Computational Physics
Multiphysics Modelling of the Molten Salt Fast Reactor using NekRS and the Fission Matrix Method
Maximiliano Dalinger, Elia Merzari and Saya Lee
The Molten Salt Fast Reactor (MSFR) has the particularity that the coolant is also the fuel, which tightens the coupling between neutronics and thermal hydraulics as the fuel circulates through the primary system. Therefore, developing computational models to analyze the MSFR requires a multiphysics approach. In this paper, we propose developing a neutronic thermal-hydraulic computational model of the MSFR that uses a reduced-order model to solve the neutronics equations. The principal computational tool chosen for this purpose is the high-fidelity code Cardinal, a wrapping within the MOOSE framework that integrates the Computational Fluid Dynamics code NekRS and the Monte Carlo particle transport code OpenMC. However, we use the Fission
Matrix (FM) Method to solve the neutronics equations instead of OpenMC. The FM method can perform fast and still accurate neutronics simulations. It relies on precalculated databases obtained through a Monte Carlo simulation.
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Plasma Physics
Suppression of Electromagnetic Pulses from Laser-Target Interactions by Strong Magnetic Fields
P. V. Heuer, J. L. Peebles, J. R. Davies et al. (+12 more)
Laser-target interactions generate intense electromagnetic pulses (EMP) that can interfere with measurements and damage equipment. In this paper we show that applying a
magnetic field to nanosecond pulse
laser-target interactions decreases the magnitude of EMP. We demonstrate this effect in two experiments with different geometries (spherical vs. planar),
laser intensities (${\sim}10^{13}$ vs. ${\sim} 10^{15}$~W/cm$^2$) and applied field strength (12~T vs. 0.1~T) that both observed suppression of EMP in the ${\sim} 1$~GHz band (by factors of $0.65\times$ and $0.32\times$ respectively). We then observe the opposite effect at high intensities with a picosecond pulse: for planar experiments with
laser intensities ${\sim}10^{19}$~W/cm$^2$ and
magnetic fields of 6–10~T, the magnitude of EMP is increased by a factor of $1.75\times$. These results provide a
benchmark for models of EMP generation, but suggest that
magnetic fields are not a viable solution for mitigating EMP on the high intensity
laser facilities where it is most damaging.
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All authors P. V. Heuer, J. L. Peebles, J. R. Davies, D. H. Barnak, B. Stanley, N. Pelepchan, M. Cufari, J. A. Frenje, C. Niemann, N. A. Rongione, C. Constantin, E. Cisneros, P. Pribyl, H. Sio, H. Chen
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP
The impact of electron precipitation on Earth's thermospheric NO production and the drag of LEO satellites
M. Scherf, S. Krauss, G. Tsurikov et al. (+6 more)
We investigate the response of space weather events on Earth's upper atmosphere over the polar regions by studying their effect on the drag of the CHAMP and GRACE satellites. Increasing
solar activity that results in heating and the expansion of the upper atmosphere threatens low Earth orbit (LEO) satellites. Auroral events are closely related to the
stellar energy deposition of
solar EUV radiation and precipitating energetic electrons, which influence photochemical processes such as the production of nitric
oxide (NO) in the upper atmosphere. To study the production of NO molecules and their influence on the thermospheric structure and satellite drag, we first model Earth's background thermosphere with the 1D upper atmosphere model Kompot by considering the incident X-ray, EUV, and IR radiation during selected space weather events. To investigate the effect of electron precipitation in the production of NO molecules in the polar thermosphere, we apply a Monte Carlo model accounting for the
stochastic nature of collisional scattering of auroral electrons in collisions with the surrounding N$_2$-O$_2$ atmosphere, including the production of suprathermal N atoms. The observed effect of the atmospheric drag on CHAMP and GRACE during the two studied events indicates that a sporadic enhancement of NO molecule production in the polar thermosphere and its IR-cooling capability, which counteracts thermospheric expansion and can lead to an “overcooling” with decreased density after the space weather event, can have a protective effect on LEO satellites. Their production efficiency, however, is highly dependent on the energy flux of the precipitating electrons. Our results have direct implications for empirical satellite orbit prediction models, as our simulations highlight the need to consider precipitation-induced NO production to improve the predictive power of these models.
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All authors M. Scherf, S. Krauss, G. Tsurikov, A. Strasser, V. Shematovich, D. Bisikalo, H. Lammer, M. Güdel, C. Möstl
Chemical Physics
Molecular g-Tensors From Spin-Orbit Quasidegenerate N-electron Valence Perturbation Theory: Benchmarks, Intruder-State Mitigation, and Practical Guidelines
Nicholas Yiching Chiang, Rajat Majumder and Alexander Yu. Sokolov
Accurate prediction of molecular g-tensors for open-shell systems requires a balanced treatment of multireference electron correlation and relativistic
spin-orbit coupling. Here, we develop and
benchmark spin-orbit quasidegenerate second-order N-electron valence perturbation theory (SO-QDNEVPT2) for g-tensor calculations, treating dynamical correlation and
spin-orbit effects consistently within a multistate effective
Hamiltonian framework. Two g-tensor approaches are implemented: a
spin-free effective
Hamiltonian (EH) approach based on second-order response and a Kramers (K) approach that extracts g from
spin-mixed SO-QDNEVPT2 states. We assess their performance on a
benchmark set of 23 molecules spanning diatomics and small polyatomics, low- to high-
spin species, and weak to strong
spin-orbit coupling. Across the
dataset, SO-QDNEVPT2 improves agreement with experiment relative to state-averaged complete active-space self-consistent field. The EH and K formalisms agree for modest g-shifts but the Kramers approach becomes essential when the shifts become large. We demonstrate that QDNEVPT2 results can be sensitive to intruder-state instabilities that can be effectively mitigated with level-shift or renormalization techniques. We then analyze the dependence of SO-QDNEVPT2 results on key computational parameters, including active space, number of states, state-averaging weights, gauge origin, and basis set. These results establish SO-QDNEVPT2 as a robust framework for computing g-tensors in correlated, relativistic open-shell molecules, offering practical guidelines for its applications.
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Physics and Society · Cross-listed from cs.LG
Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
Sheng Ran
Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has been extraordinarily successful for well-defined tasks where goals are fixed and evaluation criteria are explicit. However, if artificial systems are to move toward true autonomy-operating over long horizons and across evolving contexts-objectives may become ill-defined, shifting, or entirely absent. In such settings, a fundamental question emerges: in the absence of an explicit objective function, how can a system determine whether its ongoing internal dynamics are productive or pathological? And how should it regulate structural change without external supervision? In this work, we propose a dynamical framework for learning without an explicit objective. Instead of minimizing external error signals, the system evaluates the intrinsic health of its own internal dynamics and regulates structural plasticity accordingly. We introduce a two-timescale architecture that separates fast state
evolution from slow structural adaptation, coupled through an internally generated stress variable that accumulates evidence of persistent dynamical dysfunction. Structural modification is then triggered not continuously, but as a state-dependent event. Through a minimal toy model, we demonstrate that this stress-regulated mechanism produces temporally segmented, self-organized learning episodes without reliance on externally defined goals. Our results suggest a possible route toward autonomous learning systems capable of self-assessment and internally regulated structural reorganization.
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Atomic Physics · Cross-listed from quant-ph
Four- and six-photon stimulated Raman transitions for coherent qubit and qudit operations
Gabriel J. Gregory, Evan R. Ritchie, Alex Quinn et al. (+4 more)
Quantum computers are typically composed of an array of two-level systems, or
qubits, encoded in some information carrier, such as an electron,
photon, or quantized
circuit. The size of this array is restricted by finite access to resources like
laser power, cooling capacity, and control lines for trapping and manipulation. Under these constraints, the system's processing power can be increased by using more energy levels per information carrier, but common techniques for
qubit control provide only limited connectivity between these additional states. We experimentally demonstrate transitions between electronic angular momentum states with a difference in
magnetic quantum numbers $\Delta \mathrm{m_J} = $ 3, 4, and 5 via resonant four- and six-
photon stimulated Raman transitions in a single trapped atom. Derivation of the corresponding Rabi frequencies, which are verified experimentally, follows the standard treatment of two-
photon transitions including the adiabatic elimination of intermediate states. Finally, we discuss pathways to increase the observed multi-
photon transition fidelities to $>99.99\%$, providing a tool for efficient, high-fidelity control of qudits and single-atom logical
qubits.
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All authors Gabriel J. Gregory, Evan R. Ritchie, Alex Quinn, Sean Brudney, David J. Wineland, David T. C. Allcock, Jameson O'Reilly
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Tuning of Atomic Layer Deposition Pulse Time through Physics-Informed Bayesian Active Learning
Pouyan Navabi and Christos G. Takoudis
Atomic Layer Deposition (ALD) process development is often hindered by time-consuming and precursor-intensive tuning cycles required to identify saturation conditions. We introduce a physics-informed
Bayesian active learning (BAL) framework that autonomously tunes precursor pulse times by integrating a Langmuir adsorption model directly into the Gaussian Process (GP) kernel. A key innovation is a two-stage parameter estimation strategy that decouples noise filtering from physical parameter extraction: the GP first smooths noisy data through standard prediction, then Langmuir parameters are fitted to the noise-filtered GP predictions. This approach effectively separates signal from experimental noise. We evaluate the framework against a standard data-driven GP across four simulated regimes, demonstrating convergence within five iterations, up to fourfold improvement in prediction accuracy, and two to fourfold reduction in precursor usage. Experimental validation using TiO2 deposition via Tetrakisdimethylamido Titanium (TDMAT) and ozone confirms that the physics-informed model accurately identifies saturation times for high-coverage targets ($\geq$95%), with observed deviations at lower saturation levels providing valuable insight into non-ideal desorption behaviors.
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Chemical Physics
From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark
Shuquan Ye, Ben Fei, Hongbin Xu et al. (+2 more)
The Solid Electrolyte Interphase (SEI) is critical to the performance of lithium-ion batteries, yet its analysis via Operando Infrared (IR) spectroscopy remains experimentally complex and expensive, which limits its accessibility for standard research facilities. To overcome this bottleneck, we formulate a novel task, Operando IR Prediction, which aims to forecast the time-resolved
evolution of spectral “fingerprints” from a single static spectrum. To facilitate this, we introduce OpIRSpec-7K, the first large-scale operando
dataset comprising 7,118 high-quality samples across 10 distinct battery systems, alongside OpIRBench, a comprehensive evaluation
benchmark with carefully designed protocols. Addressing the limitations of standard spectrum,
video, and sequence models in capturing voltage-driven chemical dynamics and complex composition, we propose Aligned Bi-stream Chemical Constraint (ABCC), an end-to-end physics-aware framework. It reformulates MeanFlow and introduces a novel Chemical Flow to explicitly model reaction trajectories, employs a two-stream disentanglement mechanism for solvent-SEI separation, and enforces physics and spectrum constraints such as mass conservation and peak shifts. ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines. ABCC even generalizes to unseen systems and enables interpretable downstream recovery of SEI formation pathways, supporting AI-driven electrochemical discovery.
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All authors Shuquan Ye, Ben Fei, Hongbin Xu, Jiaying Lin, Wanli Ouyang
Space Physics
The Free-Electron Laser Model of Magnetospheric Chorus
Brandon Bonham
Chorus waves are electromagnetic waves named for their resemblance to birds chirping at dawn when their
radio frequencies are played as audio. The amplification of chorus in Earth's magnetosphere has been the subject of intense scientific inquiry since the discovery of the Van Allen radiation belts in 1958. Resonant interactions between chorus and radiation belt electrons can lead to the exponential growth of small seed waves by a factor of fifty within milliseconds. These powerful modes can cause rapid acceleration of electrons and endanger space-based technologies. Recent efforts to understand chorus amplification have drawn upon parallels to free-electron
lasers, laboratory devices that generate intense coherent light with tunable frequencies. This approach, known as the free-electron
laser model of magnetospheric chorus, is the subject of this dissertation. In this work, we build on previous research on the free-electron
laser model, ultimately presenting a novel nonlinear model of whistler-mode chorus in the magnetosphere. In the first chapter, we provide a brief introduction to whistlers, magnetospheric chorus, and free-electron
lasers. We also derive the 2N+2 equations foundational to the interaction of chorus with N resonant electrons. In the second chapter, we derive a reduced set of just three nonlinear equations using the method of collective variables. We then derive a Ginzburg-Landau equation (GLE) for the behavior of a chorus wave packet with a spectrum of frequencies with spatially varying amplitudes and discuss the prediction of solitary chorus waves. In the third chapter, we focus on the behavior of the single-mode solutions predicted by the GLE, including their linear stability and the phenomenon of mode condensation, where a single mode can emerge from a noisy spectrum. In the final chapter, we summarize the results and discuss open questions and future directions.
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Optics
Reconfigurable Geometric Phase Matching by Multilayered Nonlinear Thin-Film Crystals
Danielle Ben-Haim, Mai Tal, Xiaoxi Xu et al. (+1 more)
Phase matching is essential for efficient energy transfer in nonlinear wave-mixing processes. Traditional methods, such as birefringent and quasi-phase matching, have remained conceptually unchanged since their discovery over 60 years ago, each posing inherent constraints and limitations. Here, we demonstrate the concept of
geometric phase matching as a new paradigm for tunable nonlinear wave mixing, based on a multilayered platform of nonlinear thin-film
crystals. We leverage this concept to experimentally show reconfigurable and
spin-controlled phase matching for second-harmonic generation (SHG), opening new avenues for real-time manipulation of nonlinear interactions in photonic devices. We specifically demonstrate full modulation of SHG from a
bilayer structure, nearly perfect and tunable
geometric phase matching from an eight-layer structure, and polarization
tomography that reveals the
evolution of the
spin dependent interaction. This approach not only expands the design space for nonlinear optical processes but also paves the way for highly robust, tunable and efficient frequency conversion, for next-generation adaptive nonlinear photonic,
quantum photonic and nonlinear optical metamaterial technologies based on thin-film
crystals.
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All authors Danielle Ben-Haim, Mai Tal, Xiaoxi Xu, Tal Ellenbogen
Optics
Pole-Expansion of the T-Matrix Based on a Matrix-Valued AAA-Algorithm
Jan David Fischbach, Fridtjof Betz, Lukas Rebholz et al. (+6 more)
The transition
matrix (T-
matrix) is a complete description of an object's linear scattering response. As such, it has found wide adoption for the theoretical and computational description of multiple-scattering phenomena. In its original form, the T-
matrix describes the interaction of a scatterer with a monochromatic source. In practice, however, information about the T-
matrix is usually needed in an extended spectral domain. To access the frequency-dispersion, one might naively sample T-
matrices over a finely resolved set of discrete frequencies and store one T-
matrix per frequency. This approach has multiple drawbacks: it is computationally expensive, requires excessive
memory, and it disregards the physical origin of the spectral features, weakening physical interpretability. To overcome these major limitations, we leverage a pole-expansion technique to represent the T-
matrix with arbitrary frequency resolution within a selected frequency domain via a set of resonant contributions. A
matrix-valued variant of the recently established adaptive Antoulas-Anderson (AAA)
algorithm for rational approximation enables us to compute the pole-expansion at minimal computational cost using only a small number of direct evaluations. We demonstrate the benefits of such a representation with examples ranging from semi-analytically accessible scatterers to quasi-dual bound states in the continuum. To allow the wider community to capitalize on these findings, we provide open-source tools to perform the presented pole-expansion of the T-
matrix.
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All authors Jan David Fischbach, Fridtjof Betz, Lukas Rebholz, Puneet Garg, Kristina Frizyuk, Felix Binkowski, Sven Burger, Martin Hammerschmidt, Carsten Rockstuhl
Chemical Physics
The coherent-state transformation in quantum electrodynamics coupled cluster theory
Eric W. Fischer
We analyse the coherent-state (CS) transformation in
quantum electrodynamics coupled cluster (QED-CC) theory from the perspective of its non-vanishing commutator with the polaritonic cluster operator. Specifically, we show that a QED Hartree-Fock (QED-HF) reference state parametrized by the CS transformation leads to a QED-CC Lagrangian formally determined by CS-representations of polaritonic
Hamiltonian, polaritonic cluster and polaritonic deexcitation operators. Moreover, the herein proposed approach differs from the original formulation of QED-CC theory in the definition of the
photon state basis and exploits
photon-added coherent states in contrast to previously considered displaced number states. We find a renormalization of both QED-CC correlation energy and QED-CC ground state induced by the CS transformation, which depends on the mean-field expectation value of the molecular dipole operator and therefore breaks origin invariance for charged systems. Electronic contributions to correlation energy and QED-CC ground state are renormalized by CS-transformed mixed excitation and deexcitation operators. In contrast, the CS-transformed single-
photon excitation affects only the QED-CC ground state but not directly the correlation energy. The renormalized QED-CC ansatz becomes similar to the original QED-CC formulation for large cavity frequencies leading to small renormalization corrections. A divergent correlation energy for molecules with a non-vanishing molecular dipole moment is found in the low-frequency limit, which we discuss with respect to multi-
photon excitations in the polaritonic cluster operator and the relevance of the cavity-Born-Oppenheimer framework.
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Optics
Feedback-Driven Ground-State Search in Coupled Laser Arrays
Rajneesh Fulara, Fabien Bretenaker and Vishwa Pal
Optimisation problems, which appear in numerous fields of science and industry, are challenging to solve even with modern supercomputers. Many such problems can be mapped onto ground-state searches of
spin Hamiltonians, implemented on various physical platforms whose intrinsic dynamics are analogous to
spin systems. However, the complex energy landscape of
spin Hamiltonians often traps the system in local minima, preventing the system from reaching the ground-state (global minimum). We demonstrate an intrinsic feedback-driven annealing mechanism in class-B
semiconductor laser arrays arising from the interplay of internal ($\alpha$) and external ($\eta$) coupling. The instantaneous phase configuration self-modulates amplitude fluctuations, which act as an effective temperature, dynamically reshaping the potential and enabling the system to escape from local minima. Using a one-dimensional ring
laser array, we analyze defect formation in the $\alpha$-$\eta$ parameter space and identify an optimal regime achieving nearly 100% ground-state probability. Although both $\alpha$ and $\eta$ are essential for the feedback loop, defect suppression results from modifying two competing timescales: amplitude stabilization (t_amp) and phase locking (t_phase), analogous to the Kibble-Zurek mechanism. These timescales can be tuned independently via $\alpha$ or $\eta$. Identical timescale ratios yield identical defect probabilities, confirming that relative timescales, not specific parameters, govern defect formation. Our findings establish internal feedback-driven annealing as a practical route to ground-state search in
semiconductor laser arrays, providing a foundation for efficient and scalable
laser-based
spin simulators for tackling hard optimization problems.
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Fluid Dynamics
Solutocapillary bubble centering in a confined ethanol plume in water
Tobias Baier, Steffen Bisswanger, Sebastian Dehe et al. (+1 more)
This study investigates the radial centering of gas bubbles within a buoyant plume of ethanol injected into a co-flowing water sheath flow in a vertical capillary. Bubbles nucleate in the ethanol stream due to CO$_2$ supersaturation and rapidly migrate toward the plume axis via solutocapillary (Marangoni) forces driven by interfacial tension gradients in the ethanol-water mixture. Experiments reveal that bubbles of varying sizes reliably align along the plume centerline, facilitated by steep radial concentration gradients near the plume boundary. A reduced-order model supports robust centering across a wide range of bubble radii. For larger bubbles, axial Marangoni effects modulate ascent velocities and can even induce upstream migration under transient conditions, highlighting the complex feedback between bubble dynamics and plume distortion. The results demonstrate that solutocapillary migration provides a reliable mechanism for contact-free bubble focusing, with implications for bubble manipulation in microfluidics, reactors, and phase-separation processes.
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All authors Tobias Baier, Steffen Bisswanger, Sebastian Dehe, Steffen Hardt
Applied Physics · Cross-listed from cond-mat.supr-con
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
Optics
Super-Resolution Structured-Illumination X-Ray Microscopy based on Fourier Decomposition
Stefan Schwaiger, Lennart Forster, Martin Dierolf et al. (+2 more)
X-ray microscopy has become an important tool for non-destructive testing, e.g., in battery research. However, imaging a cm-scale battery
cell at the desired (sub-)micrometer resolution has been challenging. State-of-the-art X-ray microscopy techniques with a suited field-of-view provide (sub-) $10\,\mu m$ resolution, typically limited by the detector point-spread function and the (effective) detector pixel size. This work presents a super-resolution X-ray microscopy approach overcoming both limitations. It requires a structured X-ray illumination to encode high-frequency sample information that is natively unresolved within the resolved region of support. A mathematical framework is developed that decodes this information and generates a super-resolved image from multiple acquisitions with different phase shifts of the structured X-ray illumination. The presence of this encoded high-frequency information is first experimentally demonstrated, followed by quantification and validation using a resolution test chart. A resolution improvement by a factor of 2.2 is shown. Finally, we extend the proposed super-resolution technique to X-ray microtomography. Since the image acquisition scheme is inherently
multimodal, phase-contrast and dark-field X-ray images can be computed additionally. These results showcase the direct impact of the proposed technique across both non-destructive testing and biomedical imaging, alleviating pixel-size limitations in detectors and sample-size restrictions.
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All authors Stefan Schwaiger, Lennart Forster, Martin Dierolf, Franz Pfeiffer, Benedikt Günther
Applied Physics · Cross-listed from cond-mat.mes-hall
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
History and Philosophy of Physics
C.V. Raman's Exploration in Optics – A Spectrum of History
G.V. Pavan Kumar
C.V. Raman (1888-1970) was one of the pioneering scientists to have emerged from India during the colonial era. His scientific explorations were driven by his curiosity to understand wave phenomena. Naturally, optics and related physical effects were at the heart of such an exploration. Apart from his Nobel prize-winning discovery of the Raman effect, his research included topics such as oblique diffraction, light scattering from
liquids and amorphous solids, classical and
quantum nature of light, acousto-optics,
haloes and coronae (speckles),
crystal dynamics and soft modes, optics of minerals, floral colors, physiology of
vision and many other aspects related to light in natural settings. In this article, I give a historical overview of some of the work by C.V. Raman and his group that had a direct connection to optics and optical spectroscopy.
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Medical Physics
Prompt Gamma Timing in Carbon Therapy: First Experimental Results with the TIARA Detector
Maxime Pinson, Adélie André, Yannick Boursier et al. (+11 more)
In the context of range monitoring for particle therapy, this study presents the first experimental results obtained with the TIARA detector using carbon-ion beams at the CNAO clinical center in Pavia, Italy. TIARA is based on the
Prompt Gamma Timing (PGT) technique, which measures the time of flight (TOF) between incident ions and
prompt gamma rays (PGs) emitted during
nuclear interactions in the target. While the TIARA prototype has previously been validated with
protons, carbons present a more challenging scenario due to their higher linear energy transfer,
nuclear fragmentation products, and the continuous beam time structure of synchrotron accelerators. Experiments were performed by irradiating PMMA targets of different thicknesses with 200 MeV/u carbon beams. A coincidence time resolution of 279$\pm$35 ps FWHM was achieved, outperforming results previously obtained with
protons at the same facility. A range accuracy of 4.74$\pm$0.36 mm at a 2$\sigma$ confidence level was measured at clinical intensity, when considering 5600 detected PGs, corresponding to the grouping of four irradiation spots of 2.4$\cdot$10$^6$ ions each. Overall, the results demonstrate that PGT-based range monitoring remains viable for carbon-ion beams, although increased background from secondary
protons indicates that detector configuration adaptations are required.
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All authors Maxime Pinson, Adélie André, Yannick Boursier, Mathieu Dupont, Marie-Laure Gallin Martel, Alicia Garnier, Christophe Hoarau, Pavel Kavrigin, Daniel Maneval, Christian Morel, Jean-François Muraz, Marco Pullia, Simone Savazzi, Sara Marcatili
Chemical Physics
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
Jan Pavšek, Alexander Mitsos, Elvis J. Sim et al. (+1 more)
Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermodynamic consistency. As such, thermodynamics-informed ML, i.e., incorporating thermodynamic relations into the loss function as regularization term for training, has been proposed. We herein transfer the concept of thermodynamics-informed graph neural networks (GNNs) from the Gibbs-Duhem to the Clapeyron equation, predicting several pure component properties in a multi-task manner, namely: vapor pressure,
liquid molar volume, vapor molar volume and enthalpy of vaporization. We find improved prediction accuracy of the Clapeyron-GNN compared to the single-task learning setting, and improved approximation of the Clapeyron equation compared to the purely data-driven multi-task learning setting. In fact, we observe the largest improvement in prediction accuracy for the properties with the lowest availability of data, making our model promising for practical application in data scarce scenarios of chemical engineering practice.
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All authors Jan Pavšek, Alexander Mitsos, Elvis J. Sim, Jan G. Rittig
Optics
Photonic-computing error correction through optical en-/decoder calibrations
Adam Carstensen and Babak Vosoughi Lahijani
Photonic processors have emerged as an attractive platform for fast and energy-efficient
matrix-vector multiplication. However, they are susceptible to error due to their analog nature. Here, we present an error-correction technique that implements a correction offset to the optical en-/decoders of photonic processors. Our proposed method is general-purpose, does not require introducing any additional components to the photonic network, and can address errors stemming from unbalanced losses, 50/50 beamsplitter deviations, digital-to-analog conversion inaccuracies, and any unknown sources. In particular, we show that our method is highly effective in mitigating unbalanced-loss errors, a problem that has not previously been addressed by any error-correction technique. Using this approach, we achieve over 90% error reduction in large triangular meshes, overcoming a key obstacle to highly accurate photonic processors for information processing.
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Chemical Physics
Efficient Calculation of Absorption Spectra of Platinum Complexes Used as Luminescent Probes for Cancer Detection
Lena T. T. Nguyen, Ernst D. Larsson, Kajsa M. F. Niklasson et al. (+2 more)
Despite major advances in oncology, many chemotherapeutic
agents still cause severe side effects that reduce quality of life, motivating new approaches for early detection and targeted elimination of cancer
cells. Luminescent transition metal complexes are promising biomolecular probes as their photo-physical properties are dependent on the surrounding environment. This makes it possible to differentiate between different environments and as a result, allows for identification of abnormalities in DNA. However, reliable computational protocols to predict optical properties of transition metal intercalators are limited, making accurate absorption spectra calculations essential for screening candidates. Here, we
benchmark methods for computing UV-Vis spectra of a Pt(II) pincer complex. The complex is studied both in isolation and intercalated in a small DNA model,representing probes designed to target DNA-associated molecular abnormalities. We find that the Tamm-Dancoff approximation (TDA) and the resolution of identity (RI) approximations provide a significant increase in speed for TD-DFT with only a modest loss of accuracy. Since geometry optimizavvvvvvvvvtion is often the dominant cost, PBEh-3c emerges as an efficient alternative to conventional DFT, introducing errors comparable to those from TDA. Tight-binding methods (GFN-xTB) offer further acceleration, but yield larger deviations in structures and UV-Vis spectra; thus, unless extensive optimization is required, PBEh-3c provides the best balance between accuracy and efficiency. The largest source of uncertainty stems from the exchange-correlation functional used in the TD-DFT calculation, where we obtain good results with PBE0 based on PBEh-3c structures.
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All authors Lena T. T. Nguyen, Ernst D. Larsson, Kajsa M. F. Niklasson, Erna K. Wieduwilt, Erik D. Hedegård
Optics
Spontaneous Raman scattering in SDM fibers
Lucas Alves Zischler, Giammarco Di Sciullo, Divya A. Shaji et al. (+2 more)
Spontaneous Raman scattering (SpRS) is a weak non-linear effect, particularly relevant to classical-
quantum coexistence transmission and sensing applications. In classical transmission, the relevant Raman effect is stimulated Raman scattering (SRS), and recent studies have examined it in space-division multiplexing (SDM) fibers. An intrinsic relation between SpRS and SRS allows previous SRS results to inform SpRS models. In this work, we extend SpRS models derived for single-mode fibers (SMFs) to SDM fibers with multiple mode groups of degenerate modes, covering both Stokes and anti-Stokes bands. The proposed model is a useful, fiber-design-independent tool for evaluating scattered noise in optical links, and it is validated through experimental measurements in field-deployed multi-core fibers (MCFs) and multi-mode fiber (MMF), showing good agreement.
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All authors Lucas Alves Zischler, Giammarco Di Sciullo, Divya A. Shaji, Antonio Mecozzi, Cristian Antonelli
Fluid Dynamics
Vortex breakdown in a hydro turbine draft tube swirling jet
Artur Gesla and Eunok Yim
The swirling flow in a Francis type hydropower turbine is known to be susceptible to the formation of a large helical structure, commonly referred to as a
vortex rope. This
vortex rope can be interpreted as an unstable mode associated with
vortex breakdown. This perspective is adopted here in a simplified laminar flow setting. The helical
vortex rope mode is shown to bifurcate supercritically from an axisymmetric baseflow in a
Hopf bifurcation within a turbine draft tube. When wall friction effects are neglected, a large recirculation region at the axis can form and a range of subcritical solutions is identified for a flow regime corresponding to partial load of the turbine. The existence of these subcritical solutions promotes the emergence of a hysteresis loop. We further describe a regular dynamics of a formation of recirculation bubble at the axis and its destruction due to the emergence of a helical
vortex rope at its periphery. Increasing the axial flow discharge towards the regime corresponding to nominal turbine load leads to an unfolding of the steady solutions branch in a transcritical bifurcation. This bifurcation takes place at finite Reynolds number and complements existing evidence of transcritical bifurcation of the swirling jet flows, previously reported only in the inviscid limit.
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Biological Physics · Cross-listed from cond-mat.stat-mech
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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Fluid Dynamics
Measured multiple flow states in turbulent thermal convection with aspect ratio 10
Yi-Zhen Li, Jun-Jie Huo, Xin Chen et al. (+1 more)
We report an experimental investigation of turbulent Rayleigh-Benard convection in a rectangular
cell of large aspect ratio ($\Gamma = 10$) over the Rayleigh number range $5.4\times10^7 \le Ra \le 7.2\times10^9$ and Prandtl number range $4.3 \le Pr \le 67.3$. Planar particle image velocimetry measurements show that the flow self organises into several horizontally stacked convection rolls, and repeated experiments under identical parameters (both $Ra$ and $Pr$) reveal that the number of rolls varies within the range of 3 to 7 with 6 being the most probable, which demonstrates the presence of multiple flow states. When $Pr$ is increased to 67.3, the number of roll like structures increases significantly, indicating a structural transition from a roll dominated to a plume dominated flow. This transition is reflected in the global momentum transport, for $Pr \leq 18.3$ the Reynolds number scales as $Re \sim Ra^{0.58}Pr^{-0.97}$, whereas the scaling is changed to $Re \sim Ra^{0.72}$ when $Pr$ reaches 67.3. Within individual rolls, we further examine the Reynolds numbers based on horizontal and vertical velocity components, $Re_{u,\text{roll}}$ and $Re_{w,\text{roll}}$, and find that the former increases while the latter decreases with roll size (quantified as the aspect ratio of the roll $\Gamma_\text{roll}$) due to continuity constraints, with their ratio following $Re_{w,\text{roll}}/Re_{u,\text{roll}} \sim \Gamma_\text{roll}^{-0.61}$. We impose different initial flow conditions (roll structures) with controlled perturbations, and demonstrate that the initial condition can influence the final turbulent state. We show that the number of horizontally stacked rolls regulates the global transport, larger number of rolls induces greater vertical momentum and heat transfer.
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All authors Yi-Zhen Li, Jun-Jie Huo, Xin Chen, Heng-Dong Xi
Computational Physics · Cross-listed from cond-mat.mes-hall
Electrodynamics of swift-electron momentum transfer to a large spherical nanoparticle
Jesús Castrejón-Figueroa, Jorge Luis Briseño-Gómez, Eduardo Enrique Viveros-Armas et al. (+2 more)
Swift electrons from highly focused beams produced in aberration-corrected scanning transmission electron microscopes offer a powerful route for probing and manipulating matter at the nanoscale. Although linear momentum transfer from swift electrons to nanoparticles has been investigated theoretically and experimentally, subsequent analyzes revealed that several earlier predictions relied on non-causal dielectric functions or insufficient numerical convergence, leading to spurious sign reversals in the transferred momentum. Here, we derive analytical expressions and develop a numerically efficient electrodynamic framework to compute the linear momentum transferred from a swift electron to an isolated spherical nanoparticle described by a fully causal, local dielectric response. We apply our framework to large nanoparticles with 50 nm radius and explicitly resolve the spectral density of linear momentum transfer across the full frequency domain. Using causal dielectric functions for aluminum and bismuth, we analyze the role of electron velocity, impact parameter, and material-specific resonances. We find that, when causality and full multipolar convergence are enforced, the net transverse linear momentum transferred to spherical nanoparticles remains attractive toward the electron trajectory for all nanoparticles considered, despite the presence of material-dependent sign changes in individual electric and
magnetic contributions. These results contrast with earlier theoretical predictions of net repulsive behavior and indicate that additional physical mechanisms beyond the present isolated, local description are required to account for experimentally observed repulsion. Our work establishes a robust reference framework for momentum transfer calculations and provides quantitative
benchmarks relevant for electron-beam-based nanoscale manipulation.
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All authors Jesús Castrejón-Figueroa, Jorge Luis Briseño-Gómez, Eduardo Enrique Viveros-Armas, José Ángel Castellanos-Reyes, Alejandro Reyes-Coronado
Instrumentation and Detectors · Cross-listed from astro-ph.CO
First Dark Photon Search Results from the Dandelion Experiment
I. Ourahou, S. Savorgnano, C. Beaufort et al. (+9 more)
This paper presents the first results from the Dandelion experiment, a directional detection, which searches for 1 meV dark
photon dark matter. We use a spherical mirror to convert dark
photons into standard millimeter-wavelength
photons that can then be detected with an array of 221 Kinetic Inductance Detectors (KIDs) cooled down to 150 mK within the KISS-NIKA camera ( (KIDs Interferometric Spectral Surveyor) and operating between 150 and 350 GHz. We used 1480 minutes of data to search for the signal of dark
photons in the KID detectors, which is expected to be modulated due to the Earth's rotation. Our main challenge was to deal with a large background from room temperature and stray-light fluctuations. We used a de-correlation analysis to remove these background fluctuations. Templates of the background fluctuations were constructed from a Principal Component Analysis decomposition of detector measurements outside the expected Field of View trajectory of dark
photons. We found that the dark
photon signal was consistent with zero, giving a new upper limit on the dark
photon's kinetic mixing, $\chi$, with masses between 0.6 meV and 1.4 meV. These are the first constraints on dark
photons as a dark matter candidate using an array of KIDs at millimeter wavelength.
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All authors I. Ourahou, S. Savorgnano, C. Beaufort, M. Bastero-Gil, J. Bounmy, A. Catalano, J. Macias-Perez, D. Santos, C. Smith, F. Naraghi, D. Tourres, F. Vezzu
Computational Physics · Cross-listed from cond-mat.str-el
Machine-learning force-field models for dynamical simulations of metallic magnets
Gia-Wei Chern, Yunhao Fan, Sheng Zhang et al. (+1 more)
We review recent advances in machine learning (ML) force-field methods for Landau-Lifshitz-Gilbert (LLG) simulations of itinerant electron magnets, focusing on scalability and transferability. Built on the principle of locality, a deep neural network model is developed to efficiently and accurately predict the electron-mediated forces governing
spin dynamics.
Symmetry-aware descriptors constructed through a group-theoretical approach ensure rigorous incorporation of both
lattice and
spin-rotation
symmetries. The framework is demonstrated using the prototypical s-d exchange model widely employed in spintronics. ML-enabled large-scale simulations reveal novel nonequilibrium phenomena, including anomalous coarsening of tetrahedral
spin order on the triangular
lattice and the freezing of phase separation dynamics in lightly hole-doped, strong-coupling square-
lattice systems. These results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium
spin dynamics in itinerant magnets.
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All authors Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
A Computational Study of Organic Molecular Crystals for Photocatalytic Water Splitting
James D. Green, Daniel G. Medranda, Hong Wang et al. (+3 more)
Organic crystalline materials are potential candidates for photocatalytic overall water splitting (OWS). Although organic
crystals have been heavily investigated for application in organic electronics, such as organic light-emitting diodes (OLEDs) and
solar cells, there have been comparatively fewer studies into OWS in these materials. A major challenge is the large number of electronic and structural criteria that must be met for a material to make a viable OWS photocatalyst. Optical absorption, reduction and oxidation potentials and charge-transport properties are among the key considerations, and these are influenced both by molecular properties and the solid-state packing arrangement, making computational modelling challenging. Here, we investigate a series of known organic electronic materials that have published
crystal structures using periodic density functional theory (DFT) and compare their calculated electronic properties of optical absorption and reduction and oxidation potentials with literature experimental data. Furthermore we perform a series of gas-phase molecular calculations which show a good agreement with literature data and periodic DFT for the optoelectronic properties of the organic molecular
crystals studied, showing that gas-phase molecular calculations could be used to screen organic
crystals for OWS at a reduced computational cost.
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All authors James D. Green, Daniel G. Medranda, Hong Wang, Andrew I. Cooper, Jenny Nelson, Kim E. Jelfs