Materials Science · Cross-listed from cs.CL
SALSA: Semi-Autonomous Literature Summarization Assistant
William Schertzer, Sonakshi Gupta and Rampi Ramprasad
SALSA (Semi-Autonomous Literature Summarization Assistant) is an open- source, human-in-the-loop platform for extracting structured scientific
datasets from
multimodal literature sources. The software combines document parsing, large
language models, optical character recognition, computer
vision, figure digitization, and user-guided correction tools to recover structured information from text, tables, figures, and captions. Users can configure extraction stages, define
dataset schemas, perform interventions on digitized figures, and export verified data for downstream analysis and machine learning. SALSA is designed to automate repetitive literature curation tasks while preserving oversight where expert judgment is required. By supporting customizable extraction workflows across diverse input types, the software provides a flexible framework for scalable, reliable data curation for materials research, and potentially across several disciplines. Users are responsible for ensuring that all inputs, extraction workflows, and downstream uses comply with applicable publisher agreements, copyright and licensing terms, institutional policies, and data-use requirements.
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Materials Science
FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
Kyucheol Min, Elyssa Hofgard and Tess Smidt
Predicting the thermodynamic properties of an
alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Markov chain Monte Carlo (MCMC) is the standard tool, but it requires separate simulations at different conditions, and auxiliary free-energy methods such as thermodynamic integration are used to place results on a common absolute scale. Modern discrete neural samplers typically use reverse KL divergence as the objective and can be mode-seeking or biased. We present Free energy Offering Generative Sampler (FrOGS), a hybrid discrete neural sampler that couples an autoregressive model to a continuous-time Markov chain (CTMC) to be trained jointly under a single shared loss. FrOGS draws i.i.d. configurations, returns an unbiased estimate of the partition function, and gives consistent estimates of thermodynamic observables. We train a single model across a wide range of chemical conditions to produce estimates on a common absolute free-energy scale. FrOGS matches exact finite-size results on the 2D Ising model and reference phase diagrams for AgPd and CuAu, without mode collapse. We additionally compare to SEGAL, a published autoregressive baseline, and find that only FrOGS recovers the stability range of the CuAu$_3$ phase.
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Mesoscale and Nanoscale Physics
A Unified Description of Dirac-Cone Anisotropies in Two Dimensions
L. C. T. Brito, Cleverson Filgueiras, D. M. Lopes et al. (+2 more)
Anisotropies in two-dimensional materials are responsible for a variety of effects that significantly modify their physical properties. In this paper, we present a covariant modification of the Dirac equation that incorporates anisotropies into the effective low-energy description around the Dirac point. The model is constructed by analogy with a Lorentz-violating extension of the Standard Model of elementary particles and yields a (2+1)-dimensional framework describing physical effects such as shifted, tilted, and distorted Dirac cones through its free parameters. The proposed model thus provides a general and unified framework in which distinct anisotropy-induced modifications of the Dirac spectrum and their combinations can be systematically characterized. We further apply the model to strained
graphene and show that its effective parameters can be quantitatively extracted from first-principles electronic band structures while retaining a direct geometrical interpretation. These results establish a connection between the microscopic electronic structure and a material-independent effective description of anisotropic two-dimensional Dirac systems.
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All authors L. C. T. Brito, Cleverson Filgueiras, D. M. Lopes, A. G. Martins, Igor S. S. de Oliveira
Statistical Mechanics · Cross-listed from quant-ph
Exact diffusion coefficients for quantum–classical hybrid walks
Cook Hyun Kim, Jaesub Park and Namshik Han
One-dimensional
quantum–classical hybrid walks are introduced by combining
quantum and classical steps under periodic and random protocols. Exact
diffusion coefficients are derived for both protocols throughout the diffusive regime. The exact results show how
diffusion depends on the average frequency and temporal arrangement of classical steps. As the average frequency approaches zero, both
diffusion coefficients diverge inversely with the frequency. At matched average frequencies, the ratio of the
diffusion coefficients for the random and periodic protocols approaches two. A finite-mode approximation is also developed and tested against the exact
diffusion coefficient for the random protocol. The exact results for the one-dimensional model provide a foundation for future studies of more general
quantum–classical hybrid walks.
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Quantum Gases
Non-Additivity of Microwave-Shielded Interactions between Dipolar Molecules
Fabian Prieschl, Andreas Schindewolf and Robert E. Zillich
Theoretical models of the effective interactions between microwave-shielded polar molecules typically rely on the assumption of pairwise additivity. In this work, we test the validity of this pairwise approximation by evaluating the exact many-body potential energy for configurations of three and four molecules. We demonstrate that this approximation fails for molecule configurations in the plane of the circularly polarized microwave field. In these planar geometries, many-body effects significantly increase the depth of the attractive potential well and shift the position of the repulsive barrier. Although out-of-plane interactions in three-dimensional arrangements weaken this non-additivity, the effective interactions between microwave-shielded dipoles are not a simple sum of pair interactions. In particular, their attractive well, crucial for self-bound clusters, can only be described by the full many-body potential energy.
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Superconductivity
Interaction effects on Andreev states from an electromagnetic environment
Erik S. Samuelsen and Yuli V. Nazarov
Here we present a detailed theoretical investigation of the environment-induced interaction correction to the ground states and addition energies in a short superconducting junction. We derive general formulas for these corrections linking them to the frequency-dependent admittance of the junction and the environment impedance. We specify two environmental models: that of an external impedance and the Mattis-Bardeen model. In both cases we assume $G_Q Z \ll 1$, where $G_Q$ is the conductance
quantum and $Z$ the typical environmental resistance. While an expectation is that the typical scale of the relative correction is of the order $G_Q Z$, we have found that in many cases the dimensionless scale $L G_Q \Delta$ provides a better estimation, where $\Delta$ is the superconducting energy gap and $L$ an external inductance. We discuss the logarithmic renormalizations of the energies by low- and high-frequency phase fluctuations and single out a non-superconducting contribution related to the renormalization of transmission
eigenvalues. We investigate in detail the peculiarities of the corrections at: i. small phases, where the interaction causes a current jump, ii. Andreev bound state energies close to the gap edge, iii. Andreev bound state energies close to Fermi level. In all these cases the correction may become comparable with the unperturbed energy, even for $G_Q Z \ll 1$, and we briefly sketch non-perturbative models relevant for the situations.
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Statistical Mechanics
Microscopic dynamics of contagion using active Brownian particles: universal scaling and propagation controlled by protection
Isela Sicarú Regalado-Alvarado and Francisco Alarcón
Understanding how individual protection and population density influence epidemic spreading remains a central challenge in epidemiology. While classical compartmental models successfully describe the temporal
evolution of epidemics, they do not explicitly account for the microscopic motion and spatial organisation of individuals. Here, we investigate contagion dynamics using an
agent-based model of Active Brownian Particles (ABPs), where self-propelled
agents interact through local contact and a prescribed fraction of the population is protected. By systematically varying the protected-
agent fraction and the population density, we identify two distinct contagion regimes separated by a crossover protection of approximately $30\%$. Below this threshold, the maximum contagion rate follows the universal scaling $\nu_{\mathrm{max}}\propto\phi^{1/2}$, indicating that disease transmission is governed primarily by frequency of encounters. Above the threshold, universal scaling is lost and protection becomes the dominant mechanism controlling epidemic spreading, with the strongest suppression occurring in low-density populations. These results demonstrate that microscopic active-matter models provide a powerful framework for investigating epidemic dynamics beyond the assumptions of well-mixed population models and reveal how spatial organisation and individual protection jointly determine contagion dynamics.
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Materials Science
Machine Learning Local Potentials for Accelerated Electron-Phonon Interactions Calculations within the Projector Augmented-Wave Framework
Yi Xia
Electron-
phonon interactions govern carrier dynamics, transport, and many optical and
quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-
symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-
phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the established VASP/PHELEL framework. Using targeted grid-point sampling and feature-diversity selection for efficient model training, we validate MLLocP for elemental Cu, polar BAs, and chemically disordered Cu3Au. The learned potentials and their resulting displacement derivatives closely reproduce direct density functional theory calculations, yielding transport coefficients within approximately 3.5% for BAs mobility, 8% for Cu conductivity, and 11% for Cu3Au conductivity. For the 32-atom Cu3Au special quasirandom structure, using 40 sampled configurations reduces the number of self-consistent calculations by about fivefold relative to the 192 displaced structures required directly; using 20 or 10 configurations increases the acceleration to approximately 10- and 20-fold, with conductivity errors of 14.3% and 18.6%, respectively. MLLocP thus provides a scalable route to PAW electron-
phonon calculations in complex materials while preserving the underlying all-electron formalism.
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Soft Condensed Matter · Cross-listed from physics.flu-dyn
Yielded-region connectivity governs the onset of gravity-driven spreading in elastoviscoplastic drops
Alice Woodbridge, Patrick O'Rourke, Cláudio P. Fonte et al. (+1 more)
The physical mechanisms controlling the onset of
gravity-driven spreading of elastoviscoplastic drops remain unclear because yielding, elastic deformation, and viscous dissipation can occur simultaneously in different regions of the material. To isolate these mechanisms, we perform direct numerical simulations of large axisymmetric drops resting on a thin precursor layer of the same material. Each drop is first equilibrated under
gravity, $g$, before being subjected to an additional constant downward acceleration of varying magnitude. The drop rheology is described by the Saramito–Herschel–Bulkley model. Once the rate of deformation becomes negligible under the enhanced forcing, the additional acceleration is removed and the drop is allowed to relax again under
gravity alone, enabling the global deformation to be separated into recoverable and unrecoverable components. We find that spreading does not occur simply because some portion of the material yields locally. At low imposed accelerations, yielded regions remain confined within a predominantly solid-like viscoelastic
matrix, and the resulting deformation is mostly recoverable. Appreciable spreading is observed when these yielded regions connect to form a continuous fluidised pathway from the drop interior to the free surface, allowing radial material transport. The acceleration required for this transition is governed primarily by the yield stress, whereas elasticity controls the partition between recoverable deformation and spreading by regulating the extent and spatial distribution of yielded regions.
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All authors Alice Woodbridge, Patrick O'Rourke, Cláudio P. Fonte, Anne Juel
Disordered Systems and Neural Networks · Cross-listed from quant-ph
Quantum Circuit and Tensor Network Implementation of the 2D Acoustic Wave Equation
Tamas Nemeth and Gabor Vattay
We present a cohesive framework for simulating seismic wave propagation utilizing
quantum computing paradigms and their classical tensor network equivalents. We detail a
quantum circuit-based formulation for the explicit finite-difference time-domain (FDTD) solution of the two-dimensional acoustic wave equation and map this
quantum architecture onto a tensor train representation, namely for
Matrix Product State (MPS). The MPS solver enables deterministic simulation of large-scale wavefield dynamics on classical high-performance computing systems. We demonstrate the MPS representation by computing 2D seismic wavefields on the Marmousi model. Our results indicate that the MPS representation is a viable direction for computing and scaling wavefield propagation.
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Materials Science
Morphology and Dynamics of Self-interstitial Clusters in Irradiated Nickel
Ajay Annamareddy, Ibrahim Momohjimoh, Hangyu Li et al. (+3 more)
Self-interstitial atom (SIA) clustering is a key early step in radiation damage
evolution in face-centered cubic (FCC) metals, governing defect transport, recombination, and the long-term microstructural response of irradiated
alloys. We combine molecular dynamics (MD) simulations and high-speed (>1000 frames/s) in situ transmission electron microscopy (TEM) to investigate the structure, energetics, and migration dynamics of SIA clusters in FCC Ni. MD simulations show that interstitials initially form disordered dumbbell clusters that evolve into either sessile Frank loops or glissile perfect (prismatic) loops; the latter progressively reorganize into compact ordered configurations with increasing mobility. Direct construction of both loop types over a wide size range, validated against MD-relaxed structures, shows that perfect loops are thermodynamically favored over Frank loops for cluster sizes N greater than or equal to 14, where N is the number of SIAs, with the energetic advantage increasing with cluster size. Nevertheless, substantial kinetic barriers allow Frank loops to persist as metastable defects. For perfect loops,
diffusion coefficients computed over N = 16 to 400 reveal a nearly size-independent migration barrier of approximately 0.02 eV, while the
diffusion prefactor decreases approximately as N^(-0.54). Trajectory analysis reveals a non-rigid, row-wise relay mechanism in which the number of participating atoms increases systematically with loop size, accounting for much of the observed prefactor scaling. Sub-millisecond in situ TEM observations reveal intermittent loop motion at velocities higher than previously observed but still several orders of magnitude below the intrinsic mobilities predicted by MD, indicating migration through a heterogeneous energy landscape of mobile and pinned states.
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All authors Ajay Annamareddy, Ibrahim Momohjimoh, Hangyu Li, Kevin G. Field, Paul M. Voyles, Dane Morgan
Materials Science · Cross-listed from cs.CV
RAFT-DVC: Resolution-Aware Machine Learning-Based Digital Volume Correlation
Zixiang Tong, Lehu Bu and Jin Yang
Digital volume correlation (DVC) provides three-dimensional full-field displacement measurements from volumetric images, but how the internal resolution of a machine-learning-based DVC model affects accuracy and operating range remains poorly understood. Here, we present RAFT-DVC, a resolution-aware family of recurrent all-pairs field transforms (RAFT)-based DVC solvers with encoder downsampling factors s = 2, 4, and 8. Using a matched design, we find that the three solvers localize displacement to approximately 0.017 feature-grid voxel, giving an empirical raw-volume error scaling of approximately 0.017s voxel. The solvers exhibit complementary operating regimes governed jointly by displacement reach and volumetric-texture compatibility. Synthetic
benchmarks show that RAFT-DVC achieves errors of the same order as tuned classical DVC under fine-texture, small-to-moderate-displacement conditions and becomes competitive or advantageous under coarse-texture, large-displacement conditions. Frequency-swept tests quantify deformation spatial resolution, while tiled inference enables dense estimation on large volumes. Evaluation on confocal volumetric images acquired during indentation illustrates the importance of matching solver operating regime to deformation magnitude and image texture. Tests on micro-CT images of elastomeric foam, despite training only on particle-labeled synthetic data, provide evidence of cross-texture transfer. We also identify coordinate-order inconsistencies in three-dimensional RAFT correlation sampling and introduce a non-cubic impulse test to verify sampler geometry independently of network training. Correcting the sampler improves native-input accuracy and generalization to unseen volume dimensions. Together, these results establish RAFT-DVC as a fast, resolution-aware framework for dense DVC with characterized accuracy and operating regimes.
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Materials Science
Molecular spin qubits in a van der Waals bottle
Anna Champ, Eleanor Mackintosh, André Koch Liston et al. (+22 more)
Advancing
quantum information technologies requires
qubits whose coherence can be precisely engineered. Among the
qubit platforms in development, molecular
spin qubits (MSQs) stand out for their atomic scale tunability and chemical specificity, making them powerful candidates for sensing, simulation, and information processing. However, integrating MSQs into solid-state architectures without degrading their coherence remains a central challenge. Here, we introduce van der Waals (vdW) confinement within two-dimensional materials as a strategy for stabilizing
quantum states in MSQs by engineering their local electronic, vibrational, and
symmetry environments. Using cobaltocene as a model system, we show that confinement within vdW SnS2 and CdPS3 single
crystals reorganizes the single-ion energy landscape and slows
spin-
lattice relaxation by over two orders of magnitude relative to unconfined cobaltocene. The confined MSQs adopt deterministic orientations and self-assemble into ordered, atomically precise superlattices, establishing vdW confinement as a pathway for integrating MSQs into functional
quantum devices.
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All authors Anna Champ, Eleanor Mackintosh, André Koch Liston, Sanghyo Lee, Eric D. Walter, Raphaël P. Hermann, George Yumnam, Eric Seewald, Xuehao Wu, Willa Mihalyi-Koch, Augustin Braun, Johan van Tol, Tomas Orlando, Mykhaylo Ozerov, Byeongjun Gil, Myung-Geun Han, Yimei Zhu, Miyoung Kim, Abhay N. Pasupathy, Milan Delor, Michael L. Steigerwald, Timothy C. Berkelbach, Colin Nuckolls, Xavier Roy, Arun Ramanathan
Statistical Mechanics · Cross-listed from q-bio.PE
A greedy nearest-neighbor approach to quantify site revisitation: comparing two sympatric raven species
Bar Ashkenazi, Miguel de Guinea, Michael Assaf et al. (+1 more)
A central challenge in movement ecology is to describe ecologically meaningful residence sites from raw tracking data due to heterogeneous sampling frequency and uncertain site boundaries. Here, we develop a greedy nearest-neighbor clustering approach with local reassignment and polygon-based site construction that generates timestamped sequences of site visits for each tracked individual, and apply it to nine years of GPS data from two sympatric raven species in the Dead Sea region—the Fan-tailed Raven (Corvus rhipidurus) and the Brown-necked Raven (C. ruficollis). Using the resulting visitation sequences, we quantify recursion patterns using the non-Markovian individual mobility model (IMM), which captures the balance between novel-site discovery $\beta$ and preferential return $\alpha$. The inferred dynamics are consistent with IMM predictions and reveal clear interspecific differences: Brown-necked Ravens continue to discover new sites at a higher rate (lower $\beta$), whereas Fan-tailed Ravens show a steeper concentration of visits among top-ranked sites.
Entropy analyses further separate the species, with Brown-necked Ravens exhibiting higher site
entropy and higher conditional
entropy of site-to-site transitions. Together, these results provide a robust framework for describing interspecific differences in movement strategies that can be used to infer
memory from GPS data and suggest distinct space-use strategies in two closely related sympatric species.
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All authors Bar Ashkenazi, Miguel de Guinea, Michael Assaf, Ran Nathan
Mesoscale and Nanoscale Physics
Magnons in Metallic Altermagnetic $\text{KV}_2\text{Se}_2\text{O}$
Daniel Lourenço R. Santos and António T. Costa
We investigate the
spin excitation in altermagnetic vanadium oxyselenides, focusing on the metallic quasi-two-dimensional compound $\text{KV}_2\text{Se}_2\text{O}$. Using a fermionic
Hamiltonian derived from ab initio calculations, we compute the transverse
spin susceptibilities within the random phase approximation and extract the
magnon dispersion relation. We show that the altermagnetic
symmetry leads to characteristic degeneracies and directional splittings in both the electronic bands and the
magnon spectra along high-
symmetry paths of the Brillouin zone. The metallic $\text{KV}_2\text{Se}_2\text{O}$ exhibits finite
magnon linewidths arising from the coupling to the particle-hole continuum. Furthermore, by fixing the magnitude of the wave vector and varying its in-plane direction, we uncover a pronounced angular dependence of the
magnon energies and lifetimes, with complementary damping behavior between the two
magnon branches. Upon including
spin-orbit coupling, the system exhibits an out-of-plane easy c-axis anisotropy, which opens a
magnon gap of approximately 6 meV at the $\Gamma$-point, possibly rendering the
magnetic order stable at room temperature. Our results demonstrate that altermagnetism controls not only the dispersion but also the anisotropic decay of
spin excitations, highlighting altermagnetic metals as promising platforms for directionally selective
magnon transport.
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Materials Science
Symmetry-selective nonrelativistic spin splitting in antiferromagnets driven by coherent phonons
Sangeeta Rajpurohit, Mohsen Yarmohammadi, Sheikh Rubaiat Ul Haque et al. (+3 more)
Nonrelativistic
spin splitting (NRSS) in antiferromagnets (AFMs) enables magnetization-free
spin polarization for ultrafast spintronics. Here, we demonstrate that coherent
phonons can dynamically induce and control NRSS in collinear AFMs. Excitation of $\Gamma$-point infrared-active
phonons lifts the
spin degeneracy of the ground state, while the residual sublattice-connecting
symmetries determine the momentum-space form of the induced splitting. Because the relevant infrared modes couple to orthogonal in-plane light polarizations, distinct
spin-split phases can be selectively activated by the polarization of the driving field. Using first-principles calculations for MnPS$_3$, we show that a mode that breaks all sublattice-connecting
symmetries induces an $s$-wave
spin-split state with $\Delta(\Gamma)\neq0$, whereas a
symmetry-distinct mode that preserves a sublattice-connecting mirror
symmetry generates a $d$-wave altermagnetic state with $\Delta(\Gamma)=0$. In both cases, the
spin splitting grows linearly with the
phonon amplitude and reverses the induced
spin polarization when the displacement is reversed. Our results establish coherent
lattice driving as a direct, polarization- and mode-selective route to dynamically induce distinct NRSS phases in AFMs.
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All authors Sangeeta Rajpurohit, Mohsen Yarmohammadi, Sheikh Rubaiat Ul Haque, Tony F. Heinz, Aaron M. Lindenberg, Tadashi Ogitsu
Quantum Gases
Kelvin-Helmholtz instability in annular superfluids across the UFG-BCS crossover
Michał Śliwiński, Klejdja Xhani and Gabriel Wlazłowski
We investigate the Kelvin-Helmholtz instability of counter-rotating annular Fermi superfluids using time-dependent superfluid density functional theory. When the two initially separated superflows merge, a necklace of quantized
vortices forms at their interface and subsequently becomes unstable. We extract mode-resolved instability growth rates and study their dependence on relative flow velocity, interaction strength, and temperature across the strongly interacting
unitary regime and the weakly attractive BCS regime. In the
unitary gas and moderate BCS regimes, the growth rates remain qualitatively consistent with the point-
vortex model, though systematically at or above its predictions, with no significant dependence on interaction strength or temperature up to approximately $0.3 T_c$. Deeper in the BCS regime, the dynamics change qualitatively: boundary-generated antivortices proliferate faster than the Kelvin-Helmholtz instability itself, destroying the initial
vortex necklace before the instability can fully develop. These results provide a microscopic
benchmark for Kelvin-Helmholtz dynamics in fermionic superfluids and help constrain possible explanations of the existing experiment-theory discrepancy.
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Mesoscale and Nanoscale Physics
Tuning Boron-Vacancy Qubit Coherence through Layer Number in hBN
Juan M. Florez and Eric Suárez Morell
The negatively charged boron vacancy ($\VB$) in hexagonal boron nitride (\hBN{}) was the first optically addressable
spin qubit identified inside a van der Waals
crystal, allowing atomically defined placement relative to a target. Its coherence in bulk \hBN{} is limited by the boron
nuclei of the layers flanking the defect plane, which a thin flake removes. Here, we use a generalized cluster-correlation expansion with an extended central-
spin block to calculate the Hahn-echo coherence time of $\VB$ in h$^{11}$B$^{15}$N as a function of layer number. Our results show that $T_2$ rises from $199\ns$ in the bulk limit to $653\ns$ in a monolayer, a factor of $3.3$ that is already saturated at three layers and that a sublattice decomposition attributes entirely to boron. The enhancement is confined to low field, is insensitive to stacking registry and twist angle, and requires
alignment to within about two degrees, an onset we reproduce with no free parameters. Under dynamical decoupling, a $1/e$ threshold returns $206\ns$ for every thickness, but this is a zero of the first-shell modulation rather than a decay: measured without a threshold, the layer contrast survives and reaches a factor of $170$. Layer number thus emerges as a design parameter for $\VB$-based sensing.
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Strongly Correlated Electrons
Evolution from Kitaev to XXZ spin chains via distortion: Application to BaCo$_2$V$_2$O$_8$
Philip Richard, Mandev Bhullar and Hae-Young Kee
BaCo$_2$V$_2$O$_8$ is a prototypical
spin-orbit-coupled Ising-chain antiferromagnet that provides a unique platform for studying field-induced
quantum magnetism. Under a transverse
magnetic field, it exhibits unusual
magnetic properties, including an anomalous staggered magnetization and a pronounced anisotropy of the critical field with respect to the in-plane field direction. While these phenomena have been attributed phenomenologically to a site-dependent anisotropic $g$-tensor, a recent microscopic theory has shown that
spin-orbit coupling naturally generates bond-dependent Heisenberg, Kitaev, and $\Gamma$ exchange interactions. Here, we unify these two pictures by extending the microscopic theory to incorporate distortions of the CoO$_6$ octahedra. We show that the distortions not only generate the site-dependent anisotropic $g$-tensor but also renormalize the staggered exchange interactions through a distortion-induced contribution that partially compensates the Kitaev-derived staggered term. Using point-charge calculations to estimate the $g$-tensor of BaCo$_2$V$_2$O$_8$, we demonstrate that the strong anisotropy of the critical field originates from the combined effects of the modified exchange interactions and the anisotropic $g$-tensor. Our work provides a unified microscopic framework for understanding the
magnetic anisotropy of
spin-orbit-coupled Ising-chain materials.
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Statistical Mechanics
Piston-Like Information Engine II: Boundary-Controlled Optimum in Active Matter
Laura Hoek, Neta Ben Ari, Rémi Goerlich et al. (+2 more)
Information engines convert information into extractable work through measurements and feedback. We realize an engine that employs information to compress a gas of self-propelled bristle bots. Its work per measurement, $\bar{W}$, is controlled by the size of the detection region $\Delta x$ and the probability $p_1$ that this region is vacant. In thermal systems, $\bar{W}$ follows the universal form $-p_1 \ln p_1$, whereas the active engine shows a qualitatively modified relation. As particle density increases, the maximum of $\bar{W}(\Delta x)$ switches between two distinct operating regimes. Notably, this transition is also found in the solutions that maximize power output for finite-time cycles with dynamics affected by dry friction. We attribute this nonequilibrium feature to the accumulation of active particles near the boundaries.
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All authors Laura Hoek, Neta Ben Ari, Rémi Goerlich, Saar Rahav, Yael Roichman