Biological Physics · Cross-listed from q-bio.PE
Travelling waves of invasion in microbial communities with phenotypic switching
Diego Manso Anda and Pierre A. Haas
Complex microbial habitats see the spatial competition of different clonal bacterial populations that switch between different phenotypes. Here, we determine the effect of this subpopulation structure on the invasion of one species by another in a minimal model of two competing species: one species switches, both stochastically and in response to its competitor, to a persister phenotype resilient to competition. Surprisingly, our combined analytical and numerical results show that this phenotypic switching has no effect on the speed of the travelling wave by which the competitors invade the first population. Conversely, we discover that phenotypic switching can speed up the wave by which this population invades their competitors. Our results thus suggest, counterintuitively, that bacterial persistence can be an offensive, rather than defensive ecological strategy.
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Plasma Physics
Weibel-mediated filamentary structures observed in the ICF context
C. Ruyer, S. Bolaños, P. E. Masson Laborde et al. (+16 more)
In light of novel and past experimental results, we demonstrate how Weibel-mediated filamentary structures can develop in the expanding
plasma plume of a
laser-irradiated foil. The transverse ballistic cooling that occurs during the quasi-spherical
plasma expansion naturally drives an electron pressure anisotropy, resulting in the growth of electron current filaments. This effect competes with electron-ion Coulomb collisions which tend to isotropize the electron distribution function. Based on theoretical and particle-in-
cell modeling, we provide estimates of the dominant wavelength and amplitude of the self-generated
magnetic fluctuations, which are found to explain experimental data obtained at the OMEGA and
Laser Megajoule facilities.
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All authors C. Ruyer, S. Bolaños, P. E. Masson Laborde, L. Gremillet, N. Blanchot, G. Boutoux, W. Cayzac, C. Courtois, S. G. Dannhoff, V. Denis, L. Le Deroff, C. K. Li, J. Fuchs, A. Grisollet, I. Lantuéjoul, R. Riquier, R. Smets, G. D. Sutcliffe, B. Vauzour
Optics · Cross-listed from cond-mat.other
Pulse, polarization and topology shaping of polariton fuids
Lorenzo Dominici, David Colas, Stefano Donati et al. (+9 more)
Here we present different approaches to ultrafast pulse and polarization shaping, based on a “
quantum fluid” platform of polaritons. Indeed we exploit the normal modes of two dimensional polariton fluids made of strong coupled
quantum well
excitons and microcavity
photons, by rooting different polarization and topological states into their sub-picosecond Rabi oscillations. Coherent control of two resonant excitation pulses allows us to prepare the desired state of the polariton, taking benefit from its four-component features given by the combination of the two normal modes with the two degrees of polarization. An ultrafast imaging based on the digital off-axis holography technique is implemented to study the polariton complex wavefunction with time and space resolution. We show in order coherent control of the polariton state on the Bloch sphere, an ultrafast polarization sweeping of the Poincaré sphere, and the dynamical twist of full Poincaré states such as the skyrmion on the sphere itself. Finally, we realize a new kind of ultrafast swirling
vortices by adding the angular momentum degree of freedom to the two-pulse scheme. These oscillating
topology states are characterized by one or more inner phase singularities tubes which spirals around the axis of propagation. The mechanism is devised in the splitting of the
vortex into the upper and lower polaritons, resulting in an oscillatory exchange of energy and angular momentum and in the emitted time and space structured photonic packets.
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All authors Lorenzo Dominici, David Colas, Stefano Donati, Galbadrakh Dagvadorj, Antonio Gianfrate, Carlos Sánchez Muñoz, Dario Ballarini, Milena De Giorgi, Giuseppe Gigli, Marzena H. Szymańska, Fabrice P. Laussy, Daniele Sanvitto
Physics and Society · Cross-listed from cs.SE
DREAMS: Modelling Support for Research into Engineering and Artistic Design
Apala Chakrabarti
Design Research Methodology (DRM) supports systematic design research through representations such as Reference Models and Impact Models. However, the practical construction and maintenance of these models often remains manual, requiring repeated redrawing, layout adjustment, and separate handling of assumptions, references, and supporting evidence. This can make DRM modelling time-consuming, visually cluttered, and difficult to revise as models increase in complexity. This paper presents DREAMS, an early-stage prototype modelling environment developed to support the creation and maintenance of DRM Reference Models and Impact Models. The tool enables users to construct typed causal models using DRM-relevant elements, define signed causal relationships, and attach assumptions, experiential inputs, and references directly to causal links. It also provides layout support and search functions to improve readability, modifiability, and
retrieval of supporting information. A preliminary comparative evaluation with four DRM users was conducted against manual modelling practice. The results indicate reductions in model creation time, revision time, repositioning effort, edge crossings, and evidence
retrieval time when using DREAMS. These findings are interpreted as early evidence of practical potential rather than full validation. The contribution of the paper lies in identifying requirements for DRM-aligned modelling support, presenting the design and implementation of DREAMS, and demonstrating its potential to reduce modelling effort and improve traceability in DRM-based research.
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Computational Physics
Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
Xinran Wei, Yan Pan, Fusong Ju et al. (+8 more)
Locality-driven integration is a pervasive computational pattern in
quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral screening. The dominant
matrix multiplications in these tasks exhibit dynamic, structured sparsity driven by spatial locality, posing significant challenges for both dense batched kernels and generic sparse formats on GPUs. We present KerneLDI, a GPU-oriented framework that addresses this regime by co-designing data layout, screening logic, and
matrix-computation operators to realize block-structured
matrix multiplication for locality-driven integration. KerneLDI reorganizes operand
matrices into a unified block-filtered representation that retains only spatially relevant blocks, and executes the resulting contractions with customized dense block multipliers that adapt proven dense-matmul optimizations to retained block pairs. We develop and evaluate KerneLDI on exchange–correlation (EXC) integration in Kohn–Sham density functional theory, a representative and computationally critical instance of this pattern. Across diverse molecular systems, KerneLDI preserves numerical accuracy while delivering up to 10$\times$ speedup for EXC evaluation over a dense GPU baseline, scales favorably with increasing system size and multi-GPU parallelism, accelerates end-to-end self-consistent field calculations, and yields nearly 6$\times$ throughput improvement for ab initio molecular dynamics.
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All authors Xinran Wei, Yan Pan, Fusong Ju, Zehao Zhou, Yihong Zhang, Lin Huang, Jianwei Zhu, Jia Zhang, Huanhuan Xia, Bin Shao, Tao Qin
Chemical Physics
Learning to Rank for Selected Configuration Interaction
Wan Nie, Songwei Liu, Yingying Yu et al. (+2 more)
The accurate description of electron correlation is a central challenge in computational chemistry, with selected configuration interaction (SCI) emerging as a powerful tool to approach the full CI limit. While recent machine learning (ML) integrations have accelerated determinant selection, existing regression and classification approaches suffer from a fundamental objective-loss mismatch: they evaluate the importance of determinants in isolation without explicitly accounting for their relative importance ranking. Here, we introduce ranking configuration interaction (RCI), a novel ML-supported SCI framework that reframes determinant selection as a pairwise ranking problem. Building upon a
Transformer-based architecture to capture complex, non-local orbital dependencies, RCI progressively optimizes the partial ordering of determinants. By doing so, RCI aligns the training objective more closely with the intrinsic ranking nature of SCI. Extensive
benchmarks across both plane-wave and Gaussian basis sets, including the molecules N$_2$, CO, H$_2$O, NH$_3$, and C$_2$, demonstrate the efficiency of RCI. Compared to previously reported classification baselines, RCI consistently accelerates convergence-reducing overall computational time by 23% to over 50% depending on the system, and requiring only 55% of the determinant count in representative cases such as N$_2$ and CO. Furthermore, RCI exhibits robust performance and reaches chemical accuracy on the highly challenging iron-sulfur cluster using only 12% of the full CI space. Notably, RCI outperforms recent regression-based SCI methods by delivering a more than 15% improvement in accuracy at comparable determinant counts. RCI also demonstrates higher efficiency than heat-bath CI on the strongly correlated chromium dimer, yielding a compact and accurate wavefunction.
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All authors Wan Nie, Songwei Liu, Yingying Yu, Zhiwen Wang, and Jun Yang
Geophysics
How sea level paces faulting at fast-spreading mid-ocean ridges
Richard F Katz and Peter Huybers
Abyssal hills, arguably the most extensive coherent pattern in Earth's surface topography, record the spacing of normal faults formed at mid-ocean ridges. At fast-spreading ridges, high-resolution bathymetry shows a pronounced spectral peak near 41 ky, coincident with obliquity-paced Pleistocene sea-level variability. The origin of this apparent orbital imprint on seafloor structure remains unresolved. We hypothesise that glacial-interglacial sea-level variability influences fault spacing by modulating plate thickness and the flexural stresses produced during plate unbending. Sea-level change alters mantle melting rates and magma supply at ridge axes, generating variations in the properties of the accreting plate. As the plate moves off axis, it unbends from its ingrown
curvature, producing tensile fibre stresses that drive normal faulting. We hypothesise that small perturbations in elastic plate thickness modulate these stresses and thereby influence fault spacing. To test this, we extend the elastic unbending theory of Buck (2001) to include spatially variable plate thickness and yield-weakening viscoplastic flexure, which localises deformation into discrete kinks interpreted as faults. Linearised analysis shows that plate-thickness perturbations generate proportional fibre-stress variations. Numerical solutions demonstrate that perturbations as small as approximately 0.1 percent can phase-lock faulting to the imposed forcing. When driven by plate-thickness perturbations derived from the Pleistocene oxygen-isotope record, the model predicts fault spacings concentrated near 41 ky in the early Pleistocene and near 100 ky in the late Pleistocene, consistent with observed abyssal-hill spectra. These results provide a quantitative mechanism by which glacial-interglacial sea-level variability can be transmitted into tectonic structure.
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Data Analysis and Statistics · Cross-listed from gr-qc
BB plot: A Tool for Accurate Model Selection Using Bayes factors
Ankur Barsode
A common task in physics and astronomy is studying which of the competing hypotheses the data prefer. This is usually done by computing the Bayes factor between the two hypotheses, and either interpreting it in terms of the posterior odds or as a ranking statistic for a frequentist p-value test. Here we describe a relationship between the Bayes factor and its distributions under the two competing hypotheses, called the Bayes factor-Bayes factor (BB) relationship, expressed as a diagnostic plot. Using examples from gravitational wave (GW) astronomy, we demonstrate how the BB plot can validate the accuracy of Bayes factor calculations. The BB relationship may also be useful for estimating background distributions of the Bayes factor at low computational cost, even analytically in some cases. We apply this technique in the context of wave-optics
lensing of GWs, extrapolating the background distribution from GWTC4 to put a rough bound of $\lesssim 4.1 \sigma$ on the statistical significance of GW231123.
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Chemical Physics
Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
Sara Navarro-Rodríguez, Alec Wills, Kimberly J. Daas et al. (+2 more)
We study a simple but useful test for neural exchange-correlation (XC) functionals: can a neural model reproduce an established XC functional when it is used self-consistently? We call this test functional cloning. The model is trained at the GGA level to reproduce a known semilocal functional, using either a constrained or an unconstrained architecture. The motivation is that an XC functional is not used on a fixed input. In a Kohn-Sham self-consistent-field calculation it contributes to the potential, and the resulting density is part of the outcome of the same calculation. A good pointwise fit to sampled density descriptors is therefore not by itself enough. Because the target functional is known, the error can be measured directly. We compare the clones on sampled descriptors, molecular total energies, energy differences, transfer between PySCF and SIESTA, and equations of state for crystalline solids. The constrained models reproduce the reference functional more accurately in molecular self-consistent calculations. They also give better initial parameters for later optimization against correlated molecular energies. An additional observation is that the constrained architecture already gives a reasonable solid-state baseline before cloning, as seen from randomly initialized constrained models. Clones trained only on molecular densities transfer well to solids, reproducing reference
lattice constants and bulk
moduli across metallic, covalent, ionic,
oxide, and layered systems. Cross-code tests show that energy differences are relatively robust, while total energies depend strongly on whether the cloning descriptors come from all-electron or pseudopotential densities. These results make functional cloning a useful diagnostic before full self-consistent training of neural XC functionals.
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All authors Sara Navarro-Rodríguez, Alec Wills, Kimberly J. Daas, María Camarasa-Gómez, Marivi Fernández-Serra
Optics
Partial Quantisation of Non-Hermitian Berry Phases in Time-Varying Media
Calvin Hooper
A fundamental
symmetry of the non-Hermitian operators describing wave-propagation in time-varying media imbue such systems with non-trivial
topology. This
topology may be measured directly in a wide range of experimental settings as a quantised real part of the Berry phase, contrasting unconstrained
geometric gain or loss. This topological index is provided explicitly for practical examples, including a non-Hermitian analogue of the Su-Schrieffer-Heeger model.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Statistical mechanics of the $N$-queens problem
Zong-Yue Liu, Hai-Jun Liao and Lei Wang
We investigate the $N$-queens problem as a
lattice gas – a model in which $N$ queens are placed on an $N \times N$ chessboard with pairwise repulsive interactions along shared rows, columns, and diagonals – from the perspective of statistical mechanics. The ground states are exactly the $Q(N)$ solutions of the classical $N$-queens problem, with
entropy per queen $s_0 \approx \ln N - \gamma$ ($\gamma \approx 1.944$). This
entropy reflects a characteristic constraint hierarchy: each successive
geometric constraint – columns, then diagonals – reduces the
entropy from the free-placement value $\ln N$ by a definite constant. We derive the exact high-temperature energy $E/N \to 5/3$ as $N \to \infty$. Extensive Monte Carlo simulations with $10^8$ sweeps per temperature point for $N = 8$–$1024$ reveal that the specific heat per queen $C_v/N$ converges to a universal function of $T$ as $N \to \infty$. The converged curve features a non-divergent peak $C_v^{\max}/N \approx 1.63$ at $T^* \approx 0.235\,J$, establishing the absence of a thermodynamic phase transition. Combined with the trivially exact high-temperature
entropy $S(\infty)/N = (1/N) \ln \binom{N^2}{N}$, the convergence of $C_v/N$ enables a thermodynamic integration of $C_v/T$ from $T = \infty$ to $T = 0$ that recovers the ground-state
entropy – and hence the Simkin constant $\gamma$ – purely from Monte Carlo data. This provides an independent thermodynamic route to a fundamental combinatorial constant. Thermodynamic integration yields $\gamma_{\rm MC} = 1.946 \pm 0.003$ at $N = 1024$, within $0.1\%$ of the precise combinatorial value $\gamma = 1.94400(1)$. We further present a transfer-
matrix-based tensor network formulation that encodes the non-attacking constraints into a rank-9 site tensor with 17 nonzero elements, providing a complementary exact-enumeration route.
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Atomic Physics
Amplitude Modulation Noise Suppression of Dynamic Atom Gravimeters
Wen-Zhang Wang, Jin-Ting Li, Dan-Fang Zhang et al. (+9 more)
Dynamic atom gravimeters enable absolute
gravity measurements on moving platforms. However, their performance is severely degraded due to the complex dynamic environment. This paper finds that the amplitude modulation noise (AMN) is a key factor contributing to the degradation of
gravity measurement performance. We find that the AMN is induced by the cold atomic cloud trajectory and velocity variation. We build a model to illustrate the principles and magnitude of AMN arising from various experiment processes. Then we propose a method to fit the normalized AMN respect to the kinematic parameters of the cold atomic cloud, and successfully suppress this noise from 0.11 to 0.038 using the fitting result. With this method, we improve the fringe phase resolution from 0.244 rad to 0.092 rad, and reduce the dynamic
gravity measurement noise from 2.69 mGal to 1.68 mGal. This study finds and suppresses a key noise source for the dynamic atom gravimeters, which is important for further improving its precision. The proposed method can be also applied for precision enhancement for other dynamic atom interferometer-based sensors, such as the atom gradiometers and gyroscopes.
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All authors Wen-Zhang Wang, Jin-Ting Li, Dan-Fang Zhang, Wei-Hao Xu, Jia-Yi Wei, Jia-Qi Zhong, Biao Tang, Lin Zhou, Run-Bing Li, Xi Chen, Jin-Wang, Ming-Sheng Zhan
Atomic Physics · Cross-listed from hep-ph
Parity Nonconservation in Hydrogen Induced by Low-Mass Vector-Boson Exchange
V. A. Dzuba, V. V. Flambaum and G. K. Vong
Parity-nonconserving (PNC) effects in atoms produced by $Z$-
boson exchange between the electron and the
nucleus grow rapidly with the
nuclear charge $Z$. If a hypothetical additional $Z'$
boson is light, however, its contribution does not exhibit the same strong enhancement with $Z$. As a result, the ratio of the low-mass $Z'$ contribution to the Standard Model $Z$-
boson contribution increases rapidly with decreasing $Z$, in fact faster than $1/Z^2$. Hydrogen has a further important advantage: its theoretical description is substantially cleaner than that of heavy atoms, allowing a more accurate interpretation of experimental results. For these two reasons, hydrogen and deuterium PNC experiments may provide an especially favorable setting in which to disentangle a possible $Z'$ contribution from the Standard Model background. In this paper we calculate the ratio of the $Z'$-
boson contribution, for arbitrary $Z'$ mass, to the Standard Model $Z$-
boson contribution to parity violation in hydrogen and deuterium, including both
nuclear-
spin-independent (NSI) and
nuclear-
spin-dependent (NSD) interactions.
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Computational Physics
FusionRCG: Orchestrating Recursive Computation Graphs across GPU Memory Hierarchies
Yihong Zhang, Xinran Wei, Junshi Chen et al. (+4 more)
Evaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in
quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatch: limited per-thread
memory is quickly overwhelmed by an explosion of simultaneously live intermediate variables. As recurrence scales, this forces massive data spilling to global
memory, collapsing performance into a severe
memory-bound regime. We present FusionRCG, a framework that jointly optimizes computation graph structure and GPU
memory mapping. Exploiting the inherent topological flexibility of recurrence graphs, using electron repulsion integrals as an example, we contribute: (1) liveness-aware graph orchestration to minimize peak live intermediates; (2) algebraic dimensionality reduction via stepwise Cartesian-to-spherical fusion, shrinking intermediate footprints by up to $7.7\times$; and (3) an adaptive multi-tier kernel architecture routing graphs across the
memory hierarchy. Evaluated on NVIDIA A100 GPUs, FusionRCG achieves up to $3.09\times$ end-to-end SCF speedup over GPU4PySCF and maintains $75\%$ parallel efficiency at 64~GPUs, successfully rescuing these workloads from
memory-bound limits.
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All authors Yihong Zhang, Xinran Wei, Junshi Chen, Fusong Ju, Wei Hu, Jinlong Yang, Huanhuan Xia
Chemical Physics
Do Water Molecules Always Stabilize Resonances? Microhydration Effects on Thymine Shape Resonances
Sujan Mandal, Jishnu Narayanan S J, Ankita Gogoi et al. (+3 more)
We investigate microhydration effects on the three low-lying {\pi}* shape resonances of thymine using the Resonance via Padé approach in combination with the DLPNO-EA-EOM-CCSD method. For isolated thymine, the calculated resonance positions are benchmarked against projected CAP-EA-EOM-CCSD calculations and compared with available theoretical and experimental data. Upon hydration, the 1{\pi}* and 2{\pi}* resonances undergo systematic stabilization accompanied by significant increases in their lifetimes, whereas the 3{\pi}* resonance exhibits a more complex behavior. In particular, the lifetime of the lowest resonance increases from 39 fs in isolated thymine to 110 fs in the thymine(H2O)3 cluster. Detailed analysis reveals that the observed resonance shifts arise from competing contributions involving hydrogen bonding, electrostatic interactions, microsolvation-induced
geometric distortion, and finite-basis-set effects. Ghost-atom calculations demonstrate that diffuse basis functions associated with nearby water molecules contribute appreciably to the apparent stabilization, while explicit inclusion of water molecules leads to genuine physical stabilization of the resonance states. Furthermore, calculations on multiple conformers of the monohydrated cluster show that resonance positions and lifetimes depend strongly on the local hydrogen-bonding arrangement and microsolvation geometry. These findings demonstrate that resonance stabilization in microhydrated nucleobases is governed by a subtle interplay between geometry, basis-set effects, and intermolecular interactions.
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All authors Sujan Mandal, Jishnu Narayanan S J, Ankita Gogoi, Madhubani Mukherjee, Idan Haritan, Achintya Kumar Dutta
Fluid Dynamics
Dripping-onto-droplet capillary breakup
Ricardo El Khoury, Kindness Isukwem, Elie Hachem et al. (+1 more)
This experimental, numerical, and theoretical study investigates the capillary thinning and breakup of Newtonian filaments formed following the coalescence of a millimetric-nozzle-generated pendant drop with a lower droplet cap contained in a millimetric cylinder in ambient air, i.e., dripping-onto-droplet capillary breakup (DoD). Our mixed approach combines filament breakup experiments recorded with a high-speed camera and three-dimensional numerical simulations based on a variational multiscale framework for multiphase fluid flows. The results are analysed by considering the dynamics of fluid filament thinning, energy transfers, and scaling laws. Three flow regimes are highlighted: capillary-inertial, capillary-viscous, and mixed capillary-inertial-viscous. All regimes are affected by
gravity. The findings are summarised in a two-dimensional diagram that correlates the filament breakup time with different flow regimes using the important dimensionless parameters of the problem, e.g., the Ohnesorge number (which relates the viscous stress to inertial and capillary stresses) and the Bond number (which balances the gravitational stress with the capillary one). This diagram can be used to quantify both the
liquid viscosity and the
liquid-gas surface tension (for Newtonian fluids). Lastly, we demonstrate that DoD can also be used as a rheometric test, giving access to the extensional relaxation time of
polymer solutions (for viscoelastic fluids).
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All authors Ricardo El Khoury, Kindness Isukwem, Elie Hachem, Anselmo Pereira
Optics
Dynamically Reconfigurable Optical Skyrmions Enabled by a Silicon Microring Optical Phased Array for Robust Free-Space Communication
Zili Cai, Tian Zhang, Qi Chen et al. (+3 more)
Optical skyrmions offer a robust vectorial information degree of freedom for free-space communication, but practical deployment requires a compact platform capable of active topological reconfiguration. Here, we propose a silicon microring-resonator optical phased array that integrates
spin-selective emission and programmable phase control on a single chip. Optimized inner- and outer-grating microring emitters provide decoupled LCP and RCP radiation bases with polarization fractions of 90.27% and 91.40%, enabling active switching between Néel-type and Bloch-type skyrmions, while dynamically tuning the skyrmion number across Nsk =-1.914 to 1.918. Using these programmable topological states, a 4-symbol free-space communication link is constructed and compared with ideal LG-OAM encoding under Kolmogorov
turbulence. The skyrmion-encoded link maintains a lower symbol error rate over a broader
turbulence range, demonstrating that topological observables are more robust than scalar OAM modes. These results establish actively reconfigurable optical skyrmions as compact, programmable, and
turbulence-tolerant information carriers for next-generation free-space optical communication.
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All authors Zili Cai, Tian Zhang, Qi Chen, Zheng Wang, Jian Dai, Kun Xu
Optics
Polarization-sensitive tunable extraordinary terahertz transmission based on a hybrid metal-vanadium dioxide metasurface
S. Hadi Badri, Sanam Saeidnahaei and Jong Su Kim
A thermally tunable extraordinary terahertz transmission in a hybrid metal-vanadium dioxide (VO2) metasurface is numerically demonstrated. The metasurface consists of a metal sheet perforated by square loops while the loops are connected with strips of VO2. The frequency and amplitude of the transmission resonance are modulated by controlling the conductivity of the VO2. For y-polarized incident field, the resonance transmission peak
redshifts from 0.88 to 0.81 THz upon
insulator-to-metallic phase transition of VO2. For x-polarized incident field, the transmission resonance at 0.81 THz is observed in the
insulator phase. However, in the metallic phase of VO2, the electromagnetic field is effectively reflected in the 0.5-1.1 THz range with a transmission level lower than 0.14. The proposed metasurface can be utilized as a terahertz modulator, reconfigurable filter, or switch.
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Chemical Physics
Overfitting by design: neural network density functionals for water
Karim K. Alaa El-Din, Antonius v. Strachwitz, Ana Coutinho Dutra et al. (+1 more)
In density functional theory, simpler exchange-correlation (XC) approximations such as the local density approximation (LDA) are favored for computational speed but rely on limited information, leading to a trade-off between accuracy and generality. Machine-learned XC approximations have seen a lot of interest to address this problem. Here, we train a neural network LDA using a differentiable Kohn-Sham solver, imparting system-specific expertise for water and sacrificing generality for accuracy. Our model achieves 1 kcal / mol errors on gold standard coupled cluster ionization and atomization energies, and improves predictions of spectral lines, electron density distribution, and
equilibrium geometry from as few as eight configurations used for training. We proceed to perform transfer learning and obtain results comparable to higher-rung PBE and B3LYP functionals on the WATER27 subset of the GMTKN55 database, even when only a single two-molecule binding energy is used in the transfer process. This result opens the door for specialist functionals to be trained on different systems from little data, enhancing predictions while maintaining low training costs. Our approach of training a modified XC density functional approximation (DFA) furthermore allows for a highly interpretable result, as the neural network directly corresponds to a correction of the XC energy per electron.
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All authors Karim K. Alaa El-Din, Antonius v. Strachwitz, Ana Coutinho Dutra, Sam M. Vinko
Optics · Cross-listed from astro-ph.IM
Thermal Deformation Reduction in High-Power Interferometry with Higher-Order Laser Modes
Liu Tao, Yuhang Zhao, Zong-Hong Zhu et al. (+1 more)
Test-mass thermal noise is a limiting noise source for current and next-generation ground-based gravitational-wave observatories. Uniform-intensity higher-order
laser beams, including Laguerre-Gaussian (LG) and Hermite-Gaussian (HG) modes, have been proposed as alternatives to the fundamental Gaussian beam due to their thermal-noise advantages. As interferometer power increases toward the megawatt regime, thermal aberrations from absorption in the test-mass coatings become increasingly significant. In this work, we quantify the robustness of higher-order modes against absorption-induced thermal deformation. We show that, under identical operating conditions, higher-order modes produce substantially more uniform thermal distortions than the fundamental mode, requiring significantly less thermal compensation power. The optimal
curvature correction is reduced to 33% for the LG$_{2,2}$ mode and 24% for the HG$_{3,3}$ mode relative to the fundamental mode. We further show that the residual thermal deformation of higher-order modes results in lower optical loss, larger cavity power buildup, and improved modal purity in an aLIGO-like cavity. In addition, astigmatism compensation further enhances the intracavity purity of HG modes under self-heating-induced deformation. These results demonstrate that higher-order modes not only mitigate thermal noise but also intrinsically reduce beam self-heating effects, making them promising candidates for future high-power gravitational-wave interferometers.
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All authors Liu Tao, Yuhang Zhao, Zong-Hong Zhu, Paul Fulda
Chemical Physics
Analytical Representation for the Electronic Contribution of the Nuclear Schiff Interaction Hamiltonian
Satoshi Toda, Yasuto Masuda, Naohiro Tomiyama et al. (+4 more)
The
nuclear Schiff interaction (NSI) arises from a
nuclear force that simultaneously violates spatial parity (P) and time reversal (T)
symmetries, where T
symmetry is equivalent to CP
symmetry under CPT invariance. Detecting the NSI experimentally is important because CP violation is critical for explaining why the amount of matter in the Universe is far greater than that of antimatter. Measuring the NSI in molecules requires both precise experiments and theoretical calculations that incorporate electronic and
nuclear wavefunctions. Conventionally, the electronic terms have been approximated using a first-order power series expansion of the electronic radial function-an approach that yields the well-known
nuclear Schiff moment (NSM) -but this approximation may not be sufficiently accurate. In this study, we introduce a new, accurate analytical expression for the electronic terms based on Gaussian basis sets, which avoids any truncation of the power series. We find that the previous numerical approach overestimates the values for RaO and LrF by more than 50% and 300%, respectively, in the
nuclear-radius region. In contrast to the numerical calculations, the analytical expression-based calculations show less sensitivity to choice of the basis-functions. Furthermore, we develop a new basis set that describes accurate behavior of wave functions both interior and exterior regions of
nucleus. It also demonstrates that an even-tempered basis set is more preferrable over energy optimized basis set for calculating the NSI electronic term in molecules.
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All authors Satoshi Toda, Yasuto Masuda, Naohiro Tomiyama, Kota Yanase, Bijaya Kumar Sahoo, Masahiko Hada, Minori Abe
Computational Physics · Cross-listed from cs.LG
jNO: A JAX Library for Neural Operator and Foundation Model Training
Leon Armbruster, Rathan Ramesh, Georg Kruse et al. (+1 more)
jNO (jax Neural Operators) is a JAX-native library for neural operators and foundation models with unified support for both data-driven and physics-informed training. Its core design is a tracing system in which domains, model calls, residuals, supervised losses, and diagnostics are written in one symbolic
language and compiled into one optimization pipeline. This allows users to move between operator regression, mesh-aware residual evaluation, and PDE-constrained training without restructuring the surrounding code. jNO also supports multi-model compositions, fine-grained control at parameter level (model, optimizer, and learning rate), hyperparameter tuning, and JAX-native workflows for translated PDE foundation-model families. The source repository is available at https://github.com/FhG-IISB/jNO.
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All authors Leon Armbruster, Rathan Ramesh, Georg Kruse, Christopher Straub
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Chiral Porphyrin Monolayers on Ferromagnetic Thin Films: Ultrafast Spectroscopy of Hybrid Interfaces
Karol Hauza, Anna Lewandowska-Andralojc, Ruslan Salikhov et al. (+5 more)
Hybrid
ferromagnetic metal/organic interfaces (spinterfaces) exhibit unique properties, including
spin filtering. In parallel,
chiral organic molecules can themselves induce efficient
spin filtering, leading to unexpectedly high
spin polarizations. Here, we investigate how the proximity of gold-capped Co/Ni
ferromagnetic multilayers influences the spectroscopic properties and photoinduced electron dynamics of
chiral oligopeptides bearing a porphyrin chromophore. The molecules are covalently attached to the gold cap via a
chiral linker, forming a self-assembled monolayer. The porphyrin macrocycles adopt an orientation parallel to the surface, resulting in the formation of J-like aggregates. Photoinduced dynamics are probed using femtosecond pump-probe transient absorption spectroscopy. Despite excitation of only a single molecular layer, a clear transient absorption signal of the porphyrin singlet excited state is observed. Adsorption on the metal surface leads to a pronounced reduction of the excited-state lifetime. However, no signatures of long-lived photoinduced charge-transfer products are detected. Furthermore, no dependence of the excited-state dynamics on either the magnetization direction of the
ferromagnetic layer or the molecular chirality is observed.
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All authors Karol Hauza, Anna Lewandowska-Andralojc, Ruslan Salikhov, Jürgen Lindner, Gotard Burdzinski, Marcin Kwit, Bronislaw Marciniak, Aleksandra Lindner
Computational Physics
Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames
S. Trevor Fush, Israel J. Bonilla, Michael B. Schroeder et al. (+2 more)
Manifold-based combustion models decrease the cost of turbulent combustion simulations by projecting the thermochemical state onto a lower-dimensional
manifold, allowing the thermochemical state to be computed separately from the flow solver. The solutions to the
manifold equations have traditionally been precomputed and pretabulated, but this results in large
memory requirements and significant precomputation cost even for simple models. One approach to alleviate the
memory requirements is to use In-Situ Adaptive
Manifolds (ISAM), which only stores solutions that are encountered during a simulation in a database built with In-Situ Adaptive Tabulation (ISAT). Even with ISAM, as the
manifold complexity increases, the
memory requirements can still grow too large. Another approach to reduce
memory of these databases are machine learning methods, for they represent functions in a highly
memory-compact manner. However, current implementations of these methods require the pregeneration of training
datasets with little knowledge of the states present in a simulation. This work develops the Neural In-Situ Adaptive
Manifolds (Neural-ISAM) method, which is designed to address the drawbacks of both adaptive tabulation and machine learning methods, and leverage their benefits by coupling neural networks to
manifold databases on-the-fly. ISAM databases are built via ISAT, which stores the
manifold solutions in a binary tree, and Neural-ISAM periodically searches this tree to identify regions that can be pruned. Neural networks are trained on the candidate regions, and these portions of the binary tree are then replaced by the trained neural network, reducing the
memory requirements of the database. Neural-ISAM
memory usage, computational performance, and accuracy is evaluated in LES of two turbulent flames with increasing
manifold model complexity: Sandia Flame D and the Sandia Sooting flame.
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All authors S. Trevor Fush, Israel J. Bonilla, Michael B. Schroeder, Matthew X. Yao, Michael E. Mueller
Optics
Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout
Tianyang Chen, Mandar M. Sohoni, Saeed A. Khan et al. (+5 more)
Optical readout in low-light imaging is fundamentally limited by measurement noise, including
photon shot noise, detector noise, and quantization error. In this regime, downstream inference depends not only on the optical front end, but also on how noisy high-dimensional sensor measurements are represented before classification or decision-making. Here we show that eigentasks provide a measurement-adapted representation for optical sensor outputs by ordering readout features according to their resolvability under noise. Using experimental data from a lens-based optical imaging system and a reanalysis of published data from a single-
photon-detection neural network, we find that eigentask representations frequently outperform standard baselines including principal component analysis and filtering-based compression. The advantage is most pronounced in
photon-limited, few-shot, and higher-difficulty classification regimes. In few-shot MPEG-7 classification, for example, the advantage over other methods reaches about 10 percentage points as the number of classes increases. In these settings, eigentasks yield more informative low-dimensional features and improve sample-efficient downstream learning. These results identify measurement-adapted representation as a promising strategy for optical inference when
photon budget, acquisition time, and task complexity are constrained.
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All authors Tianyang Chen, Mandar M. Sohoni, Saeed A. Khan, Jérémie Laydevant, Shi-Yuan Ma, Tianyu Wang, Peter L. McMahon, Hakan E. Türeci
Optics
Diamond membranes: platform for photonic and opto-mechanical applications
Hsin-Hui Huang, Gediminas Seniutinas, Haoran Mu et al. (+8 more)
Diamond 1 - 10 micrometers thick membranes are platform for photonic,
quantum and opto-mechanic devices with applications across UV-IR spectral ranges. IR characterization of diamond gratings in reflection and transmission showed a change of the IR absorbance dichroism between positive and negative when the grating period was 1-2 wavelengths (free space) including inside the region of the intrinsic diamond absorbance. Femtosecond
laser cutting of micrometers-wide and mm-long structures are demonstrated by steps of carbonization > 0.4 J/cm2/pulse (1030 nm/200 fs) and oxidation of diamond membranes. Light intensity distribution inside form-birefringent diamond structure was modeled for a scaled-down structure and wavelength to reveal characteristic interference patterns for different polarizations.
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All authors Hsin-Hui Huang, Gediminas Seniutinas, Haoran Mu, Nguyen Hoai An Le, Eulalia Puig Vilardell, Vijayakumar Anand, Jitraporn Vongsvivut, Tomas Katkus, Meguya Ryu, Junko Morikawa, Saulius Juodkazis
Applied Physics
Physical design of cold neutron direct geometry inelastic spectrometer at China Spallation Neutron Source
Qian Zhao, Xiaowen Zhang, Songwen Xiao et al. (+5 more)
The Cold-
Neutron Inelastic Spectrometer (CNIS) is a direct-geometry, time-of-flight instrument designed for China Spallation
Neutron Source (CSNS) and optimized to probe low-energy
lattice and
magnetic excitations. The instrument integrates a long flight path with bent supermirror guides and an elliptical-focusing geometry to suppress high-energy background while improving cold-
neutron delivery to the sample. A flexible multi-disk chopper suite provides pulse shaping, band selection and monochromatization, enabling multi-$E_\textrm{i}$ operation. Modular features, including an interchangeable high-focusing guide insert, radial collimation and a
vacuum “airbox” for simplified sample-environment integration, enhance signal-to-noise and operational versatility. Through combined flight-path and chopper optimization, CNIS achieves excellent routine-mode energy resolution and can reach approximately $\sim 1\%$ in a dedicated high-resolution configuration. CNIS is planned to commence user operation in 2029, offering a highly flexible platform for cold-
neutron inelastic scattering studies.
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All authors Qian Zhao, Xiaowen Zhang, Songwen Xiao, Wei Luo, Zecong Qin, Zhuang Xu, Yu Feng, Xin Tong
Chemical Physics
Collective resonance light scattering from thermally relaxing systems in cavities
Bingyu Cui
We study steady-state resonance light scattering from ensembles of noninteracting molecules, both in free space and inside optical cavities, while accounting for local thermal relaxation. The scattering spectra are obtained from steady-state solutions of either the Schrödinger equation or a Liouville-space master equation. In the absence of a cavity, the spectra exhibit an elastic peak at the incident-
photon energy and an inelastic fluorescence peak near the molecular excitation energy. Inside a cavity, the fluorescence peak splits into upper- and lower-polaritonic peaks in the strong-coupling regime. We analyze how the elastic and inelastic spectral features scale with the number of molecules under fixed cavity-molecule coupling and identify distinct collective trends in the Rayleigh peak intensity and in the integrated polaritonic or fluorescence spectral weight. The two theoretical approaches yield qualitatively consistent results while highlighting different aspects of thermally induced relaxation and dephasing.
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Geophysics
Total Generalized Variation regularization closes the gap between neural-eld and classical methods in seismic travel-time tomography
Isao Kurosawa
Travel-time
tomography forces a trade-off between mesh resolution and stability in which the regularizer choice dominates what can be recovered. We introduce MIMIR, a differentiable framework that represents the 2D velocity field as a
Fourier-feature neural network, replacing the grid-based slowness vector with a continuous, infinitely differentiable function. Prior neural-field
tomography has staircased smooth fields under total-variation (TV) priors or oscillated near interfaces under $L^2$
Laplacian smoothing. We adopt second-order total generalized variation (TGV$^2$) and parametrize its auxiliary vector field as a second neural network jointly optimized with the velocity field, eliminating the inner Chambolle-Pock primal-dual loop that classically dominates TGV computation. On three synthetic
benchmarks (Gaussian, horizontally layered, curved-fault inspired by OpenFWI) using cross-well acquisition, 5% travel-time noise, and five seeds, MIMIR-TGV$^2$ ties a classical FMM-LSMR baseline with auto-tuned hyperparameters on the Gaussian ($p=0.134$, paired $t$-test) and significantly outperforms it on layered ($p<0.0001$, 44% RMSE reduction) and curved-fault ($p=0.0002$, 33% reduction). Replacing TGV$^2$ with TV degrades performance on Gaussian ($p=0.004$) and layered ($p=0.003$); curriculum-annealed TV improves Gaussian RMSE by only 5.4%, confirming that TV's staircase bias is intrinsic to the regularizer rather than a scheduling artifact. The results empirically validate the Bredies-Kunisch-Pock prediction that piecewise-affine priors are better suited to subsurface velocity recovery than piecewise-constant TV priors. We argue that the central design choice in physics-informed neural-field inversion is not the network architecture but the regularizer. The full pipeline reproduces in under one hour on consumer hardware.
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Computational Physics · Cross-listed from math.NA
Fast Evaluation of the Azimuthal Fourier Modes of the 3D Helmholtz Green's Function and Their Derivatives
Hanwen Zhang
We introduce an $O(M)$
algorithm for evaluating the azimuthal
Fourier modes $G_{k,m}$, $m = 0, 1, ..., M$, of the three-dimensional Helmholtz Green's function with real wavenumber $k$, together with all their first- and second-order derivatives with respect to the cylindrical source and target coordinates. The cost is independent of both the wavenumber and the source-target separation, and high relative accuracy is retained even for modes whose magnitude is exponentially small. The method combines contour deformation at a few boundary modes with a boundary-value formulation of the five-term recurrence in the mode index. Derivative quantities are obtained from stable recurrences, adding only a small constant factor to the cost of $G_{k,m}$ alone. Numerical experiments demonstrate high relative accuracy, linear scaling in $M$, and applications to modal boundary integral equation solvers for axisymmetric acoustic scattering, where the $k$-independent kernel evaluator makes dense per-mode linear
algebra the dominant cost.
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