Strongly Correlated Electrons
Analytic continuation of Green's functions with a neural network
Fakher Assaad, Johanna Erdmenger, Anika Götz et al. (+3 more)
An important problem in many-body physics is to reconstruct the spectral density from the imaginary-time domain Green's function. Typically, the imaginary-time Green's function is generated by Monte Carlo methods. As the one-point fermionic kernel diverges exponentially for large frequencies, numerical noise generically causes instabilities. We use a convolutional neural network to obtain the spectral density for a given imaginary time Green's function. The network is trained by data which we generate using random Gaussians. We improve the training data set available by including collision centers for the Gaussians rather than employing uniformly distributed Gaussians. Our network is constructed in such a way that its output fulfills positive semidefiniteness. We compare the results of our network with results of the Maximum
Entropy method (MaxEnt), a standard method for the same reconstruction problem for the spectral density. This comparison is performed for three different cases, namely our Gaussian based test data as well as two physical models, the 1d Hubbard model showing
spin-charge separation, and the two-dimensional SSH model in the self-consistent Born approximation. We find that the network outperforms MaxEnt when presented data close to the training set. For the physical models considered, MaxEnt recognizes physical features more precisely as compared to our network prediction. While it is hard to improve MaxEnt, the quality of the network depends on the training data set which can be systematically enhanced and improved.
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All authors Fakher Assaad, Johanna Erdmenger, Anika Götz, René Meyer, Martin Rackl, Yanick Thurn
Materials Science
Buried Stressor Engineering for Position-Controlled InGaAs Quantum Dots with Local Density Variation for Integrated Quantum Photonics
Martin Podhorský, Maximilian Klonz, Lux Böhmer et al. (+5 more)
We report on the monolithic, two-step epitaxial growth of site-controlled InGaAs
quantum dots via the buried stressor method with local
quantum dot density variation. As a result of high fabrication accuracy, we achieve low lateral displacements of the individual buried stressor apertures of $ 17^{+19}_{-17}$~nm from mesa centers. We provide extensive micro-photoluminescence and cathodoluminescence characterization of the site-controlled
quantum dots and give theoretical calculations, explaining the effect of the stressor aperture on the
quantum dot emission properties, positioning, and density. We show reproducibility of the nucleation process for apertures of the same size and achieve precisely-positioned, low- and high-density
quantum dot nucleation within one active layer growth step. The results presented in this work demonstrate the significant potential of the buried stressor concept in fabricating single photonic chips, simultaneously combining single-
photon sources and microlasers featuring different local densities of site-controlled
quantum dots, paving the way for highly functional source modules with applications in photonic
quantum technology.
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All authors Martin Podhorský, Maximilian Klonz, Lux Böhmer, Sebastian Kulig, Chirag C. Palekar, Petr Klenovský, Sven Rodt, Stephan Reitzenstein
Materials Science
AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments
Rigoberto Advincula and Jihua Chen
This review explores the
evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted
Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of
polymer dielectrics, and structure-order correlation in
polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications.
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Materials Science
Phase-field simulations of nucleation, growth, and coarsening of $\beta_1$ precipitates in Mg-Nd alloys
Lingxia Shi, Stephen DeWitt, David Montiel et al. (+3 more)
The spatial distribution and morphology of precipitates formed during aging are key factors that determine the precipitation-hardening response of various magnesium-rare-earth
alloys. In recent years, the use of high-performance computing clusters and massively parallel frameworks has enabled quantitative simulations of the
evolution of individual and multiple precipitates at relevant length and time scales. However, predictive modeling of precipitate
evolution remains challenging, in part because many key thermodynamic and kinetic parameters governing the underlying physics are either unknown or have a high degree of uncertainty. In this work, we developed a workflow in which experimental data were used to parameterize a phase-field model to perform two-dimensional (2D) simulations of concurrent nucleation and
evolution of $\beta_1$ precipitates in magnesium-neodymium
alloy during aging.
Matrix composition and precipitate number density at different aging times were obtained from atom probe
tomography and transmission electron microscopy measurements, respectively. We applied a stereological method to estimate the three-dimensional (3D) number densities from experimental cross-sectional transmission electron micrographs. The estimated 3D number density data were then converted to effective 2D number densities. The effective 2D number density and composition data were used to determine the required model parameters by minimizing the discrepancy between simulation and experimental results. The parameterized model allows for quantitative phase-field simulations of nucleation and growth of $\beta_1$ precipitates, which can be employed to optimize aging time to achieve a target number density of precipitates. This work highlights an approach to overcome the challenges associated with parameterizing a coupled phase-field and nucleation model.
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All authors Lingxia Shi, Stephen DeWitt, David Montiel, Qianying Shi, John Allison, Katsuyo Thornton
Soft Condensed Matter · Cross-listed from cs.RO
Snapping Actuators with Asymmetric and Sequenced Motion
Xin Li, Ye Jin, Mohsen Jafarpour et al. (+2 more)
Snapping instabilities in soft structures offer a powerful pathway to achieve rapid and energy-efficient actuation. In this study, an eccentric dome-shaped snapping actuator is developed to generate controllable asymmetric motion through geometry-induced instability. Finite element simulations and experiments reveal consistent asymmetric deformation and the corresponding pressure characteristics. By coupling four snapping actuators in a pneumatic network, a compact quadrupedal
robot achieves coordinated wavelike locomotion using only a single pressure input. The
robot exhibits frequency-dependent performance with a maximum speed of 72.78~mm/s at 7.5~Hz. These findings demonstrate the potential of asymmetric snapping mechanisms for physically controlled actuation and lay the groundwork for fully untethered and efficient soft robotic systems.
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All authors Xin Li, Ye Jin, Mohsen Jafarpour, Hugo de Souza Oliveira, Edoardo Milana
Disordered Systems and Neural Networks
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico et al. (+6 more)
Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid due to a lack of standard
benchmarks on truly hard instances. From a statistical physics perspective, we propose new hard
benchmarks based on random problems. We provide these
benchmarks, along with performance results from both classical heuristics and GNNs. Our fair comparison shows that classical
algorithms still outperform GNNs. We discuss the challenges for neural networks in this domain. Future claims of superiority can be made more robust using our
benchmarks, available at https://github.com/ArtLabBocconi/RandCSPBench.
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All authors Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico, David Machado, Raffaele Marino, Matteo Negri, Federico Ricci-Tersenghi, Carlo Lucibello, Maria Chiara Angelini
Disordered Systems and Neural Networks
Overlap locking and non-perturbative effects in spin glasses
Silvio Franz, Giorgio Parisi and Federico Ricci-Tersenghi
We study the phenomenon of the locking of the order parameter (or synchronization) in
spin glasses at low temperatures. When two systems with independent disorders are coupled, their overlaps become similar. A crucial question is how this effect depends on the strength of the coupling between the two systems. Non-perturbative phenomena are present when $1 \ll \Delta H \ll N$, being $\Delta H$ the coupling
Hamiltonian and $N$ the size of the system. In this intermediate-coupling region, the effect is related to finite-size free-energy corrections and to the correlations in the Dyson hierarchical
spin glass, a model that mimics the physics of finite-dimensional systems. We study this phenomenon in the mean-field approach, both analytically and numerically, and we finally compute the critical exponents for finite-volume corrections in mean-field theory and for the decay of correlations in the Dyson hierarchical model.
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Soft Condensed Matter · Cross-listed from cs.RO
Ori-Sense: origami capacitive sensing for soft robotic applications
Hugo de Souza Oliveira, Xin Li, Mohsen Jafarpour et al. (+1 more)
This work introduces Ori-Sense, a compliant capacitive sensor inspired by the inverted Kresling origami pattern. The device translates torsional deformation into measurable capacitance changes, enabling proprioceptive feedback for soft robotic systems. Using dissolvable-core molding, we fabricated a monolithic silicone structure with embedded conductive TPU electrodes, forming an integrated soft capacitor. Mechanical characterization revealed low stiffness and minimal impedance, with torque values below 0.01 N mm for axial displacements between -15 mm and 15 mm, and up to 0.03 N mm at 30 degrees twist under compression. Finite-element simulations confirmed localized stresses along fold lines and validated the measured torque-rotation response. Electrical tests showed consistent capacitance modulation up to 30%, directly correlated with the twist angle, and maximal sensitivity of S_theta ~ 0.0067 pF/deg at 5 mm of axial deformation.
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All authors Hugo de Souza Oliveira, Xin Li, Mohsen Jafarpour, Edoardo Milana
Strongly Correlated Electrons · Cross-listed from hep-lat
Phase diagram of a lattice fermion model with symmetric mass generation
Sandip Maiti, Debasish Banerjee, Shailesh Chandrasekharan et al. (+1 more)
We study the phase structure of a model containing two
flavors of massless staggered
fermions interacting through two independent four-
fermion couplings, UI and UB, formulated on a three-dimensional Euclidean space-time
lattice. At UB = 0, this model is known to exhibit a direct second-order
quantum phase transition between a massless
fermion (MF) phase and a phase in which
fermions acquire masses through the mechanism commonly referred to as symmetric mass generation (SMG). We demonstrate that introducing a small nonzero value of UB qualitatively alters this structure: the single exotic transition at UB = 0 splits into two distinct, conventional transitions, separated by an intermediate phase in which
fermion masses arise through the standard mechanism of spontaneous
symmetry breaking (SSB). The first of these is a Gross-Neveu transition separating the MF phase from the SSB-induced massive phase, while the second is a three-dimensional XY transition between the SSB phase and the SMG phase. Using the
fermion-bag Monte Carlo method, we verify that the critical exponents associated with both transitions are consistent with the literature, thereby yielding a quantitative characterization of the resulting phase structure of the model.
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All authors Sandip Maiti, Debasish Banerjee, Shailesh Chandrasekharan, Marina K. Marinkovic
Superconductivity
Mitigation of Magnetic Flux Trapping in Superconducting Electronics Using Moats
Rohan T. Kapur, Sergey K. Tolpygo, Alex Wynn et al. (+7 more)
Magnetic flux (
vortex) trapping remains a major obstacle to very large scale integration in superconducting electronics. Moats – etched regions in
circuit layers placed in ground planes and around critical circuitry – offer a simple passive approach to sequester flux. Here, we systematically examine the effectiveness of moat arrays in superconducting niobium films as a function of geometry (size, shape, and density) and background
magnetic field. By measuring the
vortex expulsion field, we estimate the flux saturation number and flux trapping temperature for a range of geometries. We find that many moat designs effectively sequester flux in magnetically shielded environments (< 1 $\mu$T), with high-aspect-ratio rectangular "slit" moats providing the strongest mitigation at minimal area cost. However, our measurements show that moats alone do not eliminate flux trapping in non-ideal films, as
vortices can preferentially pin at material defects. These results provide design guidance for flux mitigation in superconducting integrated
circuits and highlight the need for combined optimization of
circuit geometries and materials.
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All authors Rohan T. Kapur, Sergey K. Tolpygo, Alex Wynn, Pauli Kehayias, Adam A. Libson, Collin N. Muniz, Michael J. Gold, Justin L. Mallek, Danielle A. Braje, Jennifer M. Schloss
Soft Condensed Matter
Disentangling Entropic, Active, and Frictional Forces in Cytoskeletal Crosslinking
Cedrik Barutel and Sebastian Fürthauer
The forces that mixtures of motorized and passive crosslinking
proteins collectively generate between cytoskeletal filaments within our
cells are the key drivers of active cellular mechanics. Despite their importance, a unified theory to describe such crosslinking forces has so far been missing. In this paper, we derive a theory that predicts the forces generated collectively by crosslinking
proteins linking two biopolymer filaments from measurable filament and crosslinker properties, using out-of-
equilibrium thermodynamics. Our framework allows us to decompose the forces generated by crosslinkers into three separate components: entropic, active, and frictional. In doing so, it offers a clear physical interpretation of the fundamental mechanisms by which crosslinking
proteins self-organize and collectively generate forces. We demonstrate the robustness and utility of this framework by applying it to four different experiments that probe the combined roles of passive and motorized crosslinkers. For each experiment, our theoretical approach allows us to disentangle the relative contributions of entropic, active, and frictional forces, clarifying how different physical processes underpin collective force production. In turn, this makes it possible to quantitatively compare and predict how various crosslinker combinations influence force generation between filaments, pattern formation along filaments, and the dynamics of filament pairs.
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Mesoscale and Nanoscale Physics
Modeling of a magnetic field sensor based on spin Hall magnetoresistance
Syeda Farwa Bukhari, Alessandro Magni, Witold Skowroński et al. (+8 more)
Next-generation spintronic sensors aim to overcome the limitations of traditional tunneling-magnetoresistance (TMR) devices, such as complex manufacturing, high $1/f$ noise, and significant offsets. This work presents a comprehensive modeling and experimental validation of a
magnetic field sensor based on
Spin Hall Magnetoresistance (SMR) in a Wheatstone bridge configuration. Utilizing a multiphysics approach, we simulate the interplay between SMR, Anisotropic Magnetoresistance (AMR), and
Spin-Orbit Torque (SOT) using a Stoner-Wohlfarth model complemented by a Fuchs-Sondheimer analysis of current distribution. To account for the presence of
magnetic domains, we incorporate a modified Stoner-Wohlfarth framework that considers non-uniform magnetization and domain wall motion through a "truncated astroid" approach, allowing for a statistical distribution of single-domain particles. The model is validated against experimental measurements of Pt/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$ and Ta/$\text{Fe}_{60}\text{Co}_{20}\text{B}_{20}$
bilayers patterned into
Hall bars and Wheatstone bridges. The model provides critical design guidelines for optimizing material properties, layer thickness, and device layout to minimize power consumption and maximize sensitivity in SMR-based sensing applications.
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All authors Syeda Farwa Bukhari, Alessandro Magni, Witold Skowroński, Elena Losero, Vittorio Basso, Carlo Appino, Piotr Wiśniowski, Juergen Langer, Berthold Ocker, Dario Daghero, Michaela Kuepferling
Materials Science
A stochastic simulation of the dislocation-mediated etching of porous GaN distributed Bragg reflectors
Piotr Sokolinski, Ben Thornley, Zetai Xu et al. (+3 more)
Distributed Bragg reflectors (DBRs) can be fabricated by electrochemically etching nitride epitaxial structures consisting of alternating layers of highly n-type doped and non-intentionally doped (NID) GaN. Threading dislocations (TDs) can be electrochemically etched into transport pipelines that can carry the etchant through the NID layers to access the doped material. Experimentally this has been shown to involve a mechanism where the etching pathway may follow one TD into a doped layer and then propagate sideways through the doped layer to continue via a different TD. Across multiple layers this process creates complex pore structures that have been described as 'cascades'. Here, we build a
stochastic simulation for the DBR etching process that can reproduce some key features of the observed microstructures including the cascade morphology. By comparing the simulation output to samples etched at a range of voltages, we show that we can reproduce variations in experimental chronoamperometry data with applied bias by varying the probability of etching the doped layers within the simulation. The outputs of the resulting simulations replicate the experimentally observed cascade morphology. At higher voltages, experimental data reveal a lower proportion of cascade features, a trend that is also replicated by the simulations for relevant probability values. Outputs of the simulations also correlate well with experimental chronoamperometry data for samples where - unlike in a DBR - the thicknesses of the doped layers vary through the epitaxial multilayer, suggesting that the probabilistic simulation can be applied to a range of structures to help understand the dislocation-mediated electrochemical etching process.
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All authors Piotr Sokolinski, Ben Thornley, Zetai Xu, Thom R. Harris-Lee, Menno J. Kappers, Rachel A. Oliver
Disordered Systems and Neural Networks
Responsive Disorder in a Metal-Organic Framework Enables Solid-State Reservoir Computing
Guy Greenbaum, Will R. Branford and Andrew L. Goodwin
Complex systems with nonlinear response mechanisms can be applied as reservoir computers for energy-efficient machine learning tasks. Historically explored at the macro- and meso-scale, physical reservoir computing has recently been extended to the atomic scale via chemical mixtures with strong and dynamic heterogeneity. Here we explore the possibility that configurational degeneracy within disordered materials might form the basis for solid-state atomic-scale reservoirs. Our proof-of-concept uses the disordered metal-organic framework DUT-8, which undergoes a series of disorder-disorder transitions on exposure to different guest species. We show that variations in X-ray diffuse scattering associated with these transitions function as suitable readouts for machine learning applications. A combination of nonlinearity and
memory effects in the DUT-8 response allows the system to carry out both classification and time-series machine learning tasks with accuracies comparable to those of mesoscale physical reservoir computers. Our results suggest a new avenue for exploiting correlated disorder in solid phases whenever the nature of that disorder can be modulated through external perturbations-a phenomenon we term `responsive disorder'.
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Statistical Mechanics
Near-optimality of conservative driving in discrete systems
Jann van der Meer and Andreas Dechant
Transferring a physical system from an initial to a final state while minimizing energetic losses is an interdisciplinary control problem that bridges
stochastic thermodynamics and optimal transport theory. Recent research typically considers problems in which the optimal solution is realized via conservative forces, but whether this situation applies depends on the problem's constraints. In systems with complex
topologies like discrete networks, the optimal, dissipation-minimizing protocol involves applying nonconservative forces along cycles if the timescales of the transitions in the network are fixed. We show that although nonconservative driving is optimal in this setting, a conservative protocol always exists whose dissipation is at most 4/3 times the optimum. This finding is complemented with an example modeling transport across an energy barrier, which saturates this bound. We also prove near-optimality of conservative driving with a weaker numerical factor for a more general class of rate parametrizations with different load-sharing factors, stimulating the idea that optimality of nonconservative driving might be a generic phenomenon: As fewer degrees of freedom can be optimized, additional degrees of freedom due to adding nonconservative forces become more significant.
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Materials Science
Finely Tunable Thermal Expansion of NiTi by Stress-Induced Martensitic Transformation and Thermomechanical Training
Won Seok Choi, Won-Seok Ko, Yejun Park et al. (+6 more)
Tailoring the thermal expansion of martensitic materials by crystallographic texture and anisotropic variation of
lattice parameters is a promising route to a flexible design of thermally stable systems. NiTi
alloys are prototype materials in this respect, with shape-
memory and superelastic properties owing to their thermoelastic martensitic transformations. Here, we propose a method to realize finely tunable coefficients of thermal expansion (CTE) for the NiTi
alloy based upon a special combination of mechanical and thermal training. We achieve a near-zero in-plane CTE that is smaller in value than that of the FeNi-based Invar
alloy. Atomistic simulations and theoretical calculations guide the method design and clarify the underlying mechanisms of the relationship between the processing conditions, the microstructural
evolution, and the thermal expansion behavior. The directions for further, finer adjustments of the CTE without constraints on the shape of the materials are indicated.
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All authors Won Seok Choi, Won-Seok Ko, Yejun Park, Edward L. Pang, Jong-Hoon Park, Hye-Hyun Ahn, Yuji Ikeda, Pyuck-Pa Choi, Blazej Grabowski
Mesoscale and Nanoscale Physics
Toroidal Fermi-surface geometry and phonon-limited transport in nodal-line semimetals
Aman Anand and Alessandro De Martino
Nodal-line semimetals (NLSs) can display unconventional quasiparticle dynamics and charge transport properties due to their extended band degeneracy and the peculiar geometry of their
Fermi surface. We consider electron-acoustic
phonon scattering as the dominant relaxation mechanism and compute the quasiparticle decay rate and dc conductivity by solving the linearized semiclassical Boltzmann equation in a minimal model of a doped circular NLS. We find that the toroidal geometry of the
Fermi surface gives rise to two parametrically distinct Bloch-Grüneisen temperatures, associated with momentum transfers along the poloidal and toroidal directions, respectively. As a result, an intermediate temperature window opens between these two scales, in which the decay rate follows $\Gamma\propto T^2$, while the conductivity follows $\sigma\propto T^{-2}$. We also obtain the low- and high-temperature asymptotic behaviors, and discuss implications for ARPES and transport measurements in candidate NLS materials.
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Mesoscale and Nanoscale Physics · Cross-listed from quant-ph
Impossibility of Refrigeration and Engine Operation in Minimal Qubit Repeated-Interaction Models
Gabrielle Barsky-Giles, Alessandro Prositto, Matthew Gerry et al. (+1 more)
We investigate the operation of a
qubit as a
quantum thermal device within the repeated interaction framework, allowing for strong system-bath coupling and finite interaction times. We analyze two minimal models: an alternating-coupling setup, in which the
qubit sequentially interacts with hot and cold baths, and a simultaneous-coupling setup, where both baths interact with the
qubit during each collision. For the alternating model, we obtain an exact analytical solution for the limit-cycle state, valid for arbitrary coupling strengths and collision durations. Using this solution, we rigorously prove a no-go
theorem for
quantum refrigeration. We further demonstrate that, although work can be generated locally at individual system-bath contacts, the total work over a cycle is always nonpositive, precluding engine operation. In the absence of work, the model describes pure heat conduction, for which we derive a closed-form expression for the heat current and show that it exhibits a nonmonotonic turnover behavior. The simultaneous-coupling model is analyzed perturbatively. In the short-collision-time limit, it reproduces the same steady-state behavior as the alternating model, reinforcing the generality of the constraints identified. Our results establish fundamental limitations on
qubit-based
quantum thermal machines operating under Markovian repeated interactions and highlight the need for enriched models to realize functional
quantum thermal devices.
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All authors Gabrielle Barsky-Giles, Alessandro Prositto, Matthew Gerry, Dvira Segal
Quantum Gases · Cross-listed from hep-th
On self-dualities for scalar $\phi^4$ theory
Paul Romatschke
Scalar field theory is studied by constructing interacting saddle point expansions in the symmetric and broken phase, respectively. Focusing on analytically tractable saddle expansions, it is found that broken and symmetric phases are related by sign flip of the quartic coupling. Applications to dimensions $d<4$ recover previous results for the phase diagram, whereas $d=4$ is possibly new.
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Mesoscale and Nanoscale Physics
Anomalous Edge Contrast in Magnetic Force Imaging of 2D Topological Insulators
Timothy W. Carlson, Motahhare Mirhosseini, Allison Swyt et al. (+10 more)
Magnetic force imaging (MFM) of 2D topological
insulators, such as crystalline plates of Bi2Te3 and Bi2Se3, reveals anomalously large contrast around the plate edges even under ambient imaging conditions. And while there are several known contrast mechanisms in MFM, these cannot easily explain the magnitude of contrast observe for such systems. In this work we use multi-parameter
magnetic force phase imaging together with a simple analytical model to suggest that the observed
magnetic edge-features can be associated with two sources: a pseudo-Faraday effect in the conductive edge channels of the topological system and
magnetic scattering in those channels caused by the proximal, localized field of the tip. These results suggest novel MFM contrast mechanisms unique to topological systems and point to the utility of multi-parameter MFM techniques in studies of topological-material-based
circuit elements.
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All authors Timothy W. Carlson, Motahhare Mirhosseini, Allison Swyt, Gabriel Marcus, Daniel Lazega, Scott McGuire, Lindsey J. Gray, Anthony Walsh, Swathi Kadaba, Maria Kolesnik-Gray, C.C. Dun, Vojislav Krstic, D. L. Carroll
Statistical Mechanics
Analytical solutions for a charged particle with white, thermal, and active noises in the presence of a uniform magnetic field
Y. J. Kang, S. K. So and Kyungsik Kim
We study the two-dimensional equations of motion for a charged particle subjected to white, thermal, and active noises in uniform a
magnetic field. By deriving the corresponding Fokker Planck equation, analytical solutions for the joint probability density are obtained in different time domains.
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Statistical Mechanics · Cross-listed from quant-ph
Higher-order spatial photon interference versus dipole blockade effect
Arthur Rotari and Mihai A. Macovei
The steady-state
quantum dynamics of three dipole-dipole coupled two-level emitters, fixed at the vertices of an equilateral triangle, and interacting via the environmental thermostat is investigated. We have analytically obtained the populations of the involved three-atom cooperative states as well as of the second- and third-order spatial
photon correlation functions of the light scattered by the few-
qubit sample. As a consequence, we have demonstrated that this incoherently excited system spontaneously generates streams of single
photons possessing sub-Poissonian
photon statistics. In analogy to the dipole-dipole blockade, one may expect that at smaller inter particle distances, compared to the
photon emission wavelength, the reported phenomenon has the same origin. However, we have shown that the
quantum photon features are due to the interaction's nature of the few symmetrically arranged two-level emitters with the surrounding thermal reservoir. Respectively, at larger atomic intervals the effect occurs because of high-order spatial interference phenomena. Sub-wavelength interference fringes can be observed too, via measurements of spatial higher-order
photon correlation functions.
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Materials Science
Martensitic laminate geometry controls electronic phase transitions in a Mott insulator
Ziming Shao, Benjamin Gregory, Suchismita Sarker et al. (+4 more)
Symmetry-lowering structural phase transitions result in multiple degenerate structures whose coexistence is determined by macroscopic strain compatibility. In
quantum materials, these structural transformations often couple to electronic degrees of freedom, yet how the structural arrangements influence electronic phase transitions remains poorly understood. By analyzing hundreds of diffraction peaks from X-ray reciprocal space mapping, we determine the
lattice basis vectors and mutual orientations of all coexisting phases in epitaxial V2O3 thin films after a
symmetry-lowering transformation coincident with a metal-
insulator transition. We identify the orientations of interfaces between all coexisting structures using the theory of martensitic phase transformations and find that the low temperature structure comprises finely tuned layered mixtures of alternating twin variants, akin to metal
alloys. By comparing films grown on various substrate orientations, we show that the metal-
insulator transition temperature increases monotonically with the degree to which these layered mixtures satisfy macroscopic strain compatibility imposed by the substrate.
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All authors Ziming Shao, Benjamin Gregory, Suchismita Sarker, Jacob Ruff, Ivan K. Schuller, Yoav Kalcheim, Andrej Singer
Materials Science
Large Pyroelectric Enhancement in Freestanding Epitaxial BaTiO3 Membranes on Si
Ajay Kumar, Asraful Haque, Shubham Kumar Parate et al. (+5 more)
Ferroelectric membranes transferred onto arbitrary substrates provide reduced mechanical clamping at the interfaces that can diminish the effective polarization-rotation barrier offering a pathway to engineer larger electromechanical and thermally driven responses in
oxide electronics. Here, we report integration of single crystalline thin film BaTiO3 (BTO) ferroelectric membrane on Si and demonstrate a 4x at 30C and 34x at 60C enhancement of pyroelectric coefficient compared to clamped films. The BTO membrane is grown epitaxially on a water-soluble Sr3Al2O6 sacrificial layer, released by selective dissolution, and transferred onto Si, yielding a strain-relaxed membrane with robust intrinsic polarization. Temperature dependent piezoresponse force microscopy (PFM) reveals pronounced thermally driven
evolution of domain orientation, consistent with reduced barriers for dipolar modulation in the freestanding state. Variable-temperature Kelvin probe force microscopy (KPFM) quantifies an effective pyroelectric coefficient of ~75 uC/m^2K at 30C and 450 uC/m^2K at 60C with a detectivity of 40 m^2K^-1at room temperature. These results establish lead-free freestanding BTO membranes as a promising silicon-integrable platform for cryogen-free infrared detection and waste-heat energy management.
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All authors Ajay Kumar, Asraful Haque, Shubham Kumar Parate, Harshal DSouza, Jishnu NK, Binoy Krishna De, Srinivasan Raghavan, Pavan Nukala
Statistical Mechanics
Emergence of generic first-passage time distributions for large Markovian networks
Julian B. Voits and Ulrich S. Schwarz
First-passage times are often the most relevant aspect of a complex Markovian network because they signify when information processing has resulted in a definite decision. Previous studies have shown that for kinetic proofreading networks in the limit of large network size the first-passage time distribution converges either to a delta or to an exponential distribution. Remarkably, these two forms correspond to the two extreme distributions of minimal and maximal
entropy for a fixed mean, respectively. Here we build on the connection between first-passage times and graph theory to show that these two limits are not model-specific, but arise generically in Markovian networks from the distribution of the
eigenvalues of the generator
matrix. A deterministic peak emerges when infinitely many
eigenvalues contribute, while the exponential limit arises from a single dominant
eigenvalue. We also show that the exponential limit emerges robustly for reversible networks when the mean first-passage time from the initial state to the target state becomes much larger than the mean first-passage time in the reverse direction. In contrast, the deterministic limit is not obtained from a simple reversal of this condition, but follows from a non-vanishing conductance or a mean-residual lifetime of the process which becomes small compared to the mean first-passage time in the long-time limit. This reveals a fundamental asymmetry between the two regimes. Our theoretical analysis is illustrated and validated by computer simulations of one-step master equations and random networks.
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Materials Science
Phonon assisted light absorption and emission in cubic-Boron Nitride
Ashwin Pillai, Elena Cannuccia, Aurelien Manchon et al. (+2 more)
Cubic boron nitride (cBN) is a wide-bandgap polymorph of boron nitride whose optical response remains only partially understood due to the coexistence of indirect electronic transitions and strong
exciton-
phonon coupling. Using first-principles many-body perturbation theory, we investigate the optical properties of cBN by combining GW quasiparticle corrections with Bethe-Salpeter equation calculations of excitonic effects.
Phonon-assisted absorption and emission processes are explicitly included through the
exciton-
phonon coupling formalism. We find that
phonon-mediated optical transitions provide a dominant contribution to both absorption and luminescence spectra, partially reconciling the discrepancy between the theoretical optical gap ($\simeq$ 11 eV) and experimental emission around 6-7 eV. Our results demonstrate the importance of including
exciton-
phonon interactions for the correct interpretation of experimental spectra, offering new insights into light emission in wide-bandgap materials.
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All authors Ashwin Pillai, Elena Cannuccia, Aurelien Manchon, Fulvio Paleari, Claudio Attaccalite
Materials Science
Quadrupole formation and coupling to magnetic and structural degrees of freedom in the $5d^1$ double perovskites Ba$_2$MgReO$_6$ and Ba$_2$NaOsO$_6$
Francesco Martinelli and Claude Ederer
We investigate the interplay between charge,
magnetic, and structural degrees of freedom in the isostructural and isoelectronic $5d^1$ double-perovskites Ba$_2$MgReO$_6$ and Ba$_2$NaOsO$_6$. Using first-principles-based electronic structure calculations, we show that both materials exhibit a tendency toward spontaneous quadrupolar order in the cubic paramagnetic phase, which is slightly weaker in Ba$_2$NaOsO$_6$ than in Ba$_2$MgReO$_6$. Our analysis further reveals an intimate coupling between the local
magnetic moments and charge quadrupoles, mediated by the strong
spin-orbit interaction, that leads to the unusual canted configuration of
magnetic moments observed in these systems. When structural degrees of freedom are included, the two materials exhibit pronounced differences. In Ba$_2$MgReO$_6$ the strong coupling to Jahn-Teller distortions stabilizes the antiferroic $\mathcal{Q}_{x^2-y^2}$ order, yielding excellent agreement with available experimental data. In contrast, the Jahn-Teller coupling is significantly weaker in Ba$_2$NaOsO$_6$ and appears insufficient to stabilize the antiferroic quadrupolar order. While this is consistent with the absence of any measurable long-range structural distortion above the
magnetic transition temperature, it contrasts with experimental results indicating a strong canting of the
magnetic moments. Our analysis thus successfully describes the mechanisms shaping the properties of the Re-compound while a full quantitative description of the
magnetic ground state of Ba$_2$NaOsO$_6$ is still elusive.
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Materials Science
RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures
Nathan Muetzel, Viet Luu, Sara Bey et al. (+5 more)
The use of reflection high energy electron diffraction (RHEED) plays a critical role for in-situ characterization in molecular beam epitaxy, pulsed
laser deposition and sputtering. While sensitive to
crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is therefore difficult to incorporate into the growth strategy in real-time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity towards real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its reached accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely Volmer Weber, Stransky-Krastanov and Frank-van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of Stransky-Krastanov growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology.
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All authors Nathan Muetzel, Viet Luu, Sara Bey, Muhsin Abdul Karim, Kota Yoshimura, Xinyu Liu, Marwan Gebran, Badih A. Assaf
Strongly Correlated Electrons
T-linear specific heat in pressurized and magnetized Shastry-Sutherland Mott insulator SrCu2(BO3)2
Jing Guo, Pengyu Wang, Cheng Huang et al. (+13 more)
The pressurized Shastry-Sutherland Mott
insulator SrCu2(BO3)2 has been found to host a plaquette-singlet phase and an
antiferromagnetic phase that break different
symmetries spontaneously.The recent experiment showed that their transition is of a first order nature, which seems against the pursuit of exotic and deconfined degrees of freedom in this famous frustrated
quantum magnet. We found a new direction in this study. By applying a
magnetic field to the material, we discover that SrCu2(BO3)2 exhibits a universal and metallic T-linear specific heat behavior in a large magnetitic field range close to the pressure of zero-field first order transition between plaquette-singlet and
antiferromagnetic phases. Such an unexpected gapless response from an electronically gapped Mott
insulator could be attributed to magnetized Dirac spinons liberated by the combined effect of
magnetic field and pressure, consistently seen from our
quantum many-body thermal tensor network computation of the Shastry-Sutherland model under
magnetic field. Such a robust and universal T-linear specific heat phase points out the richness of the phase diagram of the material expanded by the axes of pressure and
magnetic field and is calling for new theoretical frameworks to its full explanation.
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All authors Jing Guo, Pengyu Wang, Cheng Huang, Chengkang Zhou, Menghan Song, Xintian Chen, Ting-Tung Wang, Wenshan Hong, Shu Cai, Jinyu Zhao, Jinyu Han, Yazhou Zhou, Qi Wu, Shiliang Li, Zi Yang Meng, Liling Sun
Soft Condensed Matter
Properties of Liquid Crystalline Elastomer Foams
Oliver Dai, Andrew Terentjev and Eugene M. Terentjev
We investigate how controlled foaming alters the mechanical dissipation of
liquid crystalline elastomers (LCEs). Using thermal expandable microspheres, we generate homogeneous foams with precisely tuned bubble volume fractions up to 13% and compare their behaviour with non-mesogenic silicon analogues. We show that microsphere expansion induces a particle-centred mesogenic interphase arising from local elastic distortion and preferential
alignment of mesogenic units at the inclusion surface. At low bubble volume fraction (0.5 to 5%), these interfaces remain spatially isolated and produce a pronounced no-monotonic enhancement of damping, with the loss factor reaching tan-delta=0.2 even in the isotropic regime. At higher loading, interphase overlaps and mechanical constraints suppress this effect, and the dissipation returns towards baseline elastomeric values. Large-strain tensile tests and impact experiments exhibit the same non-monotonic trend, demonstrating that low density LCE forms achieve the highest mechanical energy absorption per unit mass. Compared with conventional high porosity
polymer foams used for acoustic damping, these materials retain sufficient mechanical integrity to sustain impact loads, establishing a microstructural route to engineer high-performance damping in soft solids.
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