Chemical Physics
Impurity-Preserved Density Matrix Embedding Theory for Local Electronic Excitations
Teng Zhang, Ze-Wei Li, Zhe-Bin Guan et al. (+1 more)
Density
matrix embedding theory (DMET), which is usually based on a Schmidt decomposition of Slater determinants by partitioning the full system into
impurity and environment in terms of local orthogonal orbitals (LOs), has demonstrated considerable promise in electronic structure studies because it enables the extraction of local properties using a high-level solver within an embedded
impurity subsystem with greatly reduced degrees of freedom, thereby achieving a balance between accuracy and computational cost. However, its application to excited states of strongly correlated systems, such as lanthanide complexes, remains challenging because the errors relative to all-electron results can still be significant. Motivated by the success of the previously developed atomic orbitals (AOs) based DMET framework (Ai, Li, and Jiang, Phys. Rev. Lett. 2025, 135, 026502.), termed AO-DMET, which attains improved accuracy by constructing the embedded subspace based on a non-orthogonal decomposition of the Slater determinant in terms of AOs, we propose a new LO-based partitioning scheme that fully preserves the
impurity space spanned by corresponding AOs and can achieve accuracy closely matching that of AO-DMET while retaining the orthogonal partition and its associated computational efficiency. The performance of the proposed method is demonstrated through excitation energy calculations for several representative lanthanide complexes. These results establish an efficient and accurate partitioning scheme for describing excited states in strongly correlated systems within the DMET framework.
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All authors Teng Zhang, Ze-Wei Li, Zhe-Bin Guan, Hong Jiang
Computational Physics · Cross-listed from cs.CE
A kinetic-diffusion Monte Carlo-based particle-level fluid-kinetic decomposition for neutral transport simulations
Zhirui Tang, Niels Horsten and Giovanni Samaey
Neutrals in the
plasma edge are commonly modeled by kinetic equations, with quantities of interest given by macroscopic quantities such as density, velocity, and temperature. In reactor-relevant regimes, fully kinetic descriptions solved by Monte Carlo (MC) methods, although accurate, become computationally expensive, whereas fluid-limit approximations are computationally more efficient but may lose accuracy due to boundary effects or low-collisional regimes. Hybrid fluid-kinetic approaches aim to combine the strengths of both descriptions. However, existing simulation methods face challenges, including interface handling in domain decomposition, unphysical assumptions, and iterative coupling in distribution decomposition. In this work, we propose a distribution-decomposition hybrid model constructed at the particle level based on the kinetic-
diffusion Monte Carlo (KDMC) method. The model inherits key properties of KDMC: it is asymptotic-preserving and does not require iterative coupling between the fluid and kinetic components. To improve the accuracy of the fluid-part quantities estimation, a Navier-Stokes-type fluid system is derived via
Hilbert-Chapman-Enskog expansions, tailored for KDMC. In the considered one-dimensional tests, the resulting fluid system has comparable accuracy to the AFN model used in SOLPS-ITER while requiring substantially fewer nonlinear iterations. Additionally, a tunable reflective boundary condition is introduced that allows balancing accuracy and efficiency. The model exhibits at least 500 times speedup over the kinetic MC, while maintaining relative L2 errors around 10% in a charge exchange (CX)-dominant test case. In non-CX-dominant regimes, the accuracy becomes increasingly sensitive to boundary treatment due to the inherent limitations of the fluid approximation near the boundary, motivating further refinement of the KDMC boundary conditions.
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Accelerator Physics
Monte Carlo_Based Beam Dynamics Study of a Compact Linear Accelerator for Localized X_ray Generation
Hossein Emami and Hossein Afarideh
Compact electron linear accelerators are being increasingly investigated as enabling platforms for localized X-ray generation and energy-controlled irradiation, due to the demand for a reduced footprint and improved beam control. In this work, a conceptual compact LINAC architecture with a fixed total length of 0.5 m is investigated through comprehensive Monte Carlo beam dynamics simulations, focusing on the comparative performance of two operating modes delivering electron beams at 60 keV and 300 keV, respectively. The study aims to isolate energy-dependent beam transport effects within an identical
lattice configuration, emphasizing transverse and longitudinal phase-space
evolution, emittance growth, beam envelope stability, and target spot formation. Particle tracking is performed using a statistical Monte Carlo framework incorporating RF cavity acceleration, simplified quadrupole focusing, and space-charge-induced
diffusion effects. Transverse and longitudinal phase spaces are analyzed at multiple locations along the accelerator, and key beam quality metrics are extracted at the exit. The results demonstrate that low-energy operation at 60 keV is strongly influenced by collective effects, leading to pronounced emittance growth and
halo formation, whereas the 300 keV mode exhibits significantly enhanced beam
rigidity, improved phase space preservation, and a compact beam spot at the target.
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Optics · Cross-listed from nucl-th
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang et al. (+1 more)
Precise knowledge of
nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic
tomography for imaging the atomic
nucleus. UPCs offer a pristine electromagnetic pathway: coherent vector-
meson photoproduction generates patterns of diffraction and two-source interference that directly encode the
nuclear spatial density. Turning these patterns into quantitative constraints is, however, a challenging inverse problem, complicated by correlated sensitivities to deformation and
neutron skin, phase smearing, and experimental backgrounds. Here we introduce an interpretable Multitask deep-learning framework that maps transverse momentum distributions to multiple
nuclear-structure indicators simultaneously and identifies the kinematic regions driving each inference. We demonstrate the approach with coherent $J/\psi$ photoproduction in $^{96}_{40}\text{Zr} + ^{96}_{40}\text{Zr}$ collisions, showing that the learned features separate diffraction-dominated and interference-dominated information and provide analysis-ready observables for future high-luminosity data.
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All authors Jing-Zong Zhang, Wang-Mei Zha, Lingxiao Wang, Guo-Liang Ma
Chemical Physics · Cross-listed from cond-mat.soft
Dissociation of NaCl in supercritical aqueous fluids of moderate and high concentrations: A molecular dynamics study
Mikhail V. Ivanov and Olga V. Alexandrovich
We report classical molecular dynamics simulations of NaCl association and dissociation in supercritical aqueous fluids over a wide range of salt concentrations, from moderate salinity to highly concentrated H2O-NaCl mixtures attainable at high temperatures. The degree of dissociation a and the corresponding ideal dissociation constant Kd, derived directly from a, were calculated as functions of the stoichiometric NaCl mole fraction at selected pressure-temperature (PT) conditions from 673.15 to 1273.15 K and from 0.1 to 2 GPa. At moderate salinity corresponding to a molality of approximately 1 mol/kg, NaCl remains largely dissociated a = 0.3-0.7 depending on pressure and temperature). In contrast, when the mole fraction of NaCl increases up to xNaCl = 0.333 (27.8 mol/kg), the degree of dissociation tends towards zero, and most ions form Na$^+$Cl$^-$ contact pairs and multi-ion clusters. As a result of these competing trends, the mole fraction of structurally dissociated Na$^+$ and Cl$^-$ ions is a non-monotonic function of the stoichiometric NaCl concentration and typically reaches a maximum at xNaCl = 0.06-0.10. This result shows that increasing salinity does not necessarily increase the abundance of structurally available chloride ions in supercritical aqueous fluids. Additional fixed density simulations at 1 and 7 mol/kg extend the analysis up to 1673.15 K and separate the effects of temperature and density on the associate/dissociate state of the ions. The obtained concentration dependences provide molecular-level constraints for thermodynamic descriptions of concentrated supercritical electrolytes and for evaluating chloride availability in high-temperature aqueous fluids.
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Fluid Dynamics
A Methodology to Quantify Interscale Energy Transfer at Solid Boundaries
Lennard Miller
Far away from solid boundaries, energy can be transferred between different flow scales due to the non-linear self-advection of velocity. This energy transfer can be quantified using well-established
Fourier diagnostics or filtering methods. However, these diagnostic tools fail to provide a physical representation of the linear energy transfer that may occur during the formation of oceanic boundary layers or Rossby wave reflections. In this document, I outline a novel filtering methodology that is able to quantify this linear energy transfer by combining coarse-graining with volume-penalization. Its utility is illustrated by quantifying the down-scale energy transfer occuring during a Rossby wave reflection off a western boundary. The conceptual framework developed here is thought to be broadly applicable to the study of multi-scale energetics of bounded geophysical fluid flows.
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Fluid Dynamics
Eulerian Lagrangian relations in decaying two dimensional incompressible Navier Stokes fluids across initial vorticity packing and Reynolds number
Snehanshu Maiti and Rajaraman Ganesh
Recent studies Vorticity packing effects on long time turbulent transport in decaying two dimensional incompressible Navier Stokes fluids, Phys. Fluids 38, 045159 (2026) demonstrated that, at a fixed high Reynolds number (Re), the initial vorticity packing fraction (VPF) governs the coupled Eulerian flow
evolution and Lagrangian tracer particle transport in decaying two dimensional incompressible Navier Stokes fluids, revealing a strong Eulerian Lagrangian relationship during the nonequilibrium inverse cascade regime and a direct Eulerian Lagrangian correspondence in the late time coherent
vortex quasi
equilibrium regime, wherein increasing VPF drives transitions from point
vortex to patch
vortex equilibria and from subdiffusive to superdiffusive transport. In the present work, we investigate how these Eulerian Lagrangian connections evolve across a broad (VPF, Re) parameter space. The results show that the Eulerian Lagrangian relationship remains largely preserved during the nonequilibrium inverse-cascade regime, where transport increases systematically with VPF and remains primarily controlled by VPF despite secondary Re dependent oscillatory modulation. In contrast, the late-time coherent-
vortex quasi-
equilibrium regime exhibits Eulerian statistical
equilibria that remain largely insensitive to Re, while the corresponding tracer-particle transport displays a strong Re dependence characterized by strong oscillatory and nonmonotonic variations across the (VPF, Re) parameter space, substantially weakening the VPF-ordered transport hierarchy observed in the inverse-cascade regime. Consequently, for the parameter range, spatial resolutions, and integration times explored in the present study, the direct Eulerian Lagrangian correspondence identified at fixed high Re is not universally maintained across the broader (VPF, Re) parameter space.
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Optics
Hexagonal Boron Nitride Spin Defects for Quantum Photonics: Annealing-Free Generation by Krypton Ion Implantation
Ikshvaku Shyam, Raj Singh, Mangababu Akkanaboina et al. (+11 more)
Controlled, reproducible generation of luminescent defect centres in hBN remains a key challenge for scalable
quantum-photonic technologies. Here, we report Kr$^{+}$ ion implantation as a tunable, annealing-free, and chemically inert route to room-temperature near-infrared luminescent
spin defects in hBN, requiring no pre- or post-implantation annealing. SRIM Monte Carlo simulations were used to optimise the parameters for 40 keV Kr$^{+}$ irradiation of hBN flakes. The implanted samples exhibit a stable near-infrared photoluminescence (PL) band centred at $\sim$830 nm whose intensity increases with implantation fluence over $10^{11}$-$10^{15}$ions/cm$^{2}$. Temperature-dependent PL measurements (20-300 K) reveal a linewidth broadening well described by a $T^{3}$ dependence, consistent with acoustic-
phonon-mediated dephasing. Raman spectra show the characteristic $E_{2g}$ mode of pristine hBN at $\sim$1366 cm$^{-1}$ alongside an implantation-induced defect feature at $\sim$1295 cm$^{-1}$, confirming irradiation-induced
lattice disorder. Electron paramagnetic resonance (EPR) measurements reveal a paramagnetic centre with a $g$-factor of 2.003, and density functional theory (DFT) calculations indicate that a spatially separated $V_{\mathrm{N}}$-$C_{\mathrm{B}}$ donor-acceptor pair complex is a viable origin of the observed optical and
magnetic signatures. Overall, Kr$^{+}$ implantation offers an effective, annealing-free, and scalable platform for generating stable room-temperature luminescent defects, providing a promising route toward
quantum photonics.
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All authors Ikshvaku Shyam, Raj Singh, Mangababu Akkanaboina, A. M. Sonawane, Muthu Satheeshkumar, Amrita Majumder, Ekta, Janhavi Jayawant Khunte, Akash Khaire, Kenji Watanabe, Takashi Taniguchi, Gopalan Rajaraman, S. S. Dahiwale, Anshuman Kumar
Computational Physics · Cross-listed from nucl-th
Scattering Observables from Few-Body Densities and Application in Light Nuclei
Alexander Long
The Transition Density Amplitude (TDA) method of Griesshammer et al. is applied to compute scattering observables for Compton scattering, threshold neutral pion photoproduction, and elastic pion scattering on the light
nuclei Hydrogen 3, Helium 3, Helium 4, and Lithium 6. In this formalism the amplitude factorizes into an irreducible few-body kernel, which encodes the interaction of the probe with the active nucleons, and a transition density amplitude, which carries the
nuclear structure information. Because the densities are computed once per
nucleus and momentum transfer and then convolved with any process-specific kernel, the same
nuclear input is reused across reactions, yielding substantial computational savings over direct evaluation. This factorization is realized in the publicly available Fortran suite DensityScattering, developed as part of the present work; a researcher implementing a new reaction need only supply the corresponding kernel in a prescribed format, with infrastructure for density handling, integration,
quantum-number summation, and output already provided. The TDAs are constructed from
nuclear wave functions using the semilocal momentum-space regularized
chiral potential of Reinert, Krebs, and Epelbaum at cutoffs of 450 and 500 MeV. For Lithium 6, no-core shell model wave functions are evolved under a Similarity Renormalization Group (SRG) transformation; a back-transformation scheme ("SRG-And-Back"), co-developed for the present work, returns the densities to the physical momentum scale and thereby preserves the original
chiral ordering. The induced uncertainty is validated against exact Helium 4 results at the 2% level, and convergence studies for Lithium 6 Compton scattering bound the residual uncertainty below 6%.
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Atomic Physics
Vortex photoelectron holography in strong-field tunneling ionization
Yongkun Chen, Oleg I. Tolstikhin, Toru Morishita et al. (+2 more)
Vortex electrons, characterized by a helical phase front, offer unique advantages for probing material structures. Such electrons can be generated via tunneling ionization in strong
laser fields. In this paper, we investigate the rescattering dynamics of
vortex photoelectrons by the parent ion. Specifically, we introduce
vortex photoelectron holography, extending conventional strong-field photoelectron holography (SFPH) from plane-wave to
vortex rescattering. By solving the time-dependent Schrödinger equation, we extract the
vortex scattering phase from the SFPH fringes, showing excellent agreement with scattering calculations. Thus, our work provides direct access to the
vortex scattering phase, paving the way for applying SFPH to structurally sensitive imaging with phase-engineered photoelectrons.
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All authors Yongkun Chen, Oleg I. Tolstikhin, Toru Morishita, Yueming Zhou, Peixiang Lu
Optics · Cross-listed from cond-mat.other
Synchronization in coherently and dissipatively coupled spinor polariton time crystals
I. Carraro-Haddad, A. Ramos-Pérez, G. Usaj et al. (+6 more)
The spinor degree of freedom associated to
exciton-polariton condensates can spontaneously self-oscillate breaking time translation
symmetry, thus showing a continuous time-
crystal (CTC) behavior. An open question in such driven-dissipative and non-linear
quantum open systems is what happens when CTCs are brought together to interact. Here we experimentally study polariton condensates in coupled traps, evidencing mutual induction and synchronization of the pseudospin temporal GHz dynamics in the CTC phase. The individual and relative orientation of the (limit cycle) precessing pseudospins can be tuned by the optical excitation power, displaying both ferro and anti-ferro dynamical configurations. We theoretically show that the
exciton reservoir, and both the coherent and long-range dissipative inter-trap coupling, play important roles in the CTC dynamics. The investigation of time-broken
symmetry is thus extended here to more complex non-hermitian systems opening the path to study self-sustained collective dynamics in
lattices of non-linear
quantum condensates.
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All authors I. Carraro-Haddad, A. Ramos-Pérez, G. Usaj, A. Bruchhausen, K. Biermann, P. V. Santos, A. S. Kuznetsov, A. A. Reynoso, A. Fainstein
Optics
Reconfigurable all-optical inference via tunable second-harmonic generation and spin-orbit coupling cascade
Li Zhang, Zikuan Zhuang, Ronghao Deng et al. (+10 more)
Spin-orbit coupling (SOC) is widely exploited as a fundamental mechanism for generating orbital angular momentum (OAM); however, conventional approaches typically lack flexibility and tunability. Here, we introduce a continuously tunable second-harmonic generation (SHG)-SOC cascade mechanism modulated by a spatially movable nonlinear
crystal. Under linearly polarized excitation, the SHG-SOC cascade engages synchronously with both degenerate and nondegenerate SHG processes, thereby expanding the OAM spectrum and significantly enhancing the information density and feature-mapping capacity of the optical field. Moreover, the OAM spectral distribution can be continuously reconfigured simply by translating the nonlinear
crystal. This deterministic physical
evolution, which maps simple OAM modes onto a tunable high-dimensional OAM space, is mathematically analogous to the high-dimensional feature expansion performed by a kernel function of a support vector machine (SVM) in machine learning. Such dimensional expansion can project linearly inseparable input data into a high-dimensional space where they become linearly separable. Exploiting this physics-
algorithm analogy, we develop a reconfigurable all-optical inference platform. As a proof of concept, we successfully perform classification tasks, including the recognition of Iris flowers and Palmer penguins. This work establishes a scalable, physically reconfigurable architecture for high-dimensional all-optical computing and neuromorphic photonics.
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All authors Li Zhang, Zikuan Zhuang, Ronghao Deng, Ling Hong, Yu Zhang, Fei Lin, Zhengxian Liu, Jingxuan Sun, Wenguo Zhu, Zhenwei Xie, Yongyao Li, Dongxu Zhao, Xiaocong Yuan
Physics and Society
Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics
Yajing Hao, Longzhao Liu, Xin Wang et al. (+3 more)
Network dismantling aims to identify a node removal sequence that can rapidly destroy network connectivity, which is an important problem for understanding the structural fragility of complex systems and designing intervention strategies. Existing studies mainly focus on pairwise networks or assume weak-deletion rules where node removal only causes hyperedges to shrink in higher-order networks. However, in many real higher-order systems, the failure of one participant may cause the entire group interaction to fail, i.e., the strong-deletion mechanism. Such a mechanism cannot be fully captured by projected networks or methods based on weak-deletion rules. To address this challenge, we propose hyper-Vulnerability-weighted Dominance rank (hyper-VDrank), a higher-order centrality method for hypergraph dismantling under strong deletion. Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments, where a node does not compete only with individual neighbors but responds to the collective pressure formed by other nodes in the same hyperedge. It further introduces a hyperedge vulnerability weight based on redundancy and size effects to capture the vulnerable structures, facilitating the distinction of critical nodes. Experiments show that hyper-VDrank reduces the largest connected component more rapidly, collapses the hypergraph earlier, and produces greater structural fragmentation than classical and recent methods. On 14 real-world hypergraphs, hyper-VDrank improves dismantling efficiency by 23.65% and reduces the collapse threshold by 27.63% on average compared with the baselines. In summary, hyper-VDrank offers an effective hypergraph dismantling tool and a new perspective on identifying vulnerable structures in higher-order complex systems.
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All authors Yajing Hao, Longzhao Liu, Xin Wang, Zhihao Han, Hongwei Zheng, Shaoting Tang
Fluid Dynamics
Data assimilation of flow MRI data into RANS models with algebraic closures
C. Namuroy, M. P. Juniper, P. Nair et al. (+4 more)
We adopt the
Bayesian inference framework to solve an inverse Reynolds-Averaged Navier-Stokes (RANS) problem for the approximate posterior probability distribution of the
turbulence model parameters and inlet boundary conditions of a confined turbulent jet. The data are noisy 3D flow MRI measurements of a Newtonian fluid flowing through the Food and Drug Administration (FDA) nozzle geometry. We assimilate this into RANS using two algebraic
turbulence models based on the mean shear rate magnitude and the
turbulence kinetic energy. We demonstrate that the inferred models are able to reconstruct the measured mean flow velocities without overfitting and we provide uncertainty estimates for the model parameters. The methodology can readily be extended to more complex RANS models, provided that they remain differentiable.
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All authors C. Namuroy, M. P. Juniper, P. Nair, M. Loecher, D. B. Ennis, A. Marsden, A. Kontogiannis
Geophysics
Frictional timescales and the impact of climate change-driven extreme weather on rainfall-triggered landslides in Mizoram, NE India
Pritom Sarma and Krishnendu Paul
Mizoram records the highest landslide frequency among all Indian states, yet physics-based models that predict the timing of slope failure remain unavailable for the region. Here, we apply the rate-and-state friction (RSF) block-slider framework of Paul et al.(2024) to 19 rainfall-triggered landslides in and around Aizawl (2016–2025) to investigate the hydro-mechanical coupling between pore-pressure transients and the slow-to-fast transition of slopes hosted in Miocene shale-dominated lithology. For each event, satellite-derived (GPM) rainfall is propagated to failure depth using a 1D infiltration model across three hydraulic conductivity scenarios, and RSF parameters are inverted to reproduce the observed failure date. The resulting dimensionless normalized pore-pressure $\chi = \mu_0 \Delta P / (a\,\sigma'_f)$ cleanly separates the 19 events into two dynamically distinct failure regimes: synchronous failure ($\chi \gtrsim 4$, zero delay from peak pore-pressure), exemplified by the eight-event Cyclone Remal cluster of May 2024, and delayed failure ($\chi \sim 1$–$3$, delays of hours to 10~days), controlled jointly by $\chi$ and the velocity-weakening ratio $a/b$. Using CMIP6 extreme-rainfall scaling factors for northeast India under SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, we project that the fraction of landslides falling in the zero-warning synchronous regime increases from the current $\sim$56% to $\sim$72% under SSP5-8.5. Our results imply a significant
climate-driven escalation of multi-site, clustered landslide failure risk driven by the intensification of extreme precipitation events.
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Biological Physics
A Nonequilibrium Internal-Time Model of Aging: Entropy-Normalized Biological Proper Time and Repair Bifurcations
Mesfin Taye
Chronological age is an incomplete coordinate for aging. Individuals and species sharing the same calendar time can differ substantially in physiological reserve, molecular damage, mortality hazard, and remaining lifespan. The Principle of Biological Time Equivalence (PBTE) offers a thermodynamic reformulation: biological aging is governed by the accumulation of internal physiological time rather than chronological time alone. Building on prior PBTE work, this paper defines the internal-time coordinate $\theta(t)=\int_0^t f(s)\dd s$, where $t$ is chronological time and $f(s)$ is an instantaneous physiological frequency (for example heart rate or respiratory rate), so that $\theta$ is the accumulated count of physiological cycles. Its
entropy-normalized extension is $\Tsig(t)=\int_0^t[\sigz(s)/\sref]f(s)\dd s$, where $\sigz(s)=\dd\Sigma/\dd\theta$ is the
entropy produced per physiological cycle (the
entropy cost per biological tick), $\Sigma$ is cumulative
entropy production, and $\sref$ is a fixed reference
entropy cost per cycle used as a normalizing unit. The normalized PBTE age $\APBTE(t)=\Tsig(t)/\Nref$ measures the fraction of a reference
entropy–cycle budget consumed, where $\Nref$ is the reference number of
entropy-weighted cycles available over a lifetime. The manuscript is explicitly theoretical: no empirical cohort is analyzed, and the numerical demonstrations are synthetic stress tests rather than validation.
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Computational Physics
Parallelized contraction of tensor trains or matrix product operators
Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka et al. (+1 more)
Tensor Trains (TT), also known as
Matrix Product States (MPS) and
Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and operators. One of the most expensive manipulations involving tensor trains is the contraction of two MPOs. A popular and accurate method for mitigating this cost is the fit
algorithm. However, it is still comparatively costly since it involves 2-site updates. Moreover, the parallelization of the fit
algorithm when used for MPO-MPO contractions has received comparatively little
attention. In this work, we present two strategies for accelerating the fit
algorithm, usable in combination: (1) We use MPI-based distributed-
memory parallelization tailored for MPO-MPO contractions, employing one of two MPO gauge choices: (1a) the inverse canonical gauge, which yields near-ideal parallelization speedup across all problem sizes; and (1b) the site-canonical gauge, which avoids the inversion of singular values but requires extra computations to ensure global consistency, thus yielding excellent parallelization speedup only for large problems requiring several sweeps before convergence. (2) We use randomized projections to reduce the cost of local updates from 2-site to 1-site costs while retaining 2-site accuracy, and to speed up contractions of environment tensors with MPO tensors.
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All authors Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka, Jan von Delft
Fluid Dynamics
Scaling of the minimal energy for turbulence transition in pipe flow
Patrick Keuchel, Daniel Morón and Marc Avila
Predicting the transition of
turbulence in pipe flow remains a fundamental problem in fluid dynamics. We use a variational approach to compute nonlinear optimal perturbations to the laminar flow at Reynolds number $Re\leq 5000$. As $Re$ increases, optimal perturbations remain structurally similar, but increasingly localize while their thickness scales as $\delta_r \propto Re^{-1/3}$. They grow via the Orr mechanism, followed by a phase of strong nonlinear interaction of oblique waves and a lift-up phase. The energy gain during the Orr phase increases linearly with $Re$ and is independent of the initial perturbation energy, $E_0$. The energy gain during the oblique and lift-up phases is governed by nonlinearities and scales as $\propto Re^2$. We find that regardless of the Reynolds number, transition occurs if the energy of the perturbation exceeds a constant threshold. As a result, the minimum perturbation energy required to cause transition in pipe flow scales as $\mathcal{O}(Re^{-3})$.
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Chemical Physics
Reduced-Alphabet QUBO/Ising Formulation for Constraint-Driven Cyclic Peptide Sequence Design
Yan Zhou, Yuqi Wang and Xin Li
Cyclic peptide design requires balancing local residue preferences with constraints from ring-forming chemistry, residue spacing,
topology, target compatibility, and developability. Here, we present a reduced-alphabet quadratic unconstrained binary optimization (QUBO)/Ising formulation for constraint-driven cyclic peptide sequence design. Amino acids are grouped into physicochemical or interaction-based residue classes, and peptide positions are represented by binary residue-class assignment variables. The objective combines one-hot sequence validity, cyclization constraints, optional target-compatibility terms, motif and composition rules, and coarse developability proxies. By modifying the relevant constraint terms, the same framework can represent head-to-tail, disulfide-bridged, stapled, and bicyclic peptide designs. A resource-aware eight-class alphabet motivated by MJ interaction-profile clustering is used as a default representation to balance coarse interaction-pattern preservation with encoding cost. The resulting QUBO/Ising objective is solver-agnostic and can be explored using classical or
quantum-compatible binary optimization procedures. The model is intended as an early-stage search-space reduction and prioritization layer: it produces low-energy residue-class sequences rather than final molecular candidates, which require amino-acid decoding, cyclization-aware construction, and downstream structural or experimental validation.
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Computational Physics
Properties of the matrix functions arising in exponential integrators with applications to stochastic simulations of PDEs
Elliot J. Carr
The
matrix exponential and the closely related
matrix phi-functions play a fundamental role in the solution of first-order systems of ordinary differential equations (ODEs). In particular, they appear in exact solutions of certain linear ODE systems, and in exponential integrators, a class of explicit time discretisation methods for computing approximate solutions of stiff semi-linear ODE systems. Fundamental properties of the
matrix exponential include that it is nonnegative if the
matrix is essentially nonnegative and
stochastic if the
matrix is a transition-rate
matrix. In this paper, we study related properties for the
matrix phi-functions. Using these properties, we then provide insights into deterministic solutions of linear and semi-linear ODE systems and outline how such solutions can be used to generate
stochastic simulations to quantify variability in model predictions. The paper concludes with some illustrative examples exploring the theory for some ODE systems arising from spatial discretisation of PDEs of
diffusion, advection-
diffusion, Fisher-KPP and Allen-Cahn type.
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Plasma Physics
Multi-objective Bayesian optimisation of a double-layer target for quasi-monoenergetic TNSA protons
Chengqi-Zhang, Yang He, Mamat Ali Bake et al. (+1 more)
We carry out a six-parameter multi-objective
Bayesian optimisation of a carbon–hydrogen double-layer target for target-normal-sheath
proton acceleration. The campaign consists of 80 two-dimensional EPOCH simulations with the
laser amplitude $a_0$, pulse duration $\tau$, carbon-layer thickness $L_1$, hydrogen-layer density $N_2$, hydrogen-layer thickness $L_2$ and hydrogen-layer radius $r_p$ as input variables. Each final
proton spectrum is scored by the peak energy, the charge fraction inside a $\pm10\%$ peak-energy window and the charge in that window. Among the Pareto-set evaluations, the cases with peak energies between 64 and 71 MeV occur near $a_0=30$, $\tau=45$ fs, $L_1=0.3\,\mu{\rm m}$, $L_2=30$ nm and $r_p=0.15\,\mu{\rm m}$. Along this branch, increasing $N_2$ raises the in-window charge and increases the bandwidth. The small rear-layer radius keeps the
proton source within the flat central region of the transverse sheath field, where the accelerating field is nearly uniform. A 3D calculation is performed for the intermediate-density case $N_2=11.85\,n_c$, which balances bandwidth and in-window charge along this branch. The corresponding 2D spectrum has $E_{\rm peak}=67.4$ MeV and $\Delta E/E=18.8\%$, whereas the 3D spectrum has $E_{\rm peak}=34.1$ MeV and $\Delta E/E=7.0\%$. The lower 3D peak energy and narrower bandwidth are associated with an earlier decay of the rear-sheath field and an earlier saturation of the
proton peak energy, and the quasi-monoenergetic peak is retained in 3D.
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All authors Chengqi-Zhang, Yang He, Mamat Ali Bake, Bai-Song Xie
Optics
A unified perspective on wavelength selection for molecular composition inference from diffuse spectroscopy
Kevin Scibilia, Marcel Rosier, Luca Giannoni et al. (+6 more)
Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extract molecular, or more precisely, chromophore concentration from a broad-range spectrum of the reflected light. However, selecting the shortest wavelength range sufficient for quantitative optical monitoring remains an open problem. Various wavelength optimization methods are scattered throughout the literature, however, there is no unified view to date. Our work's motivation is to construct a wavelength selection framework by unifying existing selection approaches and propose a novel projection-based method that allows for the pre-identification of a wavelength range that is adequate for the final selection. The framework specifically focuses on proposing different methods that quantify match or mismatch between the chosen light-matter interaction model, defined by the chosen endmembers (e.g. molecular chromophores), and the measured intensity from the spectroscopic data. To evaluate the framework, we perform a retrospective analysis on a broadband spectroscopy
dataset of piglets during an induced hypoxia-ischemia state. Overall, we show that our novel projection-based method can be used for band selection, and that existing approaches can be used in conjunction to select an optimal minimal wavelength set that satisfies biophysical model constraints.
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All authors Kevin Scibilia, Marcel Rosier, Luca Giannoni, Pietro Ricci, Frédéric Lange, Francesco S. Pavone, Ilias Tachtsidis, Daniel Rueckert, Ivan Ezhov
Data Analysis and Statistics
Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy
Yifan Wang
Hybrid models combine trusted physics with data-driven correction, but a physical model is rarely wrong everywhere or in the same way. The key diagnostic question is local: where does the model fail, what missing mechanism explains the failure, and is the evidence statistically real? Existing sparse-discovery and discrepancy-learning methods usually fit one global correction, which can spread a local error into clean regimes, bias trusted physical parameters, and provide no calibrated significance for selected terms. We introduce LISDD, Localized, Identifiable Sparse Discovery of Discrepancy, a framework that localizes model error to an operating regime, identifies a sparse symbolic form for the missing mechanism, and certifies the discovery with an exact finite-sample test. LISDD fits the known physics on an automatically detected clean regime, flags discrepant regions with a calibrated residual-energy statistic, selects the local missing term by exhaustive holdout over a candidate library, and confirms significance with a sample-split $F$-test. A false-discovery-rate extension handles multiple discrepant regions with different missing mechanisms. In controlled experiments, LISDD keeps physical-parameter bias at 0.002 versus 0.43 for global-discrepancy and black-box baselines, raises localization $F_1$ from 0.44 to 0.80, recovers the correct symbolic form with probability one, attains exact detection, and controls the multi-region false-discovery rate while recovering every planted mechanism. The result is a calibrated diagnostic tool for grey-box building-energy models when a fixed physical law silently breaks in one operating regime.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
Jonas Hänseroth, Aaron Flötotto and Christian Dreßler
Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling when reactive events are involved remains unsettled. We compare five universal MLIPs across seven chemically diverse systems and find that strong performance on standard
benchmarks does not guarantee accurate predictions of target observables. In particular, zero-shot models do not reliably reproduce reactive, transport, or high-barrier processes, exemplified here in particular by the sulfur-vacancy jump in MoS$_2$. We therefore propose a practical alternative: universal MLIPs are used to generate long molecular dynamics trajectories, the resulting configurations are sub-sampled and relabeled with DFT, and material-specific MLIPs are subsequently trained or fine-tuned on the resulting first-principles
datasets. This workflow converts universal models into efficient configuration-space generators while retaining ab initio reference labels for training. Across the tested systems, $2{,}000$ DFT-recalculated structures are often sufficient to obtain accurate fine-tuned or trained-from-scratch models. For the most challenging case, iterative self-training progressively refines the sampled configuration space and recovers the DFT MoS$_2$ potential energy profile with only $600$ first-principles calculations in total. The resulting workflow enables the generation of $1$ ns ab initio-quality trajectories - including training data generation and model creation - within three days.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Hierarchical Unsupervised Clustering of Nanobeam Electron Diffraction Data Using Sparse Bragg-Vector Representations
Ian MacLaren, Ala Al-Afeef, Trevor Almeida et al. (+5 more)
If disk detection is applied to nanobeam electron diffraction
datasets, then the results are effectively a list of vectors describing the position of every diffraction peak in real and reciprocal space. This is the natural territory for the application of clustering
algorithms, and they are shown to be highly effective at decomposing such
datasets and automating imaging and analysis. In particular, this is found to work particularly well in a hierarchical clustering approach, where a first level is concerned with finding clusters of similar diffraction vectors and a second level with grouping those diffraction vector clusters that originate from the same real space area (i.e.
crystal or crystallographic variant). Examples are shown in both polycrystalline and single
crystal (with precipitates) systems. Additionally, automated separation of amorphous or deeply nanocrystalline components is also found to be possible in an optional third level of clustering, allowing composite images of both amorphous and crystalline components in partially crystallised samples to be easily and automatically generated. These advances promise to increase throughput in atomic structure analysis with nanobeam diffraction, and also make finding minor components much easier. They can also serve as a preliminary step towards more detailed crystallographic or
crystal size/shape distribution analysis.
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All authors Ian MacLaren, Ala Al-Afeef, Trevor Almeida, Rantej Bali, Shriyar Tariq, Emily Wackan, Luke Daly, Joshua F. Einsle
Optics
Silicon Ring Based 64$\times$100 GHz Wavelength Division Multiplexing filter
Q. Deng, A. Elshazly, M. Oktay et al. (+17 more)
Silicon-based wavelength division multiplexing (WDM) filters are essential for scaling optical communication capacity in data centers and telecommunications networks.However, extending silicon WDM systems beyond 32 channels with 100 GHz spacing poses significant challenges due to limitations in conventional filter architectures. Here we present the first silicon 64$\times$100 GHz WDM filter by introducing a novel ring-Mach-Zehnder interferometer (MZI) cascade architecture. Our design utilizes third-order
polynomial interconnected circular (TOPIC) bends to construct low-loss half-ring waveguides, facilitating an MZI configuration where the arm length difference is determined entirely by half of the ring structure. This approach ensures precise
alignment between the MZI interference peaks and the ring resonator wavelengths, with the MZI FSR being exactly double that of the ring, eliminating the need for dynamic tuning between the MZI and the ring. We demonstrate the concept through a 16$\times$400 GHz WDM filter with insertion loss of 1.3$\pm$0.6 dB and channel isolation $\geq$14.3 dB. The 64$\times$100 GHz implementation, realized using a 4-channel interleaver followed by four 16$\times$400 GHz WDM filter, achieves insertion loss of 3.2$\pm$1.1 dB and channel isolation $\geq$10.7 dB. This work opens new possibilities for high-density silicon photonic WDM systems, addressing the growing bandwidth demands of artificial intelligence and machine learning applications.
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All authors Q. Deng, A. Elshazly, M. Oktay, J. De Coster, R. Magdziak, C. Marchese, G. Lepage, D. Bode, H. Kobbi, H. K. Tyagi, J. R. Vaskasi, M. E. Filipcic, H. Sar, M. Chakrabarti, D. Velenis, P. Verheyen, P. Absil, F. Ferraro, Y. Ban, J. Van Campenhout
Instrumentation and Detectors · Cross-listed from cond-mat.mtrl-sci
Toward in-situ/operando X-ray absorption spectroscopy and electrochemical characterization of solid oxide fuel cells
Renato A. N. de Oliveira, Rafael Galiza Yoshimura, Liliana Mogni et al. (+15 more)
The focus of the present work is the development of specialized experimental instrumentation compatible with synchrotron characterization for in-situ and operando symmetric intermediate temperature solid
oxide fuel
cells (IT-SOFC) studies at maximum temperatures of 800 C , exposed to reducing and oxidizing atmospheres, using fluorescence X-ray absorption spectroscopy (XAS) measurements in combination with electrochemical impedance spectroscopy (EIS) in the multipurpose Quati beamline at CNPEM/SIRIUS synchrotron facility [1]. Symmetric IT-SOFC are gaining importance due to their structural simplicity, as they allow for the use of identical materials on both sides of the fuel
cell electrolyte; the anode, and the cathode [ 2,3 ]. The symmetric configuration opens new opportunities for fundamental research of electrode materials and improves the versatility of SOFC electrochemical devices [2,3].
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All authors Renato A. N. de Oliveira, Rafael Galiza Yoshimura, Liliana Mogni, Maurico Arce, Diego G. Lamas, Lucia Toscani, Maria Belén Arcentales Vera, Esteban Elvis Asto Ramos, Magna Monteiro Schaerer, Celeste Z.A. Aquino, Marcia Carvalho de Abreu Fantini, Taofeeq Oladayo Bello, Tereza da Silva Martins, Danilo Waismann Losito, James Moraes de Almeida, Valeria Spolon Marangoni, Leopoldo Suescunand, Santiago Figueroa
Optics · Cross-listed from cs.ET
General-Purpose Nonlinear Function Approximation via Linear Integrated Photonics
Ayana Mizuno, Isamu Takai, Makoto Nakai et al. (+3 more)
Photonic computing has emerged as a promising platform for accelerating artificial intelligence workloads by enabling low-latency and energy-efficient linear operations such as vector-
matrix multiplication. However, scalable on-chip high-order nonlinear processing remains challenging, limiting the functional versatility of current photonic hardware. Here, we present an optoelectronic approach for approximating high-order and high-dimensional nonlinear functions. The key to this approach lies in optical random
Fourier feature mapping, which transforms nonlinear function evaluation into an equivalent linear computation. This approach enables nonlinear computing within a linear photonic framework, eliminating the need for complex optical nonlinear or active materials while preserving scalability and computational throughput in a simple silicon photonic
circuit. We experimentally demonstrate a broad class of nonlinear functions, including tenth-order Legendre
polynomials, computationally demanding special functions (Voigt,
Fermi-Dirac, and Fresnel), neural-network activation functions, two-dimensional nonlinear functions, and a 10-dimensional softmax layer. This work establishes a general and scalable strategy for nonlinear computing in photonic integrated hardware and opens a pathway toward fully functional optical accelerators for next-generation computing systems.
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All authors Ayana Mizuno, Isamu Takai, Makoto Nakai, Atsutaka Miyamichi, Ryuichi Konishi, Satoshi Sunada
Atomic Physics
An Al$^+$ clock with $1.6\times10^{-18}$ systematic uncertainty and its frequency ratios
Fabian Dawel, Johannes Kramer, Derwell Drapier et al. (+15 more)
Advances in optical clocks motivate a redefinition of the second, requiring rigorous evaluations of systematic uncertainties and robust consistency among the clocks. Here, we report the full evaluation of the systematic frequency shifts of an $^{27}\mathrm{Al}^+$ single-ion clock, and the measurement of its absolute frequency and frequency ratio with a $^{87}$Sr optical
lattice clock at PTB. The evaluated total systematic fractional frequency uncertainty is $1.6\times10^{-18}$, mainly limited by the accuracy of the relevant atomic coefficients and by background gas collisions. The absolute frequency of the clock has been measured to be $\nu_{\mathrm{Al}^+}=1 121 015 393 207 859.19(24)\,$Hz, obtained by comparison with two primary caesium fountain clocks at PTB. The frequency ratio between the Al and Sr optical clocks has been determined to be $\nu_{\mathrm{Al}^+}/\nu_{^{87}\mathrm{Sr}}=2.611 701 431 781 462 668(36)$, limited by the accuracy of the Sr clock. This ratio differs by $8.6\sigma$ and $1.2\sigma$ from the 2021 and 2025 frequency ratio published by the BACON collaboration, respectively. These results represent an important contribution toward a future redefinition of the second using optical clocks, and underscore the importance of independent measurements of clock-candidate frequency ratios across different institutions.
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All authors Fabian Dawel, Johannes Kramer, Derwell Drapier, Lennart Pelzer, Kai Dietze, Mirza A. Ali, Marek Hild, Vincent Barbé, Steven A. King, Joshua Klose, Kilian Stahl, Johannes Rahm, Navraj Poudel, Sören Dörscher, Stefan Weyers, Erik Benkler, Christian Lisdat, Piet O. Schmidt
Optics
Double-slit optical ventriloquism: High phase sensitivity via diffraction patterns
Merav Kahn, John C. Howell and Nadav Katz
High-sensitivity phase sensing is traditionally performed using complex interferometric configurations. As an alternative, we present a robust and simple system based on the classical Young's double-slit experiment that leverages the "optical ventriloquism" effect to amplify phase signals. This phenomenon arises from super-oscillations near intensity minima, which cause an anomalous shift of the local wave vector as a consequence of weak-value behavior. In this work, we constructed an accessible experimental setup that translates minute phase differences into measurable spatial displacements of the diffraction pattern. We compare the detection performance of an sCMOS camera and a quadrant-
cell detector, analyzing the noise sources that limit the system's sensitivity. Our results demonstrate that engineering the dark regions of simple diffraction patterns can provide a foundation for advanced optical sensing technologies with minimal structural complexity.
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