coherence Scaling in a Quantum-Critical Dirac Semimetal

Sana Nakamichi, Ryotaro Kobara, Yoshinari Unozawa et al. (+8 more)

We have investigated the weak antilocalization (WAL) in the pressurized Dirac semimetal $\alpha$-(BEDT-TTF)$_2$I$_3$ across a correlation-driven quantum phase transition to a charge-ordered insulating state and evaluated the phase coherence length $L_{\phi}$ and its temperature scaling under various pressures from the low-temperature magnetoconductivity. In the high-pressure regime, the system exhibits the conventional two-dimensional dephasing behavior ($L_{\phi} \propto T^{-p}$ with $p \approx 1/2$), characteristic of electron-electron scattering in diffusive conductors. As the pressure approaches the critical pressure ($P_c \sim 1.2$ GPa), the temperature exponent is suppressed to $p \sim 0.3$, while $L_{\phi}$ remains large ($700\text{-}800$ nm at 0.5 K). This anomalous scaling suggests nontrivial inelastic scattering associated with Dirac electrons near the quantum critical point. The persistence of WAL across the transition supports a gapless or nearly gapless quantum phase transition.
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All authors Sana Nakamichi, Ryotaro Kobara, Yoshinari Unozawa, Yoshitaka Kawasugi, Sakura Hiramoto, Koki Funatsu, Toshio Naito, Masafumi Tamura, Reizo Kato, Yutaka Nishio, Naoya Tajima

Thursday 7 May

Statistical Mechanics

Anomalous Diffusion as Structural Memory: An Extended Structural Dynamics Approach

Patrick BarAvi

Sub-diffusion in biological systems is conventionally treated as anomalous, requiring fractional derivatives, heavy-tailed waiting times, or fitted memory kernels. We argue that this anomaly is an artifact of an incomplete phase space. Standard frameworks model diffusing particles as points. Biological molecules are not points. They are three-dimensional deformable entities whose position, orientation, and internal structure are irreducible physical properties, not modeling conveniences appended to a point mass. Within the Extended Structural Dynamics (ESD) framework, each particle is a primitive structured entity with translational, orientational, and deformational degrees of freedom. When dynamics on this full phase space are projected onto the translational subspace alone, a memory kernel emerges from the projection without phenomenological postulate. The subdiffusion exponent is determined by the internal mode spectrum, independently measurable from B-factors, NMR order parameters, or molecular dynamics simulations, without fitting to transport data. Four falsifiable predictions follow: subdiffusion strength correlates with molecular flexibility; temperature drives crossover to normal diffusion at a characteristic energy scale set by internal mode frequencies; a non-zero rotation-translation cross-correlation spectrum encodes internal dynamics, identically zero in point-particle models; and memory timescales scale as the square of particle size. Quantitative consistency with experimental observations for proteins in crowded media is demonstrated using independently estimated structural parameters. What appears anomalous from the point-particle perspective is the expected behavior of structured matter projected onto an impoverished description. The anomaly is not in the physics. It is in the phase space.
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Materials Science

SLayerGen: a Crystal Generative Model for all Space and Layer Groups

Rees Chang, Andrew Novick, Ryan P Adams et al. (+1 more)

Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as 2D superconductors, thin film semiconductors, and catalytic surfaces are diperiodic, i.e., aperiodic along one of the lattice directions. These systems are invariant under the layer groups, which are known to influence materials properties yet not considered by existing models. In this paper, we propose SLayerGen, a generative model that produces crystals constrained to be invariant to any space or layer group. SLayerGen consists of coarse-to-fine discrete autoregressive lattice generation; transformer-based autoregressive sampling of Wyckoff positions, elements, and numbers of symmetrically unique atoms; and space or layer group equivariant diffusion of atomic coordinates. For the diffusion component, we corrected an inconsistency in the loss from prior work arising from hexagonal groups being non-orthogonal in fractional coordinates. To facilitate progress in generative modeling of diperiodic materials, we assembled and filtered datasets of monolayers and bilayers, propose relevant evaluation metrics, and developed novel representations for layer group symmetries. For de novo generation of diperiodic materials, SLayerGen achieves consistent performance gains over bulk crystal generative models and is competitive when training jointly on bulk and diperiodic materials.
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All authors Rees Chang, Andrew Novick, Ryan P Adams, Elif Ertekin

Materials Science

Two-dimensional clay channels for tunable nanofluidic memristor

Sangeeta Yadav, Raj Kumar Gogoi, Aziz Lokhandwala et al. (+4 more)

Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this effect to store and process information within a single component. We report switching between distinct memory loops by tuning ion transport pathways, governed by asymmetrical device architecture and intrinsic surface-charge. Polarity-dependent memory switching between crossing-1 and crossing-2 loops is achieved solely by altering electrode configurations, without modifying electrolyte, channel surface chemistry or device structure: providing mechanistic insights into ionic memristors through a straightforward, experimental strategy. The memristive characteristics are demonstrated in both in-plane and out-of-plane channel configurations with channel lengths spanning from centimeters to micrometers length scales using re-stacked vermiculite membranes and further investigated for miniaturization with devices of nanometer scale channel lengths, fabricated via ultramicrotomy method. Furthermore, we demonstrate neuromorphic functionalities, including synaptic potentiation-depression and programmable memory retention, highlighting potential for bio-inspired computing systems. Cost-effective and scalable fabrication solution processed vermiculite membrane memristors pave the way for practical integration of nanofluidic memristors for neuromorphic computing applications.
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All authors Sangeeta Yadav, Raj Kumar Gogoi, Aziz Lokhandwala, Siddhi Vinayak Pandey, Ankit Bhardwaj, Sunando DasGupta, Boya Radha

Materials Science · Cross-listed from cs.LG

Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?

Yuchu Liu, Rui Zhu, Jingwei Xiong et al. (+1 more)

Can large language models predict physical and mechanical polymer properties simply by reading unstructured scientific prose? Polymer performance is rarely determined by chemical structure alone; identical nominal polymers can exhibit drastically different behaviors depending on their synthesis route, processing history, morphology, and testing conditions. Yet, state-of-the-art polymer property models typically rely on structure-only representations – such as SMILES or molecular graphs – which strip away this vital experimental context. In this work, we introduce PolyLM, a natural-language-only, process- and condition-aware framework that predicts materials performance directly from full-text literature. By circumventing structural inputs entirely, PolyLM preserves the nuanced, unstructured descriptions of synthesis and processing reported by domain scientists. To train this framework, we curated an unprecedented, literature-scale dataset encompassing 185,000 scientific papers and over 276,400 unique polymer samples across 22 physical, mechanical, and thermal properties. We fine-tuned a massive 9-billion-parameter language model (Qwen3.5-9B) using Low-Rank Adaptation (LoRA) and task-level uncertainty weighting. Evaluated on 68,283 held-out observations, the model achieves remarkably high predictive accuracy, establishing new state-of-the-art benchmarks for complex properties. Across the 22 diverse targets, the model achieves a median $R^2$ of 0.74, with predictions for key thermal, mechanical, and physicochemical properties frequently surpassing an $R^2$ of 0.80. These results unequivocally demonstrate that natural language is a powerful, highly scalable interface for realistic materials performance prediction.
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All authors Yuchu Liu, Rui Zhu, Jingwei Xiong, Haixu Tang

Mesoscale and Nanoscale Physics

Dynamically Characterizing the Structures of Dirac Points via Wave Packets

Dan-Dan Liang, Xin Shen and Zhi Li

Topological non-trivial band structures are the core problem in the field of topological materials. In this paper, we investigate the topological band structure in a system with controllable Dirac points from the perspective of wave packet dynamics. By adding a third-nearest-neighboring coupling to the graphene model, additional pairs of Dirac points emerge. The emergence and annihilation of Dirac points result in hybrid and parabolic points, and we show that these band structures can be revealed by the dynamical behaviors of wave packets. Particularly, for the gapped hybrid point, the motion of the wave packet shows a one-dimensional Zitterbewegung motion. Furthermore, we also show that the winding number associated with the Dirac point and parabolic point can be determined via the center-of-mass and spin texture of wave packets, respectively. The results of this work could motivate new experimental methods to characterize the system's topological signatures through wave packet dynamics, which may also find application in systems of other exotic topological materials.
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Materials Science

Exploring the Potential of Ternary Blending for Two and Three-Junction RAINBOW Solar Cells

Francesc Xavier Capella-Guardià, Jolanda Simone Müller, Muhammad Ahsan Saeed et al. (+7 more)

The efficiency of organic photovoltaics (OPV) has been steadily increasing over the past decade until reaching the 20% milestone. Multijunction architectures provide a promising approach to further enhance performance. Here we explore the potential of a spectral splitting geometry, referred to as RAINBOW, in which subcells are placed side-by-side and externally connected, thus minimizing the fabrication and current matching challenges found in vertically stacked configurations. First, we tested 7 different binaries with bandgaps spanning from 1.98 to 1.16 eV. The systems with the widest and narrowest gaps suffered greater losses and so we evaluate if ternary mixing could help to overcome these limitations by evaluating 5 different ternaries. Generally speaking, ternary mixing tunes the Voc, and when morphology and energy levels are well aligned, the overall PCE can be boosted in the spectral region where the subcell should absorb, as is the case for PTB7-Th:COTIC-4F:BTP-eC9 when operating as red subcell. Device simulations help to identify the 2-junction and 3-junction configurations with highest PCEs, all of which include ternaries. We fabricate proof-of-concept RAINBOW devices using scalable methods in which the subcells are deposited by meniscus-guided blade coating. The efficiency improves from 12.9% in single-junction devices to 15.9% in 2-junction devices (16.4% in simulations) and 17.3% in 3-junction devices (17.7% in simulations), confirming the viability of the RAINBOW architecture for scalable, high-efficiency OPVs. Finally, detailed balance analysis indicates that the potential of this geometry can be very high provided that high efficiency wide bandgap (2-2.5 eV) materials become available.
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All authors Francesc Xavier Capella-Guardià, Jolanda Simone Müller, Muhammad Ahsan Saeed, Xabier Rodríguez-Martínez, Miquel Casademont-Viñas, Albert Harillo-Baños, Jaime Martín, Jenny Nelson, Alejandro R. Goñi, Mariano Campoy-Quiles

Mesoscale and Nanoscale Physics

Nonadiabatic Theory of Phonon Magnetic Moments in Insulators and Metals

Haoran Chen, Wenqin Chen, Kaijie Yang et al. (+2 more)

We develop a nonadiabatic theory of phonon magnetic moments applicable to both insulators and metals. By relating the phonon magnetic moment to the force-velocity response of ions in a magnetic field, we derive a gauge-invariant expression using a gauge-covariant Wigner expansion. The formalism naturally separates Fermi-sea and Fermi-surface contributions and captures the full dependence on phonon frequency. In gapped systems, our theory reduces to previous adiabatic expressions in the low-frequency limit. Beyond this limit, it reveals additional contributions arising from resonant interband processes and the Fermi surface. Applying our theory to Pb$_{1-x}$Sn$_x$Te, we find that the Fermi-surface contribution substantially enhances the phonon magnetic moment, reproducing the same order of magnitude as the experimental observation. Our results provide a unified framework for describing phonon magnetic moments beyond the adiabatic regime.
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All authors Haoran Chen, Wenqin Chen, Kaijie Yang, Ting Cao, Di Xiao

Statistical Mechanics

Multilane Asymmetric Exclusion Process with stationary Bernoulli measure

Vladislav Popkov

We consider an Asymmetric Exclusion Process evolving on parallel mutually interacting lanes with neighbouring nearest hoppings of hardcore particles. Number of particles on each lane is conserved. We find a choice of the hopping rates, for which the process has Bernouilli stationary product measure, and calculate the stationary particle currents as a function of average particle densities.
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Strongly Correlated Electrons

Topological superconductivity in a Hubbard model for twisted bilayer cuprates

T. Vibert and D. Sénéchal

We investigate the emergence of nontrivial topology in a twisted cuprate bilayer described by the Hubbard model in the weak-interaction regime. Our results show that the topological character depends sensitively on the doping level. For $U/t=3.85$, the Chern number assumes a value of $\pm 8$ in the electron-doped case, whereas it vanishes (0) in the hole-doped regime. The presence of nontrivial topology is further supported by an analysis the associated edge states and their chirality in a finite-width geometry, while keeping full correlations.
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Soft Condensed Matter · Cross-listed from math.AP

Asymptotic analysis of the energy for a ferroelectric nematic

Dmitry Golovaty and Peter Sternberg

The variational model for a ferroelectric nematic bears close resemblance to the well-known energy model for micromagnetics. Despite this similarity, the two models operate in fundamentally distinct parameter regimes describing different physics. In this paper we establish that the ferroelectric nematic energy functional $\Gamma$-converges to the energy of a nematic with high elastic anisotropy.
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Disordered Systems and Neural Networks · Cross-listed from quant-ph

Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics

Steven Abel, Andrei Constantin and Luca A. Nutricati

Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial Hamiltonian into one whose ground state encodes the solution, the system traverses a complex landscape via a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Unlike gate-based quantum computing, quantum annealing is a specialised and near-term approach aimed primarily at discrete optimisation and sampling tasks. While it is not expected to provide polynomial-time solutions to NP-hard problems in the worst case, it offers a physically motivated heuristic for navigating rugged energy landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, placing them among the largest controllable quantum devices currently available. As a result, their significance extends beyond optimisation: they also function as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are difficult to access using classical simulation. In this review we present an accessible introduction to the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse how tunnelling, spectral gaps, and open-system effects shape computational performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges in benchmarking, scaling, and control. These perspectives position quantum annealing as a distinctive framework at the interface of optimisation, stochastic sampling, and programmable quantum dynamics, with a role that is complementary to both classical algorithms and gate-based quantum computing.
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Strongly Correlated Electrons

Fluctuation-driven chiral ferromagnetism

Rokas Veitas, Ahmed Khalifa, Francisco Machado et al. (+1 more)

Quantum fluctuations are often suppressed in ferromagnetic materials because they admit a simple unfrustrated ground state, greatly limiting the scope of the phenomena that can be observed. In this work, we show how naturally occurring magnetization-non-conserving couplings fundamentally alter this paradigm by demonstrating the existence of a chiral ferromagnet that is stabilized by quantum fluctuations. More specifically, we show how these spin-orbit interactions modify the classical phase diagram; whereas a classical analysis predicts only achiral collinear states, we observe fluctuation-stabilized phases, including a ferromagnet with large orbital chirality and a chiral stripe. We elucidate how such couplings generate a scalar orbital chirality spontaneously, in contrast to conventional mechanisms which rely upon a field-induced canting of vector chiral order. The resultant chiral states exhibit distinct transport signatures, namely an enhanced thermal Hall effect, and are of direct relevance to moiré heterostructures, Rydberg-atom arrays, and solid-state materials featuring non-Kramers spins
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All authors Rokas Veitas, Ahmed Khalifa, Francisco Machado, Shubhayu Chatterjee

Materials Science

LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments

Boris Slautin, Utkarsh Pratiush, Yu Liu et al. (+2 more)

Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy tuning, strucutre property relationship discovery, and exploration of combinatorial libraries. However, most current workflows remain limited to selecting measurements within fixed objective or hypothesis spaces, rather than generating new physical models from experimental data. Here, we introduce an open hypothesis-learning framework that combines symbolic regression with large-language-model-based physical evaluation and implement it for autonomous scanning probe microscopy. Symbolic regression generates candidate analytical relationships directly from sparse measurements, while the language-model evaluator ranks these candidates according to physical plausibility, scaling behavior, and consistency with known mechanisms. We demonstrate the approach on autonomous piezoresponse force microscopy measurements of ferroelectric domain switching in a PZT thin film. Starting from five seed measurements, the workflow evolves from physically incomplete candidate expressions toward interpretable voltage-time growth laws consistent with kinetic domain-wall motion. This work extends autonomous microscopy from closed-loop optimization toward open hypothesis discovery, where candidate physical laws emerge from the experiment itself rather than being specified in advance. More broadly, the framework establishes a route for integrating symbolic regression, physical reasoning, and adaptive experimentation into hierarchical autonomous scientific workflows.
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All authors Boris Slautin, Utkarsh Pratiush, Yu Liu, Kamyar Barakati, Sergei Kalinin

Mesoscale and Nanoscale Physics

Majorana bound states in chiral ferromagnet-superconductor heterostructures revisited

A. S. Slobodskoi, S. S. Apostoloff and I. S. Burmistrov

Majorana zero modes are central to the pursuit of fault-tolerant topological quantum computation. While traditionally sought in one-dimensional hybrid nanowires, a robust alternative platform involves heterostructures combining superconductors with noncollinear magnets. This work focuses on a particularly promising system: a chiral ferromagnet hosting a magnetic skyrmion coupled to a superconducting film containing a superconducting vortex. Such skyrmion-vortex pairs have recently been realized experimentally and are theorized to harbor localized Majorana states, offering a potential pathway for braiding operations. We present a comprehensive theoretical analysis of the low-energy quasiparticle bound states in these heterostructures. Extending previous studies, we develop an analytical framework for the Majorana wavefunctions as well as the wavefunctions and spectrum of other lowlying states within a Bogoliubov-de Gennes approach. Our analytical results explicitly demonstrate the critical role of spin-orbit coupling for the stabilization of Majorana modes and provides approximate analytical expressions for low-lying states localized at the vortex, both with and without an accompanying skyrmion. The derived analytical results show excellent agreement with numerical simulations. We further elucidate the role of realistic effects, including vector potentials and texture perturbations from stray magnetic fields, to assess their impact.
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