Peer Revenue Equity in science

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Condensed Matter

cond-mat Watch Condensed Matter

Wednesday 18 February

Statistical Mechanics · Cross-listed from hep-th

Where Multipartite Entanglement Localizes: The Junction Law for Genuine Multi-Entropy

Norihiro Iizuka and Akihiro Miyata

We uncover a "junction law" for genuine multipartite entanglement, suggesting that in gapped local systems multipartite entanglement is controlled and effectively localized near junctions where subsystem boundaries meet. Using the Rényi-2 genuine multi-entropy $\mathrm{GM}^{(\mathtt{q})}_2$ as a diagnostic of genuine $\mathtt{q}$-partite entanglement, we establish this behavior in $(2+1)$-dimensional gapped free-fermion lattices with correlation length $\xi$. For partitions with a single junction, $\mathrm{GM}^{(\mathtt{q})}_2$ exhibits a universal scaling crossover in $L/\xi$, growing for $L\ll\xi$ and saturating to a $\xi$-dependent constant for $L\gg\xi$, up to $\mathcal{O}(e^{-L/\xi})$ corrections. In sharp contrast, for partitions without a junction, $\mathrm{GM}^{(\mathtt{q})}_2$ is exponentially suppressed in $L/\xi$ and drops below numerical resolution once $L\gg\xi$. We observe the same pattern for $\mathtt{q}=3$ (tripartite) and $\mathtt{q}=4$ (quadripartite) cases, and further corroborate this localization by translating the junction at fixed system size. We also provide a geometric explanation of the junction law in holography. Altogether, these results show that in this gapped free-fermion setting genuine multipartite entanglement is localized within a correlation-length neighborhood of junctions.
Read the full abstract

Mesoscale and Nanoscale Physics · Cross-listed from physics.optics

Birefringence-Driven Anisotropic $\alpha$-MoO3 Optical Cavities

Jia-Liang Xie, Ting-Ting Wang, Chen-Kai Liu et al. (+4 more)

Many anisotropic layered materials, despite their strong in-plane birefringence, exhibit substantial visible absorption, which severely restricts cavity lengths and hinders the observation of purely birefringence-governed optical phenomena. Here, we realize a birefringence-driven anisotropic optical cavity using $\alpha$-MoO3 flakes, capitalizing on their ultralow optical loss and pronounced in-plane birefringence. Using angle-resolved polarized Raman (ARPR) spectroscopy, we observe a mode-sensitive enhancement of anisotropy, dependent on both flake thickness and Raman shift. A unified model that incorporates the intrinsic Raman tensor, birefringence, and chromatic dispersion accurately reproduces the experimental data, elucidating how cavity resonances at both excitation and scattered wavelengths interact. Within this framework, the intrinsic phonon anisotropy is quantified, providing invaluable insights for accurately predicting ARPR responses and identifying crystallographic orientation. This work provides fundamental insights into birefringence-governed cavities and opens avenues for high-performance birefringent optics and cavity-enhanced anisotropic phenomena.
Read the full abstract

All authors Jia-Liang Xie, Ting-Ting Wang, Chen-Kai Liu, Rui Mei, Li-Fa Zhang, Miao-Ling Lin, Ping-Heng Tan

Strongly Correlated Electrons

Stripe antiferromagnetism in van der Waals metal HoTe3 decoupled from charge density wave order

Weiyi Yun, Ryota Nakano, Ryo Misawa et al. (+10 more)

The $R\mathrm{Te}_3$ ($R = \text{rare earth}$) family of layered van der Waals (vdW) compounds hosts coexisting magnetic and charge density wave (CDW) orders, yet the interplay between these degrees of freedom remains little explored. Combining polarized and unpolarized neutron diffraction on single-crystal $\mathrm{HoTe}_3$, we identify two distinct antiferromagnetic (AFM) phases, both exhibiting a collinear $\uparrow\uparrow\downarrow\downarrow$ motif within individual vdW layers. The two phases are distinguished by the vdW stacking of magnetic layers: ferromagnetic (FM) stacking in the higher-temperature AFM-II phase, here termed “vertical-stripe”, and AFM stacking in the AFM-I ground state, here termed “tilted-stripe”; the two phases have propagation vectors $\boldsymbol{q}_{\mathrm{m2}} = (0.48, 0, 0)$ and $\boldsymbol{q}_{\mathrm{m1}} = (0.5, 0.5, 0)$, respectively. In contrast to the CDW-driven exotic magnetism in $\mathrm{DyTe}_3$, $\mathrm{TbTe}_3$, and $\mathrm{GdTe}_3$, we find no evidence for coupling between magnetism and CDW in $\mathrm{HoTe}_3$. The relative alignment between AFM and CDW propagation vectors, as well as single-ion anisotropy, are likely essential for generating coupled spin/charge orders in layered vdW systems.
Read the full abstract

All authors Weiyi Yun, Ryota Nakano, Ryo Misawa, Rinsuke Yamada, Shun Akatsuka, Yoshichika Onuki, Priya Ranjan Baral, Hiraku Saitoh, Ryoji Kiyanagi, Takashi Ohhara, Taro Nakajima, Taka-hisa Arima, Max Hirschberger

Materials Science

Unveiling and quantifying the topology-dependent pre-melting of nanoparticles

Marthe Bideault, Arnaud Allera, Ryoji Asahi et al. (+2 more)

The melting of metallic nanoparticles is governed by surface premelting, a phenomenon traditionally modeled as the isotropic growth of a uniform liquid shell. Challenging this classical view, we report facet-dependent premelting in hexagonal close-packed Co nanoparticles, arising from the structural heterogeneity of their surface. In molecular dynamics simulations (587 to 13047 atoms), the onset of surface mobility is observed as low as 20% of the bulk melting point, driven by the early disordering of stepped $\{01\bar{1}1\}$ facets. These facets consistently melt nearly 150 K below flat $\{0001\}$ facets, regardless of particle size. We show that both surface and facet melting temperatures scale with nanoparticle size through the Gibbs-Thomson effect, and determine a size-dependent critical liquid layer thickness that triggers complete melting of the nanoparticle, which saturates near three atomic layers. Our results confirm recent experimental observations of surface premelting and extend the framework to anisotropic particles with facet-orientation-dependent behavior.
Read the full abstract

All authors Marthe Bideault, Arnaud Allera, Ryoji Asahi, Jérôme Creuze, Erich Wimmer

Mesoscale and Nanoscale Physics · Cross-listed from physics.optics

Breaking the Moss rule

Søren Raza, Kristian Sommer Thygesen and Gururaj Naik

Photonic devices depend critically on the dielectric materials from which they are made, with higher refractive indices and lower absorption losses enabling new functionalities and higher performance. However, these two material properties are intrinsically linked through the empirical Moss rule, which states that the refractive index of a dielectric decreases as its band gap energy increases. Materials that surpass this rule, termed super-Mossian dielectrics, combine large refractive indices with wide optical transparency and are therefore ideal candidates for advanced photonic applications. This Review surveys the expanding landscape of high-index dielectric and semiconductor materials, with a particular focus on those that surpass the Moss rule. We discuss how electronic band structures with a large joint density of states near the band edge give rise to super-Mossian behavior and how first-principles computational screening can accelerate their discovery. Finally, we establish how the refractive index sets the performance limits of nanoresonators, waveguides, and metasurfaces, highlighting super-Mossian dielectrics as a promising route toward the next performance leap in photonic technologies.
Read the full abstract

Superconductivity

Superconducting Decoherence and Thermal Quenching of the Josephson Diode Effect in Low-Dimensional Josephson Systems

F. Yang, C. Y. Dong, Joshua A. Robinson et al. (+1 more)

Motivated by recent studies on superconducting (SC) diode nonreciprocity, we uncover a generic smooth SC-phase decoherence mechanism in low-dimensional Josephson structures. Contrary to the conventional single-energy-scale paradigm where Josephson coherence and diode nonreciprocity vanish simultaneously only at the SC gap-closing temperature, we demonstrate, within a fully self-consistent microscopic framework beyond mean-field theory, that SC phase fluctuations generically split these phenomena into distinct energy scales. As a result, rather than a single SC-normal transition, the system exhibits a sequence of distinct thermal crossovers upon heating: the diode effect disappears first at $T_{\eta}$, Josephson coherence is subsequently lost at $T_c$, and the SC gap collapses only at a higher temperature $T_s$. Using a bilayer SC system as a concrete example, we show that the separation between these temperature scales is not solely dictated by Josephson coupling, but is instead strongly and counterintuitively shaped by the in-plane disorder and carrier density. These findings reveal that smooth SC phase decoherence introduces a distinct and more fragile energy scale, with potential implications for layered superconductors such as cuprates and recently discovered nickelates, as well as for SC qubit platforms.
Read the full abstract

All authors F. Yang, C. Y. Dong, Joshua A. Robinson, L. Q. Chen

Superconductivity

Coexistence of Rashba and Ising Spin-Singlet Pairings in Two-Dimensional IrTe$_{2}$

Kunal Dutta, Rajesh O. Sharma, Shreya Das et al. (+3 more)

Symmetry offers a useful approach to unfold the intertwined degrees of freedom. Thus it paves the way to resolve coexisting quantum orders into distinct symmetry sectors. Motivated by the recent observation of superconductivity in nano-flaked IrTe$_2$, we investigate the superconductivity in strain-stabilized two-dimensional (2D) limit of IrTe$_2$ by combining density-functional theory with mean-field solution of spin-fluctuation mediated pairing interaction on a symmetry-constrained ${\bf k}\cdot{\bf p}$ model. The spin-orbit coupled band structure shows $\Gamma$-centred Fermi sheets with coexistence of band-selective Rashba-like (in-plane) and Ising-like (out-of-plane) superconductivity. Remarkably, the superconducting gaps are odd in spin, orbital, and momentum channels despite the presence of global inversion symmetry. Fermi surface topologies and little-group symmetry enforce distinct irreducible representations to the Rashba and Ising channels, forbidding their mixing. Our findings open up a symmetry-based route to multichannel superconductivity in 2D transition-metal dichalcogenides with unique functionalities.
Read the full abstract

All authors Kunal Dutta, Rajesh O. Sharma, Shreya Das, Indra Dasgupta, Tanmoy Das, Tanusri Saha-Dasgupta

Mesoscale and Nanoscale Physics

Stack of correlated insulating states in bilayer graphene kagome superlattice

Xinyu Cai, Fengfan Ren, Qiao Li et al. (+9 more)

Graphene-based systems have emerged as a rich platform for exploring emergent quantum phenomena-including superconductivity, magnetism, and correlated insulating behavior-arising from flat electronic bands that enhance many-body interactions. Realizing such flat bands has thus far relied primarily on moiré graphene superlattices or rhombohedral stacking graphene systems, both of which face challenges in reproducibility and tunability. Here, we introduce an artificial Kagome superlattice in bilayer graphene, engineered via nanopatterning of the dielectric substrate to create a precisely defined and electrostatically tunable periodic potential. Magnetotransport measurements reveal the emergence of a stack of correlated insulating states at moderate superlattice potentials, characteristic of strong electron-electron interactions within Kagome-induced flat bands. As temperature increases, these correlated gaps collapse, signaling the thermal suppression of interaction-driven states. Continuum-model calculations confirm the formation of multiple flat minibands and reproduce the observed evolution of band reconstruction. Our results establish dielectric-patterned graphene superlattices as a robust and controllable architecture for realizing flat-band-induced correlated phenomena beyond moiré systems.
Read the full abstract

All authors Xinyu Cai, Fengfan Ren, Qiao Li, Yanran Shi, Yifan Wang, Yifan Zhang, Zhenghang Zhi, Jiawei Luo, Yulin Chen, Jianpeng Liu, Xufeng Kou, Zhongkai Liu

Statistical Mechanics · Cross-listed from quant-ph

Reinforcement learning for path integrals in quantum statistical physics

Timour Ichmoukhamedov and Dries Sels

Machine learning is rapidly finding its way into the field of computational quantum physics. One of the most popular and widely studied approaches in this direction is to use neural networks to model quantum states (NQS) in the Hamiltonian formulation of quantum mechanics. However, an alternative angle of attack to leverage machine learning in physics is through the path integral formulation, which has so far received far more limited attention. In this paper, we explore how reinforcement learning can be used to compute a class of Euclidean path integrals that yield the thermal density matrix of a quantum system, thereby enabling the computation of the free energy or other thermal expectation values. In particular, we propose a two-step approach with the unique feature that after a variational approximation for a quantity is obtained in a first step, it can then be used to efficiently compute the exact result in a second step. We benchmark this method on several simple systems and then apply it to the quantum rotor chain.
Read the full abstract

Materials Science

Negative Strain-Rate Sensitivity in Metallic Glasses Driven by Rejuvenation-Relaxation Competition: Kinetic Monte Carlo Simulations and a Minimal Effective Model

Tomoaki Niiyama, Akio Ishii, Takahiro Hatano et al. (+2 more)

When strain-rate sensitivity (SRS) is negative in metallic glasses, the material becomes weaker as the deformation rate increases, leading to accelerated plastic deformation and, eventually, catastrophic fracture. In this study, we elucidate the mechanism underlying the negative SRS using micromechanics-based kinetic Monte Carlo simulations that couple heterogeneous randomized shear transformation zone (STZ) models for metallic glasses. The model accounted for both the thermomechanical structural rejuvenation and relaxation of the energy barrier for thermal activation of STZs, incorporating a Kohlrausch-Williams-Watts (KWW)-type relaxation function. The present simulations systematically reproduce the dependence of flow stresses on strain rate, temperature, and the form of the relaxation function. The SRS tends to decrease at high strain rates and low temperatures in the simulations, and negative SRS appears when a compressed-exponential relaxation function is employed. Shear localization also appears; however, the conditions under which the observed localization emerges do not fully coincide with those leading to the negative SRS, leaving the dominant factor unclear. To clarify the dominant factor, we introduce a simplified theoretical model that reproduces flow stresses consistent with the simulation results. An analytical expression derived from the theoretical model reveals that negative SRS originates primarily from the temporal evolution of the activation barrier. Specifically, negative SRS arises when the timescale of external loading exceeds that of STZ relaxation.
Read the full abstract

All authors Tomoaki Niiyama, Akio Ishii, Takahiro Hatano, Tomotsugu Shimokawa, Shigenobu Ogata

Materials Science

Dislocation-ledge coupling governs semicoherent precipitate growth

Jin-Yu Zhang, Juan Du, Lin Yang et al. (+3 more)

Many crystalline materials acquire their properties as one crystal phase grows through another, but a long-standing defect-kinetic puzzle remains: how dense interfacial dislocation networks between two crystals advance, reorganize, and accommodate strain without relying on ordinary glide. Here, using in situ transmission electron microscopy, O-lattice analysis, and three-dimensional phase-field crystal simulations, we identify how semicoherent interfaces overcome this constraint during lath precipitate growth. In situ observations of austenite precipitates in duplex stainless steel reveal nanometer-high growth ledges propagating laterally along migrating habit planes. O-lattice analysis shows how lattice misfit prescribes a closed network across habit planes, side facets, and end faces. Simulations resolve the hidden three-dimensional dynamics: the network undergoes diffusion-assisted, non-conservative motion, producing steady end-face advance and ledge-mediated broad-facet migration while accommodating transformation strain. These results reveal a general defect-kinetic route linking point-defect transport, dislocation-network motion, interface migration, and morphology evolution.
Read the full abstract

All authors Jin-Yu Zhang, Juan Du, Lin Yang, Frédéric Mompiou, Shigenobu Ogata, Wen-Zheng Zhang

Materials Science

Generative Inverse Estimation of 3D Atomic Coordination from Near-Edge Spectra via Equivariant Diffusion Models

Ren Okubo, Yu Fujikata, Izumi Takahara et al. (+1 more)

Extracting 3D atomic coordinates from spectroscopic data is a longstanding inverse problem. We present an equivariant diffusion model that generates site-specific 3D structures directly from near-edge spectra (ELNES/XANES). Trained on Si-O crystals, the model achieves radial accuracy comparable to Extended X-ray Absorption Fine Structure (EXAFS) (RMSD ~0.06 Å) but with superior coordination number precision (errors < 4.3% vs. EXAFS ~20%). Crucially, it reconstructs full 3D geometries including bond angles, overcoming the limitations of 1D radial distribution analysis. The model demonstrates robust out-of-distribution generalization, accurately predicting local structures in amorphous systems despite being trained exclusively on crystalline lattices. Application to experimental O K-edge spectra from {\alpha}-quartz validates practical applicability. This generative approach outperforms template matching and establishes automated, quantitative 3D structure determination from spectroscopic data.
Read the full abstract

All authors Ren Okubo, Yu Fujikata, Izumi Takahara, Teruyasu Mizoguchi

Mesoscale and Nanoscale Physics

Enhanced Graphene-Water Thermal Transport via Edge Functionalization without Compromising In-Plane Thermal Conductivity

John Crosby, Haoran Cui, Mehrab Lotfpour et al. (+2 more)

Interfacial thermal transport between graphene and water plays a critical role in a wide range of thermal and energy applications. Although chemical functionalization can significantly enhance graphene-water interfacial thermal conductance, it often degrades graphene's intrinsic in-plane phonon transport. In this work, we perform a systematic deep neural network molecular dynamics study comparing edge-functionalized graphene nanoribbons with surface-functionalized graphene in aqueous environments. We demonstrate that functionalizing only 10% of the ribbon edges with hydroxyl groups increases the graphene-water interfacial thermal conductance by more than eightfold, primarily due to strengthened interfacial interactions and improved wettability at the edges. In contrast to basal-plane oxidation, edge functionalization largely preserves in-plane thermal conductivity. Importantly, hydroxyl edge groups exert competing effects on phonon transport: they introduce additional boundary scattering that suppresses heat conduction, while simultaneously passivating dangling bonds at bare edges, thereby reducing phonon localization and edge-induced scattering. This competition leads to a non-monotonic dependence of in-plane thermal conductivity on edge functionalization ratio. These results establish edge functionalization as an effective strategy for enhancing graphene-water interfacial thermal transport without sacrificing intrinsic phonon transport properties.
Read the full abstract

All authors John Crosby, Haoran Cui, Mehrab Lotfpour, Yan Wang, Lei Cao

Tuesday 17 February

Materials Science · Cross-listed from physics.comp-ph

A comparative study of transformer models and recurrent neural networks for path-dependent composite materials

Petter Uvdal and Mohsen Mirkhalaf

Accurate modeling of Short Fiber Reinforced Composites (SFRCs) remains computationally expensive for full-field simulations. Data-driven surrogate models using Artificial Neural Networks (ANNs) have been proposed as an efficient alternative to numerical modeling, where Recurrent Neural Networks (RNNs) are increasingly being used for path-dependent multiscale modeling by predicting the homogenized response of a Representative Volume Element (RVE). However, recently, transformer models have been developed and they offer scalability and efficient parallelization, yet have not been systematically compared with RNNs in this field. In this study, we perform a systematic comparison between RNNs and transformer models trained on sequences of homogenized response of SFRC RVEs. We study the effect on two types of hyperparameters, namely architectural hyperparameters (such as the number of GRU layers, hidden size, number of attention heads, and encoder blocks) and training hyperparameters (such as learning rate and batch size). Both sets of hyperparameters are tuned using Bayesian optimization. We then analyze scaling laws with respect to dataset size and inference accuracy in interpolation and extrapolation regimes. The results show that while transformer models remain competitive in terms of accuracy on large datasets, the RNNs demonstrate better accuracy on small datasets and show better extrapolation performance. Furthermore, under extrapolation, there is a clear difference, where the RNN remains accurate, while the transformer model performs poorly. On the other hand, the transformer model is 7 times faster at inference, requiring 0.5 ms per prediction compared to the 3.5 ms per prediction for the RNN model.
Read the full abstract

Materials Science · Cross-listed from physics.chem-ph

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-ion Battery Chemistry by Mass Spectrometry and Electro-Chemical Reaction Networks

Mona Abdelgaid, Oliver Hvidsten, Theo Sombret et al. (+10 more)

The formation and stability of the solid electrolyte interphase (SEI) play a central role in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI's heterogeneous, dynamic, and multi-phase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational-experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electro-chemical reaction networks (eCRNs), stochastic algorithms, and Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonate-based electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in literature and predicted 28 novel SEI species, nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectra analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational-experimental approach deepens our molecular-level understanding of SEI chemistry and supports the rational design of advanced electrolytes and engineered interphases for next-generation lithium-based batteries.
Read the full abstract

All authors Mona Abdelgaid, Oliver Hvidsten, Theo Sombret, Egon Kherchiche, Julien Maillard, Antonin Gajan, Patrick Bernard, Kamila Kazmierczak, Mauricio Araya-Polo, Germain Salvato Vallverdu, Carlos Afonso, Pierre Giusti, Kristin A. Persson

Mesoscale and Nanoscale Physics

Influence of electrical properties on thermal boundary conductance at metal/semiconductor interface

Quentin Pompidou, Juan Carlos Acosta Abanto, M. Brouillard et al. (+10 more)

Recent experimental investigations have demonstrated that doping a semiconductor is a route to increase the thermal boundary conductance at metal/semiconductor interfaces. In this work, the influence of the electrical properties on heat transfer across metal/doped semiconductor junctions is investigated. Specifically, thermal boundary conductance at the interfaces between p- and n-doped silicon and titanium is measured by employing frequency-domain photothermal radiometry under varying external conditions. The influence of the doping level of the semiconductor, the barrier height, and the space charge area is analyzed. In particular, a 40% increase in the interface thermal conductance with the application of a current at n-doped silicon/titanium interfaces is reported. The enhancement of the thermal boundary conductance is explained by the shrinking of the surface charga area induced by the electric current. This study opens the way to modulating interfacial heat transfer at metal/semiconductor interfaces through fine tuning of electrical effects.
Read the full abstract

All authors Quentin Pompidou, Juan Carlos Acosta Abanto, M. Brouillard, Nicolas Bercu, L. Giraudet, Rami Sheikh, C. Adessi, S. Mérabia, S. Gomès, Pierre-Olivier Chapuis, J.-F. Robillard, Mihai Chirtoc, N. Horny

Soft Condensed Matter

Evaporation-Induced Pattern Formation and Wetting in Active Microtubule-Kinesin Droplets

Vahid Nasirimarekani, Mehrana R. Nejad, Olinka Ramírez-Soto et al. (+4 more)

Active networks composed of biopolymers and motor proteins provide versatile biomimetic systems that have advanced active matter physics and deepened our understanding of cytoskeletal dynamics and self-organization under diverse stimuli. In these systems, activity arises in aqueous solutions where motor proteins cross-link biopolymers and generate active stress driving the emergent network behavior. Here, we establish the active network in the form of a sessile, multi-component droplet on a substrate and investigate how evaporation influences its dynamics. We focus on how mass loss and compositional changes in the droplet reshape the behavior of the active suspension. We show that capillary and Marangoni flows drive the self-organization of microtubules into a distinctive radial arrangement within the droplet. The cross-linking ability of motor proteins gives rise to a striking non-monotonic wetting behavior, where the extensile stresses generated by the motor proteins strongly affect the characteristic timescale of the contact-line retracting and subsequent expansion. Using a combined experimental and theoretical approach, we demonstrate the crucial role of crosslinking in evaporating microtubule networks, and explain how active stresses together with evaporation-induced flows govern the dynamics of reconstituted microtubule systems and their wetting behavior. Evaporating droplets have recently attracted significant attention in the scientific community, and the findings of the setup presented in this study can have broad implications, ranging from self-organization and mechanical pattern formation in biological systems to questions about the origin of life.
Read the full abstract

All authors Vahid Nasirimarekani, Mehrana R. Nejad, Olinka Ramírez-Soto, Susan Ali, Stefan Karpitschka, L. Mahadevan, Isabella Guido

Materials Science

Ligand Mediated Magnetic Coupling Across Metamagnetic Transitions in CrPS4

Giuseppe Buccoliero, Rachel Nickel, Roberto Sant et al. (+8 more)

Chromium thiophosphate (CrPS4) is a long-known material: a layered semiconducting antiferromagnet. Its recently discovered gate-tunable metamagnetic phase transitions, the remarkable positive and oscillating magnetoresistance as a tunnel barrier, and its Fano-resonance luminescence, elusive among the multitude of Cr3+ compounds, call for revisiting the understanding of its electronic structure, especially regarding how it relates to magnetic order. Here, we employ X-ray magnetic circular dichroism, implemented in both absorption and resonant inelastic X-ray spectroscopies, together with quantum many-body calculations, to unveil the role of metal-ligand covalency in mediating the metamagnetic transitions in CrPS4, using crystal-field and charge-transfer excitations as fingerprints of the evolving magnetic order. We reveal the role of extended superexchange paths involving P and S atoms, coupling interactions between the Cr spins across the different magnetic phases: antiferromagnetic, canted, and ferromagnetic. Our results elucidate the electronic states involved in these phases and provide prescriptions for engineering the metamagnetic phase diagram of CrPS4.
Read the full abstract

All authors Giuseppe Buccoliero, Rachel Nickel, Roberto Sant, Marli dos Reis Cantarino, Andrei Rogalev, Nathan J. Yutronkie, Tristan Riccardi, Daniel A. Chaney, Kurt Kummer, Johann Coraux, Nicholas B. Brookes

Soft Condensed Matter

Non-local physics-informed neural networks for forward and inverse solutions of granular flows

Saghar Zolfaghari and Safa Jamali

Dense granular flows exhibit nonlocal effects due to stress transmission in microplastic events, especially in quasi-static or slowly sheared regions. Hence, traditional local rheological models fail to capture spatial cooperativity effects that are prominent in many granular systems. The nonlocal granular fluidity (NGF) model addresses this limitation by introducing a diffusive-like partial differential equation for a fluidity field, governed by a key material-dependent parameter: the nonlocal amplitude A. However, determining A from experiments or simulations is known to be difficult and typically requires extensive calibration across multiple geometries. In this work, we present a data-driven platform based on Physics-Informed Neural Networks (PINNs) embedded with the NGF model, capable of solving granular flows in a forward or inverse manner. We show that once trained on transient flow fields, these non-local PINNs can readily infer the material parameters, as well as the pressure and stress fields. These data-driven frameworks allow for accurate recovery of small variations in the nonlocal amplitude, A, which lead to sharp bifurcation-like transitions in the flow field. This approach demonstrates the feasibility of data-driven parameter inference in complex nonlocal models and opens up new possibilities for characterizing granular materials from sparse experimental observations.
Read the full abstract

Soft Condensed Matter

Nonreciprocal interactions drive intermittent melting of Coulomb clusters

Zhicheng Shu, Wei-Chih Li, Wentao Yu et al. (+1 more)

Complex systems out of equilibrium often experience intermittent oscillations between quiescent and highly dynamic states. While intermittency is usually driven by stochastic noise or external forcing, energy can also be sourced from field-mediated interactions between particles, which are often nonreciprocal and effectively violate Newton's 3rd law. Here we demonstrate how nonreciprocal interactions produce intermittency in clusters of charged micron-sized particles confined in a plasma sheath. Using three-dimensional particle tracking, we observe that vertical oscillations, induced by a noisy plasma environment, are parametrically coupled to the horizontal vibrational modes. Experiments and simulations show that nonreciprocal interactions strongly amplify this coupling, injecting energy into the system. This mechanism triggers explosive melting transitions from an ordered cluster to an ergodic gas-like state, and leads to intermittent switching between states over long time. Overall, our work identifies nonreciprocal interactions as a key mechanism through which strongly-coupled finite systems transform interaction-mediated activity into dynamical nonequilibrium states.
Read the full abstract

All authors Zhicheng Shu, Wei-Chih Li, Wentao Yu, Justin C. Burton

Statistical Mechanics

Super-Arrhenius temperature dependent viscosity due to liquid-liquid phase separation in the super-cooled Kob-Andersen model

Jayme Brickley and Xueyu Song

In this study, a recently introduced order parameter called the weighted coordination number (WCN) was used to investigate the liquid-liquid (LL) phase separation, indicating temperature-dependent coarsening of the LL interface as a possible mechanism for the glass transition. A well-established glass-forming Kob-Andersen binary Lennard-Jones system was used in this study. The gas-liquid binodal line was reconstructed using WCNs, and the same approach was extended to study the liquid-liquid binodal line. Systems of various densities are instantaneously quenched from high to low temperatures where liquid-liquid separation is observed. The densities and composition of each liquid state were used to verify the level rule, along with the density and pressure profiles, demonstrating the local equilibrium of liquid-liquid phase separation. The transition from the liquid-liquid phase separation in the supercooled region to the glass transition region was modeled by adopting a Markov Network Model to estimate the temperature-dependent viscosity using liquid-liquid interfacial information from the classification.
Read the full abstract

Materials Science

Twist-induced Out-of-plane Ferroelectricity in Bilayer Hafnia

Jian Huang, Gwan Yeong Jung, Pravan Omprakash et al. (+6 more)

Ferroelectric HfO2 is a promising candidate for next-generation memory devices due to its CMOS compatibility and ability to retain polarization at nanometer scales. However, the polar orthorhombic phase (Pca2_1) responsible for ferroelectricity is metastable and requires extrinsic stabilization, which makes it challenging for integration with silicon. We predict that bilayer 1T-HfO2 can exhibit robust and switchable out-of-plane (OOP) polarization arising from stacking-induced symmetry breaking. Using first-principles density functional theory, we predict that monolayer 1T-HfO2 can be cleaved from the (111) surface of cubic hafnia, and the monolayer is dynamically stable. When two aligned monolayers are twisted to form a moiré superlattice, it breaks the interlayer symmetry and allows the emergence of bistable OOP polarization. At a twist angle of 7.34o, the system exhibits a net polarization of ~16 {\mu}C/cm2. This sizeable polarization is due to the large polar displacements concentrated in AB stacking domains. Importantly, this polarization can be reversibly switched via interlayer sliding with a low energy barrier (~8 meV/formula unit) and comparable low coercive field (~0.2 V/nm), offering electric-field tunability. These findings establish twisted bilayer 1T-HfO2 as a scalable and robust 2D ferroelectric platform, enabling new pathways for integrating ferroelectric functionality into atomically thin memory and logic devices.
Read the full abstract

All authors Jian Huang, Gwan Yeong Jung, Pravan Omprakash, Guodong Ren, Xin Li, Du Li, Xiaoshan Xu, Li Yang, Rohan Mishra

Statistical Mechanics · Cross-listed from quant-ph

Strong-to-Weak Symmetry Breaking in Open Quantum Systems: From Discrete Particles to Continuum Hydrodynamics

Jacob Hauser, Kaixiang Su, Hyunsoo Ha et al. (+6 more)

We explore the onset of spontaneous strong-to-weak symmetry breaking (SW-SSB) under U(1)-symmetric (i.e., charge-conserving) open-system dynamics. We define this phenomenon for quantum states and classical probability distributions, and explore it in three complementary models, one of which exhibits nontrivial quantum coherence at short times. Our main conclusions are as follows. In one dimension, the strong symmetry is not spontaneously broken at any finite time; however, correlators probing strong-to-weak symmetry breaking develop order on length scales that grow linearly in time, parametrically faster than charge diffusion. We provide numerical evidence for this scaling in multiple distinct probes of SW-SSB, and derive it from a field-theory analysis. Moreover, we relate this scaling to the problem of inferring the charge inside a subregion by measuring its surroundings, and construct explicit decoding protocols that illustrate its origin. In two dimensions, field theory and numerical simulations support a finite-time Berezinskii-Kosterlitz-Thouless-like SW-SSB transition. Within continuum hydrodynamics, by contrast, SW-SSB happens at infinitesimal time in two or more dimensions. The SW-SSB transition time can thus be interpreted as marking the emergence of a continuum hydrodynamic description, or (more precisely) the timescale beyond which non-hydrodynamic information such as discrete particle worldlines can no longer be inferred. We support this picture by analyzing a model in which we exploit SW-SSB to derive a classical stochastic hydrodynamic description from the underlying quantum dynamics.
Read the full abstract

All authors Jacob Hauser, Kaixiang Su, Hyunsoo Ha, Jerome Lloyd, Thomas G. Kiely, Romain Vasseur, Sarang Gopalakrishnan, Cenke Xu, Matthew P. A. Fisher

Materials Science · Cross-listed from cs.LG

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching

Cheng Zeng, Harry W. Sullivan, Thomas Egg et al. (+8 more)

Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids, and metal-organic frameworks, extending such approaches to fully periodic molecular crystals is still elusive. Here, we present MolCrystalFlow, a flow-based generative model for molecular crystal structure prediction. The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies and jointly learning the lattice matrix, molecular orientations, and centroid positions. Centroids and orientations are represented on their native Riemannian manifolds, allowing geodesic flow construction and graph neural network operations that respects geometric symmetries. We benchmark our model against a state-of-the-art generative model (MOFFlow) for large-size periodic crystals and a rule-based structure generation method (Genarris) on two open-source molecular crystal datasets. MolCrystalFlow outperforms MOFFlow while achieving competitive performance against Genarris. We also demonstrate an integration of MolCrystalFlow model with universal machine learning potential to accelerate molecular crystal structure prediction, paving the way for data-driven generative discovery of molecular crystals.
Read the full abstract

All authors Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu

Strongly Correlated Electrons

Zero Indirect Band Gap and Flat Bands in a Niobium Oxyiodide Cluster Material

Jan Beitlberger, Mario Martin, Marcus Scheele et al. (+4 more)

Explorative chemistry in a reaction system composed of NbI$_4$, Li$_2$(CN$_2$), and Li$_2$O has led to the discovery of a number of niobium oxyiodide cluster compounds. During this reaction, the formation of solid phases was detected alongside with gaseous phases, resulting in a range of products with cluster cores of varying shapes. After several niobium oxyiodide cluster compounds have already been identified within this reaction system, two additional compounds, Nb$_6$O$_3$I$_{15}$ and Nb$_{11}$O$_6$I$_{24}$, are discovered and structurally characterized by single-crystal X-ray diffraction. Both structures are based on the butterfly-shaped, oxygen-capped niobium cluster [Nb$_4$O], which is extended to larger cluster fragments. The [Nb$_4$O] cluster core in Nb$_6$O$_3$I$_{15}$is extended by two [NbO] units to form a three-dimensional framework, and Nb$_{11}$O$_6$I$_{24}$ contains two connected [Nb$_4$O] units, which form chiral units within an antiferrochiral hexagonal packing of strings. The striking string-like character of Nb$_{11}$O$_6$I$_{24}$ was investigated in terms of its electronic structure and properties. DFT calculations showed Nb$_{11}$O$_6$I$_{24}$ to possess a zero indirect band gap, with a pair of 3-dimensional flat bands surrounding the Fermi level. These unusual features of the electronic band structure suggest the presence of strongly correlated inter-cluster singlet electron states, arising from the helical shape of the clusters, the hexagonal packing of the strings, and the delocalized nature of cluster electron wavefunctions.
Read the full abstract

All authors Jan Beitlberger, Mario Martin, Marcus Scheele, Marek Matas, Carl P. Romao, Markus Ströbele, H.-Jürgen Meyer

Materials Science

Stochastic Modeling of Anisotropic Strength Surfaces from Atomistic Simulations

Alexander Bonacci, John Dolbow and Johann Guilleminot

This work develops a unified framework for inferring, representing, and statistically characterizing an anisotropic strength surface directly from molecular dynamics data. Large-scale tensile loading simulations are used to generate failure data across all principal stress ratios and loading orientations, facilitated by a data-driven mapping between imposed strain-rate tensors and resulting stresses. The orientation-dependent strength surface is then represented using a constrained parametric formulation in which the surface parameters vary smoothly with loading angle through a low-dimensional functional encoding. To deploy the framework, we specifically consider the case of monocrystalline graphene, which is a prototypical two-dimensional material that has been extensively characterized, both experimentally and computationally, in the literature. For defective graphene, multiple random realizations of vacancy defect distributions are used to construct a stochastic ensemble of angular strength surfaces. Because each anisotropic strength surface requires substantial atomistic sampling to construct, the resulting ensemble is inherently limited in size, motivating the use of compact encoding, dimensionality reduction, and probabilistic modeling to characterize strength variability. Dimensionality reduction via Principal Component Analysis reveals a condensed latent representation of the fitted, encoded surfaces, where a Gaussian mixture model is employed to capture defect-induced variability, including rare outlier behaviors arising from clustered vacancy defects. Sampling from this probabilistic model enables the generation of new, physically admissible strength surfaces and the construction of confidence intervals in both parameter space and stress space. (Abstract shortened to meet arXiv limits.)
Read the full abstract

Superconductivity

Uniaxial stress enhanced anisotropic magnetoresistance and superconductivity in the kagome superconductor LaRu$_{3}$Si$_{2}$

P. Král, V. Sazgari, Yongheng Ge et al. (+10 more)

Elucidating the role of the kagome electronic structure in determining the various quantum ground states is of fundamental importance. In this work, we employ in-plane uniaxial stress as a tuning parameter to probe the electronic structure and its impact on the superconducting and normal-state properties of the kagome superconductor LaRu$_{3}$Si$_{2}$, combining magnetotransport measurements with first-principles calculations. We identify a pronounced anisotropy in both the upper critical field and the normal-state magnetoresistance, indicating strong electronic anisotropy despite the three-dimensional crystal structure. Furthermore, we find that the superconducting transition temperature $T_{\rm c}$ increases under in-plane stress applied within the kagome plane, although the enhancement is modest, reaching approximately 0.3 K at 0.6 GPa. Furthermore, the absolute magnetoresistance exhibits a pronounced increase from about 22${\%}$ at zero stress to 35${\%}$ at 0.6 GPa, indicating a substantial modification of the normal state above $T_{\rm c}$. Previous studies have reported time-reversal-symmetry (TRS) breaking below a temperature scale that coincides with the onset of magnetoresistance. The simultaneous enhancement of both $T_{\rm c}$ and magnetoresistance under stress therefore suggests a positive correlation between superconductivity and normal-state electronic and magnetic properties in LaRu$_{3}$Si$_{2}$. Detailed calculations demonstrate that stress-induced changes in $T_{\rm c}$ arise from the joint evolution of the total density of states and the flat band, whereas the large magnetoresistance enhancement is dominated by the stress-driven downward shift of the Ru $dz^{2}$ kagome flat band.
Read the full abstract

All authors P. Král, V. Sazgari, Yongheng Ge, O. Gerguri, M. Spitaler, J.N. Graham, H. Nakamura, M. Bartkowiak, S. Nakatsuji, H. Luetkens, G. Simutis, Gang Xu, Z. Guguchia

Statistical Mechanics

Breaking of clustering and macroscopic coherence under the lens of asymmetry measures

Florent Ferro

In one-dimensional systems, spontaneous symmetry breaking (SSB) states are fragile by nature, as the injection of a non-zero energy density above the ground state is expected to restore the symmetry. This instability implies that local perturbations can lead to macroscopic correlation profiles, a breaking of clustering properties and even macroscopic quantum superpositions. In this work, we investigate the effect of interaction on this phenomenology by considering an interacting model with conserved domain wall number, that possesses a ferromagnetic ground state breaking the Z2 symmetry of the Hamiltonian. We first show that a local quench in this system amplifies quantum interferences, producing a macroscopic magnetisation profile that directly reflects the scattering phase of the model. Then, we use two asymmetry measures, namely the Entanglement Asymmetry (EA) and Quantum Fisher Information (QFI), to characterise the quantum coherence associated with the fluctuations of the magnetisation. By focusing on subsystems comparable in size to the light-cone of the perturbation, we confirm the emergence of macroscopic quantum coherence throughout the whole perturbed region. Finally, we discuss the link between EA and QFI and show that the variance/EA inequality for pure state can be generalised to a QFI/EA inequality for mixed states.
Read the full abstract

Disordered Systems and Neural Networks · Cross-listed from quant-ph

Robustness of Kardar-Parisi-Zhang-like transport in long-range interacting quantum spin chains

Sajant Anand, Jack Kemp, Julia Wei et al. (+3 more)

Isotropic integrable spin chains such as the Heisenberg model feature superdiffusive spin transport belonging to an as-yet-unidentified dynamical universality class closely related to that of Kardar, Parisi, and Zhang (KPZ). To determine whether these results extend to more generic one-dimensional models, particularly those realizable in quantum simulators, we investigate spin and energy transport in non-integrable, long-range Heisenberg models using state-of-the-art tensor network methods. Despite the lack of integrability and the asymptotic expectation of diffusion, for power-law models (with exponent $2 < \alpha < \infty$) we observe long-lived $z=3/2$ superdiffusive spin transport and two-point correlators consistent with KPZ scaling functions, up to times $t \sim 10^3/J$. We conjecture that this KPZ-like transport is due to the proximity of such power-law-interacting models to the integrable family of Inozemtsev models, which we show to also exhibit KPZ-like spin transport across all interaction ranges. Finally, we consider anisotropic spin models naturally realized in Rydberg atom arrays and ultracold polar molecules, demonstrating that a wide range of long-lived, non-diffusive transport can be observed in experimental settings.
Read the full abstract

All authors Sajant Anand, Jack Kemp, Julia Wei, Christopher David White, Michael P. Zaletel, Norman Y. Yao

Materials Science

A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems

Rakshit Kumar Singh, Aryan Amit Barsainyan and Bharath Ramsundar

Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based exchange-correlation (XC) functionals, this paper introduces a differentiable framework that integrates machine learning models into density functional theory (DFT) for solids and other periodic systems. The framework defines a clean API for neural network models that can act as drop in replacements for conventional exchange-correlation (XC) functionals and enables gradients to flow through the full self-consistent DFT workflow. The framework is implemented in Python using a PyTorch backend, making it fully differentiable and easy to use with standard deep learning tools. We integrate the implementation with the DeepChem library to promote the reuse of established models and to lower the barrier for experimentation. In initial benchmarks against established electronic structure packages (GPAW and PySCF), our models achieve relative errors on the order of 5-10%.
Read the full abstract