Fluid Dynamics · Cross-listed from astro-ph.EP
The influence of a stably stratified layer on the hydromagnetic waves in the Earth's core and their electromagnetic torques
Fleur Seuren, Santiago A. Triana, Jérémy Rekier et al. (+2 more)
Evidence from seismic studies, mineral physics, thermal
evolution models and geomagnetic observations is inconclusive about the presence of a stably stratified layer at the top of the Earth's fluid outer core. Such a convectively stable layer could have a strong influence on the internal fluid waves propagating underneath the core-mantle boundary (CMB) that are used to probe the outermost region of the core through the wave interaction with the geomagnetic field and the rotation of the mantle. Here, we numerically investigate the effect of a top stable layer on the outer core fluid waves by calculating the eigenmodes in a neutrally stratified sphere permeated by a
magnetic field with and without a top stable layer. We use a numerical model, assuming a flow with an m-fold azimuthal
symmetry, that allows for radial motions across the lower boundary of the stable layer and angular momentum exchanges across the CMB through viscous and electromagnetic coupling. On interannual time-scales, we find torsional Alfvén waves that are only marginally affected by weak to moderate stratification strength in the outer layer. At decadal time-scales similarly weak stable layers promote the appearance of waves that propagate primarily within the stable layer itself and resemble Magneto-Archimedes-Coriolis (MAC) waves, even though they interact with the adiabatic fluid core below. These waves can exert viscous and electromagnetic torques on the mantle that are several orders of magnitude larger than those in the neutrally stratified case.
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All authors Fleur Seuren, Santiago A. Triana, Jérémy Rekier, Véronique Dehant, Tim Van Hoolst
Optics
Quantum versus Classical Descriptions of Spontaneous Emission in Nanophotonic Cavities
Jian-Hua Liang, Yue You, Xi-Hua Guan et al. (+3 more)
Here, we demonstrate that
quantum and classical descriptions generally yield different results for the spontaneous emission in nanophotonic cavities. Starting from the quantized single-mode field in a general context of dispersive and lossy cavities, we derive the expression for emission rate enhancement as well as key relevant parameters such as mode volume and quality factor. For general nanophotonic cavities, this ratio of the
quantum to the classical description is typically below unity and varies with the material dispersion properties, scattering-to-absorption ratio and morphology of the cavity. Notably, the two descriptions converge for lossless, non-dispersive dielectric cavities and for noble-metal plasmonic cavities with sufficiently low scattering losses.
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All authors Jian-Hua Liang, Yue You, Xi-Hua Guan, Xiao-Jing Du, Jun He, Zhong-Jian Yang
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Elastic modeling and total energy calculations of the structural characteristics of "free-standing",periodic, pseudomorphic GaN/AlN superlattices
Th. Karakostas, Ph. Komninou and V. Pontikis
The strain states of the components of pseudomorphic superlattices can be accurately modeled analytically through the application of linear elasticity. In this particular case of GaN/AlN 'free-standing' superlattices, the predictions derived from elastic modeling have been compared with total energy calculations of several systems made of components with varying thicknesses. We demonstrate that the elastic predictions for the
lattice constants of the components align with the values obtained from their total energy counterparts, within the limits of computational errors and uncertainties. Furthermore, a phenomenological analysis of the elastic energy stored in the superlattices facilitates the evaluation of the excess energies associated with the interfaces present in these systems. The results mentioned above are briefly contrasted with findings reported in previous literature.
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Computational Physics · Cross-listed from astro-ph.GA
The First Star-by-star $N$-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh et al. (+9 more)
A major goal of computational astrophysics is to simulate the Milky Way
Galaxy with sufficient resolution down to individual
stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as
supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This approach bypasses the short timesteps caused by
supernova explosions using a surrogate model, thereby improving scalability. With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores, breaking through the billion-particle barrier currently faced by state-of-the-art simulations. This resolution allows us to perform the first
star-by-
star galaxy simulation, which resolves individual
stars in the Milky Way
Galaxy. The performance scales over $10^4$ CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs.
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All authors Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh, Naoto Harada, Kentaro Nomura, Kohji Yoshikawa, Yutaka Hirai, Tetsuro Asano, Kana Moriwaki, Masaki Iwasawa, Takashi Okamoto, Junichiro Makino
Biological Physics
Genetic interfaces at the frontier of expanding microbial colonies
Jonathan Bauermann and David R. Nelson
We study the genetic interfaces between two species of an expanding colony that consists of individual microorganisms that reproduce and undergo
diffusion, both at the frontier and in the interior. Within the bulk of the colony, the genetic interface is controlled in a simple way via interspecies interactions. However, at the frontier of the colony, the genetic interface width saturates at finite values for long times, both for neutral strains and interspecies interactions such as antagonism. This finite width arises from
geometric effects: genetic interfaces drift toward local minima at an undulating colony frontier, where a focusing mechanism induced by
curvature impedes diffusive mixing. Numerical simulations support a logarithmic dependence of the genetic interface width on the strength of the number fluctuations.
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Atomic Physics
Ground and excited potential energy surfaces for CaF+Ca interactions and isotope exchange reactions
Dibyendu Sardar and John L. Bohn
We investigate the intermolecular interactions between
laser-cooled CaF and Ca, in their ground and excited electronic states, aiming to understand atom-exchange reaction pathways. Using state-of-the-art ab initio
quantum chemistry methods, we compute potential energy surfaces for nine electronic states arising from the lowest three asymptotes of Ca$_2$F trimer, within the rigid rotor approximation applied to CaF. Two-dimensional potential energy surfaces are computed for the ground state and one of the excited states. We use a combination of the coupled cluster method restricted to single, double, and perturbative triple excitations, and the multireference configuration interaction method with single and double excitations. The ground (X)~$^2\mathrm{A}'$ electronic state of the trimer is significantly deep and highly anisotropic. The excited electronic states are also strongly bound. Notably, the potential energy surface of one of the excited states, (2)~$^2\mathrm{A}'$, lies below the ground-state asymptote of the trimer. By analyzing the potential energy surfaces, we discuss atom-exchange reaction pathways involving both the ground-state interaction between CaF and Ca and the excited metastable state of Ca.
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Medical Physics
Group-Level and Personalized Optimization for the Insula and Hippocampus Focal Electric Field in Transcranial Temporal Interferential Stimulation: A Computational Study
Taiga Inoue, Naofumi Otsuru and Akimasa Hirata
This study evaluated transcranial temporal interference stimulation (tTIS) for focal targeting of the insula and hippocampus, which are clinically relevant yet anatomically difficult to stimulate. Individualized and group-level electrode optimizations were compared to determine whether generalized montages can provide reliable targeting with reduced modeling demands. Sixty high-resolution head models (30 individuals and their mirrored counterparts) were constructed from T1- and T2-weighted MRI. Electric fields (EFs) were computed using the scalar-potential finite-difference method. Electrode montages and current ratios were optimized to minimize the root-mean-square error between simulated and target EF envelope (EFE) distributions, with a threshold of 0.3 V/m. Subsampling analysis was performed to estimate the number of models required for stable group-level outcomes. For the insula, a montage combining T7-P7 and Fp1-Fp2 achieved the highest focality, comparable to individualized results with reduced variability. For the hippocampus, the F7-T7 and T8-P8 montage gave the best group-level focality, though individualized optimization improved off-target suppression. Stable group-level patterns were obtained using 20 models for the insula and 9 for the hippocampus. Optimal tTIS montages depend on target depth. Group-level optimization suffices for cortical regions like the insula, whereas individualized tuning remains preferable for deeper targets such as the hippocampus.
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Chemical Physics
Analytic $G_0W_0$ gradients based on a double-similarity transformation equation-of-motion coupled-cluster treatment
Marios-Petros Kitsaras, Johannes Tölle and Pierre-François Loos
The accurate prediction of ionization potentials (IPs) is central to understanding molecular reactivity, redox behavior, and spectroscopic properties. While vertical IPs can be accessed directly from electronic excitations at fixed
nuclear geometries, the computation of adiabatic IPs requires
nuclear gradients of the ionized states, posing a major theoretical and computational challenge, especially within correlated frameworks. Among the most promising approaches for IP calculations is the many-body Green's function $GW$ method, which provides a balanced compromise between accuracy and computational efficiency. Furthermore, it is applicable to both finite and extended systems. Recent work has established formal connections between $GW$ and coupled-cluster doubles (CCD) theory, leading to the first derivation of analytic $GW$
nuclear gradients via a
unitary CCD framework. In this work, we present an alternative, fully analytic formulation of $GW$
nuclear gradients based on a modified version of the traditional equation-of-motion CCD formalism, enabling the inclusion of missing correlation effects in the traditional CCD methods.
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Fluid Dynamics
Analysis of Hematocrit-Plasma Separation in a Trifurcated Microchannel by a Diffusive Flux Model
Rishi Kumar, Indranil Saha Dalal and K. Muralidhar
Platelet-enriched
plasma and red blood
cells (RBC) are needed in the treatment of blood-related diseases, including anaemia and blood cancer. These essential components must be separated from blood in well-designed experimental setups. If active techniques are used, the blood components are likely to be damaged or contaminated while handling. Passive techniques for component separation are preferred, and their design for effectiveness before manufacturing is the subject of this article. Specifically, the performance of a design consisting of a trifurcated microchannel is examined in the framework of 3D numerical simulation, following similar design ideas in recent experimental studies. The influence of geometrical parameters of the channel, such as width and separation arm angle, inlet extension, flow constriction, and flow parameters, including flow rates, hematocrit concentration, and temperature, is studied. The present study utilizes the diffusive flux model (DFM) to model the shear-driven migration of red blood
cells (RBC) in a microchannel along with an appropriate rheology model. The physical mechanism driving separation is the formation of the
cell-free layer near the walls, using which the separation efficiency and device effectiveness are quantified. It is found that a microchannel with a smaller width and an extended inlet, along with diluted blood samples of lower hematocrit, is effective for greater separation, while the device performance is less sensitive to the flow rates, flow constriction, and the separator angle.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
All-Altermagnetic Tunnel Junction of RuO2/NiF2/RuO2
Long Zhang, Guangxin Ni, Xuehao Wu et al. (+1 more)
Emerging altermagnets with zero net
magnetic moment and moment-dependent
spin splitting offer a promising avenue for
antiferromagnetic spintronic devices, yet their integration into
magnetic tunnel junctions has been hindered by reliance on
ferromagnetic electrodes (introducing stray fields) or by limited functionality (non-tunable magnetoresistance without
spin filtering). Here, we propose an all-altermagnetic tunnel junction (AAMTJ) paradigm composed exclusively of altermagnets, exemplified by experimentally feasible RuO2/NiF2/RuO2. By introducing an altermagnetic NiF2 barrier, the achieved tunneling magnetoresistances of 11,704%, 2,496% and 1,892% for RuO2/NiF2/RuO2 are much higher than that of 221% for RuO2/TiO2/RuO2 with a nonmagnetic TiO2 barrier. High
spin filtering efficiencies of ~90% are also obtained. This architecture unlocks multistate high magnetoresistance and
spin filtering via magnetization control of the electrodes and barrier, stemming from their synergistic and antagonistic
alignments of momentum-dependent altermagnetic
spin-splitting. Importantly, high tunneling magnetoresistances are still achieved in the AAMTJ with TiO2 spacer of RuO2/TiO2/NiF2/TiO2/RuO2. Our AAMTJ inherently exhibits low consumption and zero stray field, with nonrelativistic
spin splitting and vanishing
magnetic moment, combining the advantages of both
ferromagnetic and
antiferromagnetic tunnel junctions. This AAMTJ paradigm opens an interesting avenue within the area of high-performance altermagnet-based tunnel junctions.
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All authors Long Zhang, Guangxin Ni, Xuehao Wu, Guoying Gao
Fluid Dynamics
Modelling Fluid–Structure Interaction in an Initially Elliptical Elastic-Walled tube: Improved Onset Criterion for Self-Excited Oscillations
Daniel J. Netherwood and Robert J. Whittaker
We present a theoretical description of the fluid–structure interaction observed within a Starling resistor. The typical setup consists of a pre-stretched finite length thin-walled elastic tube mounted between two rigid tubes. The collapsible section is enclosed within a pressure chamber and a viscous fluid is driven through the system by imposing an axial volume flux at the downstream end. Valid within a long-wavelength thin-walled regime, we use our own results to model the wall mechanics. These results arise from the solution of a generalised
eigenvalue problem, and avoid the need to invoke the ad-hoc approximations made in previous studies. The wall mechanics are then coupled to the fluid mechanics using the Navier–Stokes equations, under the assumption that the oscillations in the tube wall are of small amplitude, long wavelength and high frequency. We derive problems governing the leading-order steady and oscillatory fluid-structure interaction. At leading order, the system permits normal-mode oscillations of constant frequency and amplitude, which are obtained in the form of series solutions. Higher-order corrections govern the slow growth or decay of the oscillations, however (as in previous work) these growth rates can be determined by analysing the system's global energy budget without needing to compute the higher-order terms explicitly. Our results permit the first formal analysis of the errors incurred by neglecting contributions from higher-order azimuthal modes, and enable the determination of improved criterion for the onset of self-excited oscillations in the tube wall.
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Chemical Physics
Over four minutes relaxation of pyruvate using chemically and physically induced deceleration of relaxation
Josh P. Peters, Florin Teleanu, Huijing Zou et al. (+4 more)
[1-13C]pyruvate is the most widely used tracer for hyperpolarized metabolic
magnetic resonance imaging, with profound applications in tumor and inflammation diagnosis as well as treatment monitoring. The most fundamental hurdle to broader application, however, remains the rapid polarization relaxation and the associated signal loss. Here, we report a method to address this challenge. Studying the
nuclear spin relaxation dispersion of [1-13C]pyruvate across
magnetic fields from 8 {\mu}T to 9.4 T, as a function of additives, solvents, and preparation methods, allowed us to achieve relaxation times of up to four minutes. Such a long time could enable reliable quality control and nearly polarization loss-free transport, further boosting the power of hyperpolarized metabolic MRI.
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All authors Josh P. Peters, Florin Teleanu, Huijing Zou, Ehtisham Rasool, Jan-Bernd Hövener, Alexej Jerschow, Andrey N. Pravdivtsev
Chemical Physics · Cross-listed from astro-ph.GA
Automated Exploration of Radical-Molecule Chemistry: The Case of Oxirane + CH in the ISM
Moritz Bensberg, Silvia Alessandrini, Mattia Melosso et al. (+2 more)
Quantum chemistry provides accurate and reliable methods to investigate reaction pathways of reactive molecular systems relevant to the interstellar medium. However, the exhaustive exploration of a reactive network is often a daunting task, resulting in unexplored reactive channels that affect kinetic outcomes and branching ratios. Here, an automated workflow for exploring reactive potential energy surfaces (PESs) is employed for the first time to study the oxirane (C$_2$H$_4$O) plus methylidyne ($^.$CH) reaction. The ultimate goal is to comprehensively map its PES and, subsequently, derive rate constants for the most important reaction channels. In addition to its astrochemical relevance, this reaction has been considered because it is a challenging test case, its network being very extended, with 60 exothermic bimolecular products lying below the reactant's energy. Kinetic simulations indicate that the main product of the reaction is the HCO radical plus ethene (C$_2$H$_4$), while formation of s-trans-propenal (acrolein) and 2H-oxene is also possible, but to a lesser extent. Based on the present study and other references in the literature, we suggest that the slightly higher relative abundance of s-trans-propenal compared to methyl ketene in the interstellar medium is a gas-phase kinetic effect, s-trans-propenal being a more easily accessible product on the C$_3$H$_5$O$^.$ PES.
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All authors Moritz Bensberg, Silvia Alessandrini, Mattia Melosso, Cristina Puzzarini, Markus Reiher
Fluid Dynamics
Fluctuations, Clustering, and Interaction-Driven Dynamics in Sedimenting Particles at Low Galileo Numbers: A Neural Network Approach
Nejc Vovk, Jana Wedel, Paul Steinmann et al. (+1 more)
In this study, we investigate the behaviour of sedimenting solid particles and the influence of microscopic particle dynamics on the collective motion of a sedimenting cloud. Departing from conventional direct numerical simulations (DNS), we introduce a novel machine learning framework, the Interaction-Decomposed Neural Network (IDNN), to model hydrodynamic particle interactions. The IDNN acts as a black-box module within a Lagrangian solver, predicting the particle drag force based on the relative positions of the nearest neighbours. This enables the recovery of force fluctuations, capturing effects previously accessible only through DNS. Our results show an increase in collective settling velocity in the dilute regime, consistent with earlier experimental and numerical studies, which we attribute to (i) fluctuations in the streamwise particle force around a value that is lower than the Stokes limit and (ii) the formation of particle clusters sedimenting at enhanced velocities. These fluctuations originate from persistent entrainment and ejection of particles in and out of the long, diffusive wakes generated by upstream particles at low Galileo numbers. Energy spectra of particle velocity fluctuations reveal a scale-dependent transfer of fluctuation energy, analogous to a turbulent-like cascade, with pronounced large-scale fluctuations at higher volume fractions. At very low volume fractions, fluctuation intensity and energy spectrum amplitudes diminish, though hydrodynamic interactions still remain appreciable.
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All authors Nejc Vovk, Jana Wedel, Paul Steinmann, Jure Ravnik
Instrumentation and Detectors
Characterization of field cage and cathode for low radioactivity operation with the CYGNO experiment
F.D. Amaro, R. Antonietti, E. Baracchini et al. (+48 more)
Dark matter, which is considered to account for approximately the 27% of the Universe's energy-mass content, remains an open issue in modern particle physics along with its composition. The CYGNO Experiment aims to exploit an innovative approach applied to the direct detection search of low energy
nuclear recoils possibly induced by cold particle-like dark matter candidates. CYGNO employs a directional detector based on a Time Projection Chamber (TPC) filled with a He:CF$_{4}$ gas mixture and equipped with an optical readout. Currently, the CYGNO Collaboration is constructing the detector demonstrator, CYGNO-04, in
Hall F at Laboratori Nazionali del Gran Sasso (LNGS). This 0.4 m$^3$ detector has the goal of proving the scalability of the technology and assessing the physics and radiopurity capabilities. Given the low radioactivity requirements, especially in internal components such as field cage and cathode, the reduction of material while keeping the correct electrical behavior is paramount. In this paper, we present the validation of several internal components, mainly focusing on the field cage material and support structure. The tests included geometrical asymmetries in the electric field response, collection efficiency as well as measurement of known physical quantities. A preferred configuration is found with a structure based on Nylon material which supports a PET or Kapton sheet with copper strips deposited on.
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All authors F.D. Amaro, R. Antonietti, E. Baracchini, L. Benussi, S. Bianco, A. Biondi, C. Capoccia, M. Caponero, L.G.M. de Carvalho, G. Cavoto, I.A. Costa, A. Croce, M. D'Astolfo, G. D'Imperio, E. Danè, G. Dho, E. Di Marco, J.M.F. dos Santos, D. Fiorina, F. Iacoangeli, Z. Islam, E. Kemp, H.P. Lima Jr, G. Maccarrone, R.D.P. Mano, D.J.G. Marques, G. Mazzitelli, A.G. McLean, P. Meloni, A. Messina, C.M.B. Monteiro, R.A. Nobrega, I.F. Pains, E. Paoletti, L. Passamonti, F. Petrucci, S. Piacentini, D. Piccolo, D. Pierluigi, D. Pinci, A. Prajapati, F. Renga, F. Rosatelli, A. Russo, G. Saviano, P.A.O.C. Silva, N.J. Spooner, R. Tesauro, S. Tomassini, S. Torelli, D. Tozzi
Optics · Cross-listed from quant-ph
Spoofing resilience for simple-detection quantum illumination LIDAR
Richard J. Murchie and John Jeffers
Object detection and range finding using a weak light source is vulnerable to jamming and spoofing attacks by an intruder.
Quantum illumination with nonsimultaneous, phase-insensitive coincidence measurements can provide jamming resilience compared to identical measurements for classical illumination. We extend an experimentally-feasible object detection and range finding
quantum illumination-based protocol to include spoofing resilience. This approach allows the system to be characterised by its experimental parameters and
quantum states, rather than just its detector data. Therefore we can scope the parameter-space which provides some spoofing resilience without relying upon the prohibitive method of acquiring detector data for all combinations of the experimental parameters. We demonstrate that in certain regimes the intruder has an optimal relative detection basis angle to minimise the induced error. We also show that there are spoofing-vulnerable regimes where excessive background noise prevents any induced error, while it is still possible to perform object detection, i.e. our detectors have not been fully blinded. The sensing protocol which we describe can allow for the recognition of intrusion and the possible detection of our trustworthy return signal. Our results reinforce that
quantum illumination is advantageous for spoofing resilience compared to a classical illumination-based protocol.
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Computational Physics · Cross-listed from cs.AI
Accelerating IC Thermal Simulation Data Generation via Block Krylov and Operator Action
Hong Wang, Wenkai Yang, Jie Wang et al. (+6 more)
Recent advances in data-driven approaches, such as neural operators (NOs), have shown substantial efficacy in reducing the solution time for integrated
circuit (IC) thermal simulations. However, a limitation of these approaches is requiring a large amount of high-fidelity training data, such as chip parameters and temperature distributions, thereby incurring significant computational costs. To address this challenge, we propose a novel
algorithm for the generation of IC thermal simulation data, named block Krylov and operator action (BlocKOA), which simultaneously accelerates the data generation process and enhances the precision of generated data. BlocKOA is specifically designed for IC applications. Initially, we use the block Krylov
algorithm based on the structure of the heat equation to quickly obtain a few basic solutions. Then we combine them to get numerous temperature distributions that satisfy the physical constraints. Finally, we apply heat operators on these functions to determine the heat source distributions, efficiently generating precise data points. Theoretical analysis shows that the time complexity of BlocKOA is one order lower than the existing method. Experimental results further validate its efficiency, showing that BlocKOA achieves a 420-fold speedup in generating thermal simulation data for 5000 chips with varying physical parameters and IC structures. Even with just 4% of the generation time, data-driven approaches trained on the data generated by BlocKOA exhibits comparable performance to that using the existing method.
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All authors Hong Wang, Wenkai Yang, Jie Wang, Huanshuo Dong, Zijie Geng, Zhen Huang, Depeng Xie, Zhezheng Hao, Hande Dong
Atmospheric and Oceanic Physics
Dynamics and Model Representation of Two Contrasting Extreme Precipitation Events in the Sahel
Souleymane Sanogo, Marlon Maranan, Andreas H. Fink et al. (+2 more)
Two extreme flood-inducing precipitation events in two cities in Mali, on 08 August 2012 in San (127 mm) and on 25 August 2019 in Kenieba (126 mm), are investigated with respect to rainfall structures, dynamical forcings, and the ability of the ICOsahedral Nonhydrostatic (ICON) model to represent their
evolution. Two sets of experiments with convective parameterization enabled (PARAM) and disabled (EXPLC), both at 6.5 km grid spacing, are conducted for each case. While the (thermo)dynamical fields of the simulations are compared with ERA5 reanalysis data, the rainfall fields are tested against the satellite-based precipitation
dataset IMERG by applying the spatial verification methods Fractions Skill Score (FSS) and the Structure-Amplitude-Location (SAL) score. In addition, a spectral filtering of tropical waves is applied to investigate their impact on the extreme events. The most prominent results are: (1) Both cases were caused by organized convective systems associated with a westward propagating cyclonic
vortex, but differ in their environmental setting. Although both cases featured an east African wave (AEW), the San case involved convective enhancement along dry Saharan airmasses, whereas the Kenieba case occurred within an unusual widespread wet environment extending deep into the Sahel. (2) Although EXPLC captures the rainfall distribution in the San case better than PARAM, it fails to organize convection in the moisture-laden Kenieba case, which PARAM is capable of simulating. (3) The FSS confirms the case-dependency of the ICON skill. The SAL method hints towards a systematic deficiency of EXPLC to represent the convective organization by producing too many scattered and weak rainfall systems, while PARAM is more effective in converting abundant moisture into excessive rainfall.
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All authors Souleymane Sanogo, Marlon Maranan, Andreas H. Fink, Beth J. Woodhams, Peter Knippertz
Computational Physics · Cross-listed from cs.CE
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov et al. (+11 more)
Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective
benchmarks - leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, and obscures scientific progress. To address this, we propose a Common Task Framework (CTF) for scientific machine learning. The CTF features a curated set of
datasets and task-specific metrics spanning
forecasting, state reconstruction, and generalization under realistic constraints, including noise and limited data. Inspired by the success of CTFs in fields like natural
language processing and computer
vision, our framework provides a structured, rigorous foundation for head-to-head evaluation of diverse
algorithms. As a first step, we
benchmark methods on two canonical nonlinear systems: Kuramoto-Sivashinsky and Lorenz. These results illustrate the utility of the CTF in revealing method strengths, limitations, and suitability for specific classes of problems and diverse objectives. Next, we are launching a competition around a global real world sea surface temperature
dataset with a true holdout
dataset to foster community engagement. Our long-term
vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets that raise the bar for rigor and reproducibility in scientific ML.
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All authors Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles Cranmer, J. Nathan Kutz
Chemical Physics · Cross-listed from quant-ph
Size-consistent implementation of Hamiltonian simulation-based quantum-selected configuration interaction method for the supramolecular approach
Kenji Sugisaki
The
quantum-selected configuration interaction (QSCI) method is a promising approach for large-scale
quantum chemical calculations on currently available
quantum hardware. However, its naive implementation lacks size consistency, which is essential for accurate intermolecular interaction energy calculations using the supramolecular approach. Here, we present a size-consistent implementation of QSCI by sampling Slater determinants for the dimer in the localized molecular orbital basis, constructing the subspaces for the monomers and dimer, and augmenting the dimer subspace with additional determinants required for size consistency. Implemented within the
Hamiltonian simulation-based QSCI (HSB-QSCI) framework, our method numerically satisfies size consistency for 4H/8H/12H clusters, the FH dimer, and the FH–H$_2$O system. Application to intermolecular interaction energy calculations of hydrogen-bonded FH dimer and FH–H$_2$O demonstrates that our approach reproduces complete active space-configuration interaction (CAS-CI) values with errors below 0.04 kcal mol$^{-1}$.
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Chemical Physics · Cross-listed from cond-mat.soft
Tuneable ion selectivity in vermiculite membranes intercalated with unexchangeable ions
Zhuang Liu, Yumei Tan, Jianhao Qian et al. (+8 more)
Membranes selective to ions of the same charge are increasingly sought for wastewater processing and valuable element recovery. However, while narrow channels are known to be essential, other membrane parameters remain difficult to identify and control. Here we show that Zr$^{4+}$, Sn$^{4+}$, Ir$^{4+}$, and La$^{3+}$ ions intercalated into vermiculite laminate membranes become effectively unexchangeable, creating stable channels, one to two water layers wide, that exhibit robust and tuneable ion selectivity. Ion permeability in these membranes spans five orders of magnitude, following a trend dictated by the ions' Gibbs free energy of hydration. Unexpectedly, different intercalated ions lead to two distinct monovalent ion selectivity sequences, despite producing channels of identical width. The selectivity instead correlates with the membranes' stiffness and the
entropy of hydration of the intercalated ions. These results introduce a new ion selectivity mechanism driven by entropic and mechanical effects, beyond classical size and charge exclusion.
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All authors Zhuang Liu, Yumei Tan, Jianhao Qian, Min Cao, Eli Hoenig, Guowei Yang, Fengchao Wang, Francois M. Peeters, Yi-Chao Zou, Liang-Yin Chu, Marcelo Lozada-Hidalgo
Instrumentation and Detectors · Cross-listed from cond-mat.supr-con
THz mixing of high-order harmonics using $YBa_2Cu_3O_{7-{\delta}}$ nanobridges
Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul et al. (+4 more)
Superconducting materials are a key for technologies enabling a large number of devices including THz wave mixers and single
photon detectors, though limited at very low temperatures for conventional
superconductors. High temperature operation could in principle be offered using cuprate
superconductors. However, the complexity of the material in thin film form, the extremely short coherence length and material stability, have hindered the realization of THz devices working at
liquid nitrogen temperatures. $YBa_2Cu_3O_{7-{\delta}}$ (YBCO) nanodevices have demonstrated non-linear properties typical of Josephson-like behavior, which have the potential for the mixing of AC signals in the THz range due to the large superconducting energy gap. Here, we present AC Josephson functionalities for terahertz waves utilizing Abrikosov
vortex motion in nanoscale-confined fully planar YBCO thin film bridges. We observe Shapiro step-like features in the current voltage characteristics when irradiating the device with monochromatic sub-THz waves (100 GHz to 215 GHz) at 77 K. We further explore these nonlinear effects by detecting THz high-order harmonic mixing for signals from 200 GHz up to 1.4 THz using a local oscillator at 100 GHz. Our results open a path to an easy-fabricated HTS nonlinear nanodevice based on dimensional confinement suitable for terahertz applications.
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All authors Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul, Alexei Kalaboukhov, Floriana Lombardi, Sergey Cherednichenko, Thilo Bauch
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Topological Control of Transition Metal Networks for Reversible High-Capacity Li-rich Cathodes
Changming Ke, Yudi Yang, Minjun Wang et al. (+2 more)
Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich
oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity. The atomic-scale mechanisms behind this degradation, and whether it can be made reversible, remain open questions. Here, using submicrosecond-scale molecular dynamics simulations with first-principles accuracy, we directly visualize the entire charge-discharge cycle of Li$_2$MnO$_3$, uncovering the full lifecycle of the O$_2$-filled nanovoids responsible for degradation and identifying the critical size limit for voids to remain fully repairable upon discharge. Our results reveal that the
topology of the Mn cation network is the key factor governing void growth, coalescence, and reparability. Based on a structural
topology-informed design principle, we computationally develop a novel Li$_2$MnO$_3$ structure featuring a Mn
lattice with a Kagome-like pattern, demonstrating full electrochemical reversibility even under extreme 80% delithiation. Our work establishes a new paradigm for designing high-energy cathodes, shifting the focus from mitigating damage to engineering inherent stability through atomic-level topological control of transition metal network.
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All authors Changming Ke, Yudi Yang, Minjun Wang, Jianhui Wang, Shi Liu
Physics and Society
Case Study of a 75-Year-Old Woman with Parkinson's Disease: Rehabilitation Trajectory with Logic Workout Training
Paul-Emmanuel Sornette and Didier Sornette
We report the single-case trajectory of a 75-year-old retired occupational female therapist with idiopathic Parkinson's disease, Hoehn and Yahr stage 2 at diagnosis. Following progressive impairment despite standard care, she initiated training with Logic Workout (LW) in July 2025 under supervision. Within weeks, she reported meaningful improvements spanning motor function, posture, pain, fine motor skill, mood, sleep consolidation, and disappearing of fatigue. Although single cases cannot establish generalizable efficacy, systematic and chronological documentation can be valuable for hypothesis generation and feasibility assessment in real-world settings prior to controlled trials. We summarize the baseline condition and treatment history, describe the LW intervention, compile self-reported outcomes, and interpret the findings in light of the underlying Logic Workout hypothesis, before concluding with key caveats and perspectives for future research.
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Optics
Tensor network methods for quantum-inspired image processing and classical optics
Nicolas Allegra
Tensor network methods strike a middle ground between fully-fledged
quantum computing and classical computing, as they take inspiration from
quantum systems to significantly speed up certain classical operations. Their strength lies in their compressive power and the wide variety of efficient
algorithms that operate within this compressed space. In this work, we focus on applying these methods to fundamental problems in image compression and processing and classical optics such as wave-front propagation and optical image formation, by using directly or indirectly parallels with
quantum mechanics and computation. These
quantum-inspired methods are expected to yield faster
algorithms with applications ranging from astronomy and earth observation to microscopy and classical imaging more broadly.
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Plasma Physics
Machine Learning approach to modeling of neutral particles transport in plasma
M.V. Umansky, G.J. Parker and R.D. Smirnov
A propagator-based approach is investigated for Monte-Carlo (MC) modeling of neutral particles transport in fusion boundary
plasmas. The propagator is essentially a Green function for the neutral kinetic equation, which depends on the
plasma profiles. A Neural Network (NN) based model for the propagator provides a fast and accurate solution for the neutral distribution function in
plasma. Furthermore, continuous and smooth dependence of NN-based reconstruction of the propagator on the
plasma parameters opens the possibility for using this approach with Jacobian-based methods for time-integration and root finding. Initial results from a small 1D test problem look promising; however, important research questions are concerned with the scaling of the
algorithm to larger systems.
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Optics
Chiral exceptional bound states in the continuum: a higher-order singularity for on-chip control of quantum emission
Jin Li, Kexun Wu, Qi Hao et al. (+2 more)
We demonstrate a fully integrable and reconfigurable platform for controlling
quantum emission by harnessing
chiral exceptional bound states in the continuum (BICs) as a higher-order non-Hermitian singularity. Our architecture employs dual-microring resonators evanescently coupled to two waveguides, supporting
symmetry-protected BICs. By integrating {a waveguide-coupled reflector} coupled with one resonator as a unidirectional feedback, a pair of orthogonal BICs gets transformed into a single, {
chiral quasi-BIC} residing on an exceptional surface. The phase terms in external coupling and inter-modal coupling serve as two independent tuning knobs, enabling unprecedented dynamic control over the spontaneous emission dynamics of individual
quantum emitters, including the Purcell enhancement and the emission lineshape. The efficiency in reconfiguring the output intensity gets promoted by more than a factor of two compared to alternative schemes, offering a promising path toward high-speed
quantum optical switches and active lifetime control in integrated
quantum photonic
circuits.
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All authors Jin Li, Kexun Wu, Qi Hao, Yan Chen, Jiawei Wang
Computational Physics · Cross-listed from cond-mat.mtrl-sci
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang et al. (+6 more)
Machine learning interatomic potentials (MLIPs) bridge the gap between
quantum-mechanical accuracy and scalable molecular simulations. However, a fundamental trade-off persists: universal MLIPs (u-MLIPs) offer broad transferability but are computationally expensive, whereas task-specific MLIPs (ts-MLIPs) achieve high efficiency at the expense of costly DFT training data. Here we introduce LightPFP, a data-efficient knowledge
distillation framework. Instead of relying on expensive DFT calculations, LightPFP constructs a distilled ts-MLIP by using a u-MLIP to generate high-quality, material-specific training data, while employing a pre-trained lightweight model to further improve data efficiency. Across diverse material systems, LightPFP reduces model development time by approximately three orders of magnitude compared with DFT-based workflows, while maintaining DFT-level accuracy. Combined with a LAMMPS/Kokkos inference backend, LightPFP achieves more than three orders of magnitude higher inference efficiency than u-MLIP baselines while enabling simulations of systems thousands of times larger. When the teacher exhibits systematic bias, LightPFP supports few-shot precision correction using as few as 10 high-accuracy DFT configurations, as demonstrated by reproducing MgO's experimental melting point. This u-MLIP-driven
distillation establishes a scalable route to high-fidelity, data-efficient MLIPs, accelerating the pace of materials discovery and design.
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All authors Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang, Ryuhei Okuno, Yuta Tsuboi, So Takamoto, Junichi Ishida, Iori Kurata, Ju Li
Physics and Society
Universal Network Generation Model via Exponential Probabilistic Growth and Vari-linear Preferential Attachment
Jinhu Ren and Linyuan Lü
Generated networks are widely used in network-based research as a convenient simulation environment. Generating universal networks that more accurately reflect real-world patterns is a cornerstone task. This study proposes a vari-linear network generation model that incorporates two core mechanisms: exponential probabilistic growth and vari-linear preferential attachment. It concurrently overcomes the limitations of traditional growth in characterizing the low-degree region of the degree distribution and the issues regarding the universality of linear preferential attachment. Results indicate that our model describes real-world networks more comprehensively and faithfully, and is highly interpretable. Its performance on diverse empirical
datasets is several times better than traditional methods. Related mechanisms and conclusions are substantiated through ablation experiments and statistical analysis. Notably, it achieves a unified interpretation of previously isolated classical network characteristics. This work not only provides a higher-quality universal network generation method, but also bridges the boundaries between traditional concepts, thereby promoting substantive progress in the "world model" of networks.
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Chemical Physics · Cross-listed from cond-mat.soft
Mastering energy landscapes via liquid liquid phase separation to program active supramolecular coassembly from the nano to macro scale
Yuanhao Wu, Alexander van Teijlingen, Julie Watts et al. (+6 more)
The energy landscape dictates pathways and outcomes in supramolecular selfassembly, yet harnessing it from the nano to the macro scales remains a major challenge. Here, we demonstrate
liquid liquid phase separation (LLPS) as a powerful tool to navigate and engineer the energy landscapes of coassembly systems comprising disordered
proteins and peptides. We quantitatively map the energy barriers and transition states governing structural transitions, enabling predictive on off control of assembly and hierarchical order from nano to macro scales. By integrating supramolecular biofabrication, we achieve spatially organized architectures with life like non
equilibrium behaviour. Crucially, assembly stability and scalable selfsorting are shown to depend on accessing minimum energy states, regardless of whether the co assembled structures are disordered or ordered. This work establishes energy landscape mediation via LLPS as a general framework for designing lifelike, hierarchically structured materials.
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All authors Yuanhao Wu, Alexander van Teijlingen, Julie Watts, Zhiquan Yu, Shanshan Su, Jose Carlos RodriguezCabello, Lihi Abramovich, Tell Tuttle, Alvaro Mata