Materials Science · Cross-listed from physics.comp-ph
A complete phase-field fracture model for brittle materials subjected to thermal shocks
Bo Zeng and John E. Dolbow
Brittle materials subjected to thermal shocks experience strong temperature gradients that in turn give rise to mechanical stresses that can be large enough to induce fracture. This work presents a complete model for phase-field fracture for coupled thermo-mechanical problems, wherein the bulk material properties, the material strength, and the fracture toughness are specified independently. The capabilities of the model are assessed across a wide span of scenarios in thermo-mechanical fracture, from the propagation of large pre-existing cracks to crack nucleation under spatially uniform states of stress. In particular, we revisit the controlled quenching of glass plates, and demonstrate how the model captures experimentally observed crack patterns across a range of thermal loads. Ceramic disks subjected to infrared radiation are also examined, with the model reproducing both straight cracks in notched specimens and branching in intact specimens. Finally, ceramic pellets subjected to rapid power pulses are examined, with the model explaining experimental transitions from intact to fractured pellets and the important role of material strength. The results demonstrate that the complete phase-field model unifies the treatment of distinct fracture scenarios under thermal shock, surpassing classical approaches and enabling more reliable prediction of brittle fracture in extreme environments.
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Materials Science · Cross-listed from cs.NE
MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic
Masahi Okada, Kazuki Sakai, Hiroaki Yoshida et al. (+2 more)
We study sample-efficient molecular optimization under a limited budget of oracle evaluations. We propose MolLIBRA (MultimOdaLity and
Language Integrated
Bayesian and evolutionaRy optimizAtion), a genetic
algorithm based framework that pre-ranks candidate molecules using multiple critics before oracle calls: (i) an ensemble of Gaussian process (GP) surrogates defined over multiple molecular fingerprints and (ii) a pretrained text-molecule aligned encoder CLAMP. The GP ensemble enables adaptive selection of task-appropriate fingerprints, while CLAMP provides a zero-shot scoring signal from task descriptions by measuring the similarity between molecular and text
embeddings. On the Practical Molecular Optimization (PMO)
benchmark with a budget of 1,000 evaluations (PMO-1K), MolLIBRA-L, our variant with a
language-model-based candidate generator, attains the best Top-10 AUC on 14/22 tasks and the highest overall sum of Top-10 AUC across tasks among prior methods.
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All authors Masahi Okada, Kazuki Sakai, Hiroaki Yoshida, Masaki Okoshi, Tadahiro Taniguchi
Materials Science
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang et al. (+18 more)
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned
agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy
benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural
language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy,
memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM
agents aimed at discovering novel and useful materials.
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All authors Huan Zhang, Yizhan Li, Wenhao Huang, Ziyu Hou, Yu Song, Xuye Liu, Farshid Effaty, Jinya Jiang, Sifan Wu, Qianggang Ding, Izumi Takahara, Leonard R. MacGillivray, Teruyasu Mizoguchi, Tianshu Yu, Lizi Liao, Yuyu Luo, Yu Rong, Jia Li, Ying Diao, Heng Ji, Bang Liu
Soft Condensed Matter
Plasticity, hysteresis, and recovery mechanisms in spider silk fibers
Renata Olivé, José Pérez-Riguero and Noy Cohen
Spider silk is a remarkable biomaterial with exceptional stiffness, strength, and toughness stemming from a unique microstructure. While recent studies show that silk fibers exhibit plasticity, hysteresis, and recovery under cyclic loading, the underlying microstructural mechanisms are not yet fully understood. In this work, we propose a mechanism explaining the loading-unloading-relaxation response through microstructural
evolution: initial loading distorts intermolecular bonds, resulting in a linear elastic regime. Upon reaching the yield stress, these bonds dissociate and the external load is transferred to the polypeptide chains, which deform entropically to allow large deformations. Unloading is driven by entropic shortening until a traction free state with residual stretch is achieved. Subsequently, the fiber recovers as chains reorganize and bonds reform, locking the microstructure into a new stable
equilibrium that increases stiffness in subsequent cycles. Following these mechanisms, we develop a microscopically motivated, energy-based model that captures the macroscopic response of silk fibers under cyclic loading. The response is decoupled into two parallel networks: (1) an elasto-plastic network of inter- and intramolecular bonds governing the initial stiffness and yield stress, and (2) an elastic network of entropic chains that enable large deformations. The model is validated against experimental data from Argiope bruennichi dragline silk. The findings from this work are three-fold: (1) explaining the mechanisms that govern hysteresis and recovery and linking them to microstructural
evolution; (2) quantifying the recovery process of the fiber, which restores and enhances mechanical properties; and (3) establishing a predictive foundation for engineering synthetic fibers with customized properties.
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Materials Science · Cross-listed from physics.bio-ph
Effects of PLGA coating on biological and mechanical behaviors of tissue engineering scaffolds
A.M. Maadani, F. Davoodian and E. Salahinejad
Scaffolds have a key role in the clinical success of tissue engineering for the regeneration of damaged tissues. Their bio-performance is often described as the extent to which they can provide an extracellular
matrix-like environment for
cells embedded where their function and growth can effectively continue. For this purpose, tissue engineering scaffolds should exhibit biodegradability, biocompatibility, bioactivity, delivery, and mechanical performance. The use of
polymer coatings, especially poly(lactic-co-glycolic acid) (PLGA), on tissue engineering scaffolds has been found to be one of the most effective methods to improve the scaffold properties. This paper reviews the techniques used to coat tissue engineering scaffolds with PLGA and its effects on the mechanical characteristics, biodegradability, biocompatibility, Molecular delivery, and osteointegration of the scaffolds. It is concluded that apart from apatite-formation ability, all bio-functionalities can be tuned through PLGA coatings. This reflects the great potential of this modification approach to be used in tissue regeneration and therapeutic delivery applications.
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Materials Science
Nanoscale mapping of phase-transformation pathways in medium-Mn TRIP steel by multimodal STEM
Marc Raventós-Tato, S. Leila Panahi, Núria Bagués et al. (+4 more)
The mechanical response of third-generation advanced high-strength steels is governed by phase transformations at the nanoscale, yet the coupled
evolution of chemistry and crystallography remains poorly resolved. Here we apply a correlative scanning transmission electron microscopy approach that enables simultaneous mapping of
lattice structure, crystallographic orientation, and phase distribution at 10 nanometre resolution in a medium-manganese TRIP steel. We combine nano-beam electron diffraction and energy-dispersive X-ray spectroscopy maps to characterize an industrial medium-manganese steel containing 7.15 weight percent Mn. Tensile testing of a rolled steel sample was performed, and lamellae were extracted from deformed and undeformed regions. Manganese-resolved energy-dispersive X-ray spectroscopy provides a chemical fingerprint that, when combined with nano-beam electron diffraction based phase
segmentation, enables robust ferrite-martensite separation and phase-resolved
lattice-parameter refinement. The phase fractions of ferrite, austenite, and martensite are quantified together with their corresponding
lattice parameters, accompanied by measurable shifts in grain-size distributions and crystallographic texture in the deformed regions. Kernel average misorientation maps reveal systematically lower local misorientation in ferrite than in martensite. This
multimodal workflow provides a transferable framework for quantitative, phase-resolved analysis of complex multiphase
alloys at the nanoscale.
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All authors Marc Raventós-Tato, S. Leila Panahi, Núria Bagués, David Frómeta, Oleg Usoltsev, Núria Cuadrado, Joaquín Otón
Statistical Mechanics · Cross-listed from physics.soc-ph
Convergent Discovery of Critical Phenomena Mathematics Across Disciplines
Bruce Stephenson and Robin Macomber
Techniques for detecting critical phenomena – phase transitions where correlation length diverges and small perturbations have large effects – have been developed across multiple fields over nine decades. We survey between six and twelve disciplines (depending on classification criteria) where researchers derived functionally corresponding measures of correlation scaling, with little documented awareness of each other's work. The physicist's correlation length $\xi$, the cardiologist's DFA scaling exponent $\alpha$, the financial analyst's Hurst exponent $H$, and the machine learning engineer's spectral radius $\chi$ all detect critical signatures under different notation. We classify each surveyed domain as independent derivation, domain transfer, or empirical precursor, and present citation network evidence that cross-domain citations remained significantly below random-mixing expectations (under a simple null model of domain mixing) during the formative period (1987–2010). The framework that motivated this investigation – derived from distributed systems engineering and presented in the Supplementary Material – is excluded from the survey count given the authors' dual role. Building on prior syntheses – notably Sornette's 2004 textbook – this paper contributes a taxonomy of discovery types and quantitative documentation of the convergence pattern.
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Statistical Mechanics
Bruno Bertini
These are the notes for the 4.5-hour course with the same title that I delivered in August 2025 at the Les Houches summer school “Exact Solvability and
Quantum Information”. In these notes I pedagogically introduce the setting of brickwork
quantum circuits and show that it provides a useful framework to study non-
equilibrium quantum many-body dynamics in the presence of local interactions. I first show that brickwork
quantum circuits evolve
quantum correlations in a way that is fundamentally similar to local
Hamiltonians, and then present examples of brickwork
quantum circuits where, surprisingly, one can compute exactly several relevant dynamical and spectral properties in the presence of non-trivial interactions.
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Strongly Correlated Electrons · Cross-listed from quant-ph
Quantum bootstrap product codes
Meng-Yuan Li
Product constructions constitute a powerful method for generating
quantum CSS codes, yielding celebrated examples such as toric codes and asymptotically good low-density parity check (LDPC) codes. Since a CSS code is fully described by a chain complex, existing product formalisms are predominantly homological, defined via the tensor product of the underlying chain complexes of input codes, thereby establishing a natural connection between
quantum codes and
topology. In this Letter, we introduce the
quantum bootstrap product (QBP), an approach that extends beyond this standard homological paradigm. Specifically, a QBP code is determined by solving a consistency condition termed the “bootstrap equation”. We find that the QBP paradigm unifies a wide range of important codes, including general hypergraph product (HGP) codes of arbitrary dimensions and fracton codes typically represented by the X-cube code. Crucially, the solutions to the bootstrap equation yield chain complexes where the chain groups and associated boundary maps consist of multiple components. We term such structures fork complexes. This structure elucidates the underlying topological structures of fracton codes, akin to foliated fracton order theories. Beyond conceptual insights, we demonstrate that the QBP paradigm can generate self-correcting
quantum codes from input codes with constant energy barriers and surpass the code-rate upper bounds inherent to HGP codes. Our work thus substantially extends the scope of
quantum product codes and provides a versatile framework for designing fault-tolerant
quantum memories.
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Materials Science
Hydrogen in Brownmillerite Perovskites: First-Principles Insights into Energetics and Induced Electronic-Magnetic Changes
Vladislav Korostelev, Pjotrs Žguns and Konstantin Klyukin
Hydrogen uptake in brownmillerite perovskites A2B2O5 offers an (electro)chemically accessible route to tune functional properties, but mechanistic understanding and design rules for hydrogen-responsive
oxides remain limited. Here we employ density functional theory (DFT) to quantify how H absorption affects electronic structure,
magnetic exchange, and anisotropy in representative Sr2Fe2O5 and Sr2Co2O5
oxides. We find that hydrogenation introduces a localized electron that stabilizes near the
proton, with B-site-dependent preference. The resulting
lattice distortions and redistribution of charge density modify exchange coupling and cant the Neel vector, giving rise to weak ferromagnetism. We also show that absorption energies are highly sensitive to
proton-electron arrangements and
magnetic order, varying by up to 1 eV across different settings. This sensitivity demands consistent treatment of charge localization and
spin states, together with careful choice of computational parameters. Extending to a variety of experimentally reported A2B2O5 compositions, we identify candidates with favorable H uptake and uncover a trend linking more favorable absorption to a higher B-site d-electron count. We also demonstrate that the preferred
proton absorption site in these materials is governed by local O-O separations and
lattice flexibility, which describe the ability of the framework to accommodate
proton-induced distortions. Finally,
benchmarks of universal machine-learning interatomic potentials reveal uncertainties of about 1 eV for site-resolved absorption energies, motivating descriptor-based surrogate models and targeted DFT validation. Together, these results establish practical design rules for hydrogen-responsive
oxides relevant to iono-electronic devices, sensors, and electrically tunable
spin functionality.
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Strongly Correlated Electrons
Field-induced transitions from incommensurate to commensurate phases in helical antiferromagnets
P. T. Bolokhova and A. V. Syromyatnikov
Heisenberg antiferromagnet with an easy-plane anisotropy is discussed in which a
magnetic spiral is induced by Dzyaloshinskii-Moriya interaction and/or frustration of the exchange coupling. The distortion of the spiral by small in-plane
magnetic field is described analytically. It is found that the field can gradually change the vector of the
magnetic structure ${\bf k}_0$ and can produce transitions between phases with incommensurate and commensurate
magnetic orderings when ${\bf k}_0$ is close to ${\bf g}/n$, where ${\bf g}$ is a reciprocal
lattice vector and $n$ is integer. Analytical expressions for critical fields are derived for $n=2$, 3, and 4. Application of the theory to the triangular-
lattice compound $\rm RbFe(MoO_4)_2$ is discussed alongside its potential applicability to other materials. As a by-product of the main consideration, model parameters are found which describe more accurately the full set of available experimental data suggested before for $\rm RbFe(MoO_4)_2$.
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Materials Science
Chiral and Clock phases in Twisted Dipolar Clusters
Paula Mellado, Xavier Cazor and Andres Concha
We study samples and a dipolar model of
magnetic rods arranged on twisted polygonal clusters in terms of the twist angle. We find that the relative twist between polygons induces noncollinear
chiral phases, ranging from flux
vortex closure to hedgehog like radial configurations. Chirality, quantified in terms of a bond order parameter, is an emergent property that behaves here as an Ising variable. The
chiral configurations of the systems can be understood in terms of chirality and clock index order parameters, whose
evolution with twist occurs through discontinuous switching of the
magnetic textures. Within a fixed Ising
chiral sector, the clock index, rooted in the $C_N$ invariance of the polygons, distinguishes
chiral textures that share chirality. As the twist increases, it continuously shifts the preferred relative clock phase, but the N-fold anisotropy only allows discrete orientations; the competition produces a tilted N-fold energy landscape whose global minimum hops discontinuously between clock sectors. As the number of sites in the polygon grows, the resulting response displays a nonlinear crossover from rigid, Ising-like behavior to an almost $\rm U(1)$-invariant regime, governed by a twist-induced suppression of the emergent $Z_N$ clock anisotropy. Guided by
symmetry considerations and the outcomes of the numerical minimization, we developed a Landau phenomenological description that is compatible with both the Ising-type chirality and the $Z_N$ clock anisotropy.
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Materials Science · Cross-listed from cs.LG
SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
Can Polat, Erchin Serpedin, Mustafa Kurban et al. (+1 more)
Large
language models are increasingly applied to materials science
reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We introduce SCALAR (Structural Consistency And Logic Across Regimes), a
benchmark for evaluating
geometric scale generalization and its connection to structural
hallucination, consistency, and
reasoning in materials foundation models. Given canonical
crystal representations, models must reason about derived nanoparticle structures obtained through supercell expansion and
geometric truncation across length scales spanning a few atoms to over 18,000 atoms, totaling $\approx$100,000 structures from DFT-validated unit
cells. SCALAR defines three tasks. (i) CIF to property prediction. (ii) A Chain-of-Thought variant with explicit physics-grounded
reasoning. (iii) Inverse
retrieval identifying
crystals from candidates given target properties. Outputs are evaluated via structured metrics capturing numeric error,
hallucination, cross-
prompt consistency, monotonic
reasoning, output validity, and
retrieval regret. Experiments across diverse foundation models reveal large, model-dependent shifts under explicit
reasoning, often reducing
hallucination and error, but frequently destabilizing consistency or validity. These results demonstrate that
geometric scale generalization cannot be inferred from accuracy alone. Supplementary materials are available at https://github.com/KurbanIntelligenceLab/SCALAR.
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All authors Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Mesoscale and Nanoscale Physics
Moiré Folded Helical States at the Interfaces of Heterostructures
Paula Mellado
A minimal model of a
graphene–topological-
insulator heterostructure is considered, where a moiré superlattice modulates the Rashba
spin-orbit interaction (SOC). In the
spin-degenerate,
spin-orbit–free limit, the reduced Brillouin zone contains flat,
spin-degenerate moiré minibands, with periodicity determined by superlattice folding. The inclusion of SOC lifts the
spin degeneracy and reduces the effective spectral periodicity by a factor of two. Through SOC, the moiré potential entangles
spin, sublattice, and leg degrees of freedom, reshaping the miniband structure in momentum space and generating emergent helicity spectral functions. As the Rashba coupling is renormalized by the moiré pattern, it induces helicity fragmentation, in which the helicity weight is distributed across a dense
manifold of moiré minibands, forming an extended network of helicity-carrying states and significantly enhancing helicity fluctuations at the bare-response level. The emergence of Dirac-like miniband crossings at finite SOC demonstrates that moiré
heterostructures can support relativistic quasiparticles through band reconstruction. This model provides a microscopic mechanism by which proximity-induced
spin-orbit coupling can be amplified via moiré engineering.
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Disordered Systems and Neural Networks · Cross-listed from physics.optics
Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks
Xi Li, Disha Biswas, Peng Zhou et al. (+4 more)
We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback for online, unsupervised Hebbian learning. The proposed architecture combines optical vector-
matrix multiplication, non-volatile PCM synaptic weighting, and local coincidence-driven synaptic adaptation within a multilayer photonic crossbar framework compatible with photonic integrated
circuits. Unlike conventional PNNs that rely on externally computed gradients, repeated optical-electrical-optical conversions, or global backpropagation, the proposed framework employs local Hebbian learning governed directly by correlated pre- and post-synaptic optical activity. To investigate the feasibility of the proposed learning mechanism, we implemented the PNN design using fiber-optic components, programmable variable optical attenuators, and real-time software control that incorporates PCM thermal dynamics. Supervised and unsupervised learning behaviors were experimentally evaluated under both offline and online learning conditions using representative image-recognition tasks. The experimental results demonstrate adaptive synaptic
evolution, successful optical inference, and autonomous pattern encoding through local Hebbian learning under realistic fiber-optic hardware conditions. These results establish a pathway toward future integrated photonic neuromorphic systems capable of scalable and energy-efficient online Hebbian learning.
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All authors Xi Li, Disha Biswas, Peng Zhou, Wesley H. Brigner, Anna Capuano, Joseph S. Friedman, Qing Gu
Strongly Correlated Electrons
Anisotropic Kitaev Spin Glass in Li$_{2}$Ru$_{x}$Ir$_{1-x}$O$_{3}$
Mayia A. Vranas, Alejandro Ruiz, Vikram Nagarajan et al. (+8 more)
Kitaev iridates have emerged as an important class of
spin-orbit-entangled
quantum materials in which bond-directional exchange interactions generate strong
magnetic frustration and unconventional correlated states. Rather than realizing simple ordered magnets, members of the alpha,beta,gamma-Li2IrO3 family exhibit fragile and highly anisotropic
magnetic behavior, including incommensurate counter-rotating order, strong field sensitivity, and pressure-driven electronic reconstruction. These phenomena place the iridates in close proximity to the Kitaev
quantum spin liquid (QSL) regime, while simultaneously revealing the importance of competing interactions,
lattice distortions, and
spin-orbit-assisted hopping processes beyond the ideal Kitaev limit. Understanding how chemical substitution perturbs these competing interactions provides a route toward probing the underlying frustrated
magnetic state. Here, we study single
crystals of beta-Li2RuxIr1-xO3 with dilute Ru substitution, x<~10%. Through a combination of magnetometry, resonant elastic X-ray scattering, ac-heat capacity, and
muon spin relaxation/rotation, we show that weak
magnetic disorder continuously suppresses the incommensurate
antiferromagnetic ground state without stabilizing an alternative long-range ordered phase. Instead, the system evolves into a bulk static
spin glass characterized by slow relaxation, aging behavior, and frozen local
magnetic fields. Despite the loss of long-range
magnetic order, the glassy state retains substantial directional anisotropy without significant distortion to the honeycomb
lattice, suggesting that disorder freezes
spins within a still-active bond-directional exchange environment. In this picture, dilute Ru substitution provides a controlled pathway into an anisotropic Kitaev
spin glass regime that preserves essential fingerprints of the underlying Kitaev exchange network.
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All authors Mayia A. Vranas, Alejandro Ruiz, Vikram Nagarajan, Erik Lamb, Gerald D. Morris, Zahir Islam, Christie Nelson, Nobumichi Tamura, Benjamin A. Frandsen, James G. Analytis, Alex Frano
Materials Science
Analysis of some solid amorphous inorganic structures and the boson peak phenomenon with a computational random graph approach
A. Berezner, M. Rybakov, M. Sidlyar et al. (+1 more)
In this study, a new alternative model
algorithm has been proposed for assembling amorphous structures, unifying the bosonic paradigm applicable at low temperatures with crystalline models relevant at room and higher temperatures. Physical meaning of main model parameters is determined together with an explanation for the appearing bosonic peak using the random graph theory. Numerically, statistical atomic distribution in a multiphase amorphous system is provided without the melting simulation of base
crystals, and the mean energy function has been determined analytically. The calculated table data are in good agreement with neutronography measurements of the actual amorphous
alloy in its solid state. Programme optimisations were also implemented, and we outlined several effective steps to achieve the higher processing speed. The proposed programme code can be used for potential test assembling and simulations of amorphous systems with sorting by the optimal atomic content or proportion (i.e. glass forming ability).
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All authors A. Berezner, M. Rybakov, M. Sidlyar, V. Fedorov
Mesoscale and Nanoscale Physics
Magnon Kerr Effect in a Magnetic Thin Film Strongly Coupled to a Microwave Resonator
Davit Petrosyan, Hiroki Matsumoto, Hanchen Wang et al. (+4 more)
Progress in cavity magnonics relies on tunable nonlinearities in
magnon-
photon interactions for controlling
magnon dynamics and frequency conversion. Here, we demonstrate the
magnon Kerr effect in anisotropic magnonic systems comprising iron garnet thin films coupled to a loop-gap microwave resonator. The strong shape anisotropy, combined with strain, enhances the
magnon Kerr effect compared to a sphere of equivalent volume by up to three orders of magnitude, while the cavity enables sensitive probing of magnetization dynamics. We demonstrate continuous tunability of the magnitude and sign of the Kerr shift by controlling the static orientation of the magnetization, in excellent agreement with theory. Our findings demonstrate a scalable approach to enhancing Kerr anharmonicity in hybrid
magnon-
photon systems while preserving coherent
magnon-
photon interactions.
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All authors Davit Petrosyan, Hiroki Matsumoto, Hanchen Wang, Jamal Ben Youssef, Richard Schlitz, William Legrand, Pietro Gambardella
Materials Science
Interfacial Coupling Controls Molecular Epitaxy of HMTP on Graphene/SiC
Devanshu Varshney, Pavel Procházka, Veronika Stará et al. (+4 more)
Epitaxial growth critically influences structural and electronic properties of organic
semiconductors.
Graphene serves as a prominent van der Waals template for molecular self-assembly; however,
graphene on SiC is intrinsically heterogeneous, with decoupled monolayer
graphene coexisting with residuals of a covalently bound buffer layer, which may affect molecular ordering. Here, we track the ordering of the molecular donor, 2,3,6,7,10,11-hexamethoxytriphenylene (HMTP), from the first layer to thin films, combining low-energy electron microscopy and diffraction with X-ray diffraction. HMTP forms highly ordered epitaxial layers on single-layer
graphene, whereas growth on the buffer layer initiates as amorphous and evolves into polycrystalline films with weak orientation with respect to the substrate. Crucially, hydrogen intercalation decouples the buffer layer, converting it into quasi-freestanding monolayer
graphene and restoring epitaxial growth. These findings demonstrate that interfacial coupling governs molecular epitaxy on
graphene/SiC, and interface engineering via hydrogen intercalation provides a scalable route to control organic thin-film crystallinity on
graphene.
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All authors Devanshu Varshney, Pavel Procházka, Veronika Stará, Mykhailo Shestopalov, Jan Kunc, Jiří Novák, Jan Čechal
Statistical Mechanics · Cross-listed from quant-ph
Temperature as a Dynamically Maintained Steady State: Photonic Mechanisms, Maintenance Cost, and the Limits of the Infinite-Reservoir Idealization
David Vaknin
Classical thermodynamics treats temperature as a state variable characterizing systems in
equilibrium with idealized infinite reservoirs. We argue that this framing, while computationally exact, obscures an essential physical reality: any system at finite characteristic energy $E_c = k_B T$ continuously emits thermal radiation and cools unless energy input compensates these losses. What thermodynamics calls “thermal
equilibrium” is, at the microscopic level, a dynamically sustained steady state maintained by continuous
photon exchange. We derive that the average
photon energy required to sustain a Planck distribution is $\langle h\nu \rangle = \pi^4 E_c/[30\,\zeta(3)] \approx 2.701\,E_c$, quantifying the energetic throughput that any real system must sustain to maintain a given temperature. We resolve the apparent contradiction with the purely mechanical Maxwell velocity distribution: billiard-ball kinetics correctly describe the shape of the distribution at a given $E_c$, but cannot account for how $E_c$ is established or maintained against radiative losses in any real system of charged particles. We further show that every finite thermal reservoir is itself maintained by
photon exchange at a larger scale, organizing physical systems into a natural hierarchy from individual samples through cryostats, laboratories, and planetary surfaces to
stellar interiors, with the classical infinite reservoir emerging as the large-capacity limit within that hierarchy rather than a fundamental physical entity. We also comment on the relation between thermodynamic
entropy $S = k_B \ln W$ and the dimensionless
entropy $\mathcal{S} = \ln W$, emphasizing that $k_B$ primarily fixes units (J/K) rather than introducing new statistical content. These results do not modify thermodynamics but provide its mechanistic interpretation in terms of
quantum electrodynamics.
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Statistical Mechanics
Smart Walkers in Discrete Space
Gianluca Peri, Lorenzo Buffoni, Giacomo Chiti et al. (+4 more)
We study the statistical properties of trainable
agents moving in discrete space. After introducing the mathematical framework, we first analyze the dynamics of two completely random walkers, mutually competing in a chaser-target interaction scheme. The statistics of the encounters is analytically obtained and the predictions tested versus numerical simulations. We then move forward to extend the baseline case to
agents capable of learning and adapting to an external reward signal, using reinforcement learning. Smart walkers morph the statistics of the encounter, to maximize their cumulated reward, as confirmed by combined numerical and analytical insights. More interestingly, configuration
entropy proves a reliable proxy to gauge the acquired ability of the
agents to cope with the assigned task when no other information about them (i.e. reward signal, policy, etc) is present. We further test the proposed measure of learned skills by operating the Stockfish chess engine against a quasi-random untrained opponent. The obtained conclusions corroborate our claim. Summing up, our primary contribution is to propose and test a quantitative measure of
agents' awareness that naturally correlates with the inherent complexity of the task being performed.
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All authors Gianluca Peri, Lorenzo Buffoni, Giacomo Chiti, Duccio Fanelli, Raffaele Marino, Andrea Nocentini, Pier Paolo Panti
Mesoscale and Nanoscale Physics
Andreev spin qubits based on the helical edge states of magnetically doped two-dimensional topological insulators
Edoardo Latini, Fausto Rossi and Fabrizio Dolcini
We show that Andreev
spin qubits can be realized in a Josephson junction based on the helical edge states of a two-dimensional topological
insulator (
quantum spin Hall system) proximized by superconducting films, in the presence of
magnetic doping. We demonstrate that the electric dipole transitions between the Andreev
spin states induced by the
magnetic doping can be harnessed to manipulate the Andreev
spin qubit by microwave radiation pulses, without applying an external Zeeman field or invoking ancillary states. We numerically simulate the realization of NOT and Hadamard
quantum logic gates, and discuss implementations in realistic setups.
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Statistical Mechanics · Cross-listed from quant-ph
Probing Entanglement and Symmetries in Random States Using a Superconducting Quantum Processor
Jia-Nan Yang, Lata Kh Joshi, Filiberto Ares et al. (+3 more)
Quantum many-body systems display an extraordinary degree of complexity, yet many of their features are universal: they depend not on microscopic details, but on a few fundamental physical aspects such as
symmetries. A central challenge is to distill these universal characteristics from model-specific ones. Random
quantum states sampled from a uniform distribution, the Haar measure, provide a powerful framework for capturing this typicality. Here, we experimentally study the
entanglement and
symmetries of random many-body
quantum states generated by evolving simple product states under ergodic Floquet models. We find excellent agreement with the predictions from the Haar-random state ensemble. First, we measure the Rényi-2
entanglement entropy as a function of the subsystem size, observing the Page curve. Second, we probe the subsystem
symmetries using
entanglement asymmetry. Finally, we measure the moments of partially transposed reduced density
matrices obtained by tracing out part of the system in the generated ensembles, thereby revealing distinct
entanglement phases. Our results offer an experimental perspective on the typical
entanglement and
symmetries of many-body
quantum systems.
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All authors Jia-Nan Yang, Lata Kh Joshi, Filiberto Ares, Yihang Han, Pengfei Zhang, Pasquale Calabrese
Materials Science · Cross-listed from physics.med-ph
Chitosan/alginate bionanocomposites adorned with mesoporous silica nanoparticles for bone tissue engineering
Satar Yousefiasl, Hamed Manoochehri, Pooyan Makvandi et al. (+6 more)
The regeneration of oral and craniofacial bone defects ranging from minor periodontal and peri-implant defects to large and critical lesions imposes a substantial global health burden. Conventional therapies are associated with several limitations, highlighting the development of a unique treatment strategy, such as tissue engineering. A well-designed scaffold for bone tissue engineering should possess biocompatibility, biodegradability, mechanical strength, and osteoconductivity. For this purpose, mesoporous silica nanoparticles (MSNs) were synthesized and incorporated at different ratios (10, 20, and 30%) into alginate/chitosan (Alg/Chit)-based porous composite scaffolds fabricated through the freeze-drying method. The MSN incorporation significantly improved the mechanical strength of the scaffolds while showing a negligible decreasing effect on the porosity. All of the samples showed desirable swelling behaviors, which is beneficial for
cell attachment and proliferation. The MSN-containing scaffolds indicated a decreased hydrolytic degradation in an MSN percentage-dependent manner. The fabricated scaffolds did not depict cytotoxic characteristics. The Alg/Chit/MSN30 scaffolds not only showed noncytotoxic properties, but also increased the
cell viability significantly compared to the control group. The biomineralization properties of the MSN-containing nanocomposite scaffolds were significantly higher than the Alg/Chit composite, suggesting the potential of these nanoparticles for bone tissue engineering applications. Taken together, it is concluded that the Alg/Chit/ MSN30 scaffolds are considerable substances for bone tissue regeneration, and MSN has a great tissue engineering potential in addition to its extensive biomedical applications.
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All authors Satar Yousefiasl, Hamed Manoochehri, Pooyan Makvandi, Saeid Afshar, Erfan Salahinejad, Pegah Khosraviyan, Massoud Saidijam, Sara Soleimani Asl, Esmaeel Sharifi
Statistical Mechanics · Cross-listed from hep-th
Quantum fluctuations in hydrodynamics and quantum long-time tails
Akash Jain
We construct a
quantum Schwinger-Keldysh (SK) effective field theory for the diffusive hydrodynamics of a conserved scalar field.
Quantum corrections within the SK framework are guided by fluctuation-dissipation relations, enforced via a dynamical Kubo-Martin-Schwinger (KMS)
symmetry. We find that the KMS
symmetry necessarily generates fluctuation contributions in the SK effective action at all orders in the noise field, thereby giving rise to intrinsically non-Gaussian noise. We use our results to compute one-loop
quantum corrections to the two-point density-density retarded correlation function, leading to a
quantum generalization of hydrodynamic long-time tails. Our results apply at arbitrarily high orders in $\hbar$. The one-loop results for retarded correlation functions have been expressed in terms of a family of
polynomials. We also provide a closed-form expression for the one-loop results at leading order in the wavevector expansion.
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Materials Science · Cross-listed from quant-ph
Designing quantum technologies with a quantum computer
Juan Naranjo, Thi Ha Kyaw, Gaurav Saxena et al. (+2 more)
Interacting
spin systems in solids underpin a wide range of
quantum technologies, from
quantum sensors and single-
photon sources to
spin-defect-based
quantum registers and processors. We develop a
quantum-computer-aided framework for simulating such devices using a general many-body electron-
spin-resonance
Hamiltonian that incorporates zero-field splitting, the Zeeman effect, hyperfine interactions, dipole-dipole
spin-
spin interactions, and electron-
phonon decoherence. Within this framework, we combine Gray-encoded qudit-to-
qubit mappings,
qubit-wise commuting aggregation, and a multi-reference selected
quantum Krylov fast-forwarding hybrid
algorithm, aiming to access extended-time dynamics within the constraints of NISQ and early fault-tolerant hardware. Numerical simulations demonstrate the computation of operationally useful quantities including autocorrelation functions up to $\sim100$ ns, together with microwave absorption spectra and the $\ell_1$-norm of coherence, achieving 18-30$\%$ reductions in gate counts and
circuit depth for Trotterized time-
evolution circuits compared to unoptimized implementations. Using the nitrogen vacancy center in diamond as a testbed, we
benchmark the framework against classical simulations and identify the reference-state selection in sQKFF as the primary factor governing accuracy at fixed hardware cost. This methodology provides a flexible blueprint for using
quantum computers to design, compare, and optimize solid-state
spin-
qubit technologies under experimentally realistic conditions.
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All authors Juan Naranjo, Thi Ha Kyaw, Gaurav Saxena, Kevin Ferreira, Jack S. Baker
Strongly Correlated Electrons
Inverted anisotropy of the partially screened magnetic impurity
Krzysztof P. Wójcik and Michał P. Kwasigroch
We investigate a single
magnetic impurity in the presence of strong
spin-orbit coupling and single-ion anisotropy. We show that at sufficiently strong coupling there exists a finite temperature window, before the moment is completely screened, where the
magnetic anisotropy of the system flips: the hard-axis becomes the easy-axis or vice versa. We derive this rigorously for a single
impurity using numerical renormalization group calculations as well as Nozieres' strong-coupling limit and discuss its relevance to heavy-
fermion compounds which order magnetically along the hard-direction. We show that the coexistence of Curie-like response and Kondo fluctuations is stabilized along the initially hard direction leading to the anisotropy switch.
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Materials Science
Universal Multifractality at the Topological Anderson Insulator Transition
Ksenija Kovalenka, Ahmad Ranjbar, Sam Azadi et al. (+3 more)
Disorder is ubiquitous in
quantum materials, and its interplay with
topology can generate phases absent in the clean limit. Using the Haldane model as a minimal setting, we show that disorder not only shifts topological boundaries but also stabilizes a topological Anderson
insulator (TAI) between trivial and Chern insulating regimes. Employing the local Chern marker as a real-space topological probe, we map the full phase diagram and demonstrate that the TAI forms a finite domain bounded by trivial and Anderson
insulators. Multifractal analysis of low-energy eigenstates at the boundary reveals universal critical spectra, independent of whether disorder generates or destroys
topology. These results place
topology, localization, and criticality within a unified framework and provide clear
benchmarks for real-space diagnostics of disordered topological phases.
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All authors Ksenija Kovalenka, Ahmad Ranjbar, Sam Azadi, Rodion Vladimirovich Belosludov, Thomas D. Kühne, Mohammad Saeed Bahramy
Soft Condensed Matter
Translational and Rotational Temperature Difference in Coexisting Phases of Inertial Active Dumbbells
Subhasish Chaki and Hartmut Löwen
We investigate the effect of translational and rotational inertia on motility-induced phase separation in underdamped active dumbbells and identify the emergence of four distinct kinetic temperatures across the coexisting phases-unlike in overdamped systems. We find that the dilute, gas-like phase consistently exhibits a higher translational kinetic temperature than the dense,
liquid-like phase, with this difference amplified by increasing the rotational inertia. Rotational kinetic temperatures display a similar trend, with the dense phase remaining colder than the dilute phase; however, in this case the temperature difference grows with translational inertia and activity, while becoming practically independent of rotational inertia. This counterintuitive behavior arises from the interplay of activity-driven collisions with both translational and rotational inertia in the coexisting phases. Our results highlight the critical role of translational and rotational inertia in shaping the kinetic temperature landscape of motility-induced phase separation and offer new insights into the nonequilibrium thermodynamics of active matter.
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