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Physics Physics

physics Watch Physics

Wednesday 15 July

Fluid Dynamics

Turbulent boundary layers altered by passively rotating discs

Max W. Knoop and Pierre Ricco

Turbulent boundary layers characterised by friction Reynolds numbers in the range $Re_{\tau} = 880 - 1460$ and flowing over flush-mounted passively rotating discs are investigated in a wind tunnel with the purpose of reducing the skin-friction drag. The test surface is composed of thirty-two rotating discs arranged in a staggered configuration and supported by bearings mounted in cylindrical cavities. As the discs are half covered by thin rigid plates, a steady rotation of the discs is sustained via the asymmetric distribution of the wall-shear stress exerted by the wall turbulence on the exposed halves of the discs. Direct force measurements reveal that the drag increases with respect to a flat-plate case because of the flow interaction with the disc housings and the covering plates. The effect of the disc motion is isolated and a 3% drag reduction is measured with respect to the flow over stationary discs. The skin-friction identity by \cite{Elnahhas_Johnson_2022} (J. Fluid Mech., vol. 940, 2022), extended herein to include the disc-flow effects, is utilised for the first time to analyse experimental data. This direct slip effect, quantified by using the measured disc angular velocities in the Elnahhas-Johnson identity, is negligible. Measurements obtained by particle image velocimetry disclose that a roughness mean-flow effect occurs between adjacent discs because of the clearance gaps around the discs and that a downwash secondary flow exists near the covering plates, analogous to flows over streamwise-elongated rectangular roughness elements. This downwash velocity is streamwise modulated because of the spanwise disc motion and alters the wall-normal transport term in the Elnahhas-Johnson identity, thus reducing the drag locally.
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Optics

Photonic Ising machines toward and beyond a million spins

A. Aadhi, Nayem Al-Kayed, Tristan Austin et al. (+8 more)

Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.
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All authors A. Aadhi, Nayem Al-Kayed, Tristan Austin, Charles St-Arnault, Nir Rotenberg, Hitesh Ballani, David V. Plant, Edwin Ng, Yoshihisa Yamamoto, Peter L. McMahon, Bhavin J. Shastri

Physics and Society

Impacts of social and impact heterogeneity on social-climate outcomes

Amrita Punnavajhala, Brian Beckage, Madhur Anand et al. (+3 more)

Regional heterogeneity in social characteristics, temperature change, and vulnerability to climate impacts is likely to influence the magnitude of anthropogenic climate change, but has not been considered in coupled social-climate models, which seek to represent interactions between social and climate dynamics. Here, we examine how the projected mean global temperature anomaly and population support for mitigation respond to heterogeneity in these factors across five regions of the world, using a coupled social-climate model. We find that heterogeneity in climate impacts increases the temperature anomaly by 0.2$^\circ$C, while social heterogeneity increases it by an additional 0.1$^\circ$C. The projected temperature anomaly also increases with higher variability in climate impacts across regions, even for the same average global climate impact. Finally, we identify a social-climate tipping point, where low vulnerability to impacts under existing social conditions in one region can tip the system into an alternative slow-mitigation, high-temperature state. Our results show that heterogeneity in climate impacts leads to higher global mean temperatures and efforts to reduce global disparities could improve both social and climate outcomes.
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All authors Amrita Punnavajhala, Brian Beckage, Madhur Anand, Daniele Visioni, Jared Farley, Chris T. Bauch

Applied Physics

Chiral Landau levels induced by two in-plane pseudomagnetic fields in underwater acoustic metamaterials

Jiao Shen, Zhiyong Chang, Xinzong Wang et al. (+6 more)

The chiral zeroth Landau levels (LLs) constitute topologically protected bulk states that enable robust control of acoustic wave propagation. Given the central role of underwater acoustics in marine engineering, realizing such Landau-level physics in underwater acoustic systems is highly desirable. Nevertheless, existing studies have primarily been limited to airborne acoustic systems, and the implementation of chiral zeroth LLs in underwater acoustics remains a challenge due to the unavoidable fluid-solid interactions. In this study, we realize two kinds of chiral LLs in an open underwater spoof surface acoustic wave (SSAW) platform by introducing two perpendicular in-plane artificial pseudomagnetic fields (PMFs), oriented along the x and y directions, respectively, and reveal that scalar acoustic fields in water and vectorial elastic vibrations in solids can be jointly manipulated within a unified framework. Specifically, by strategically opening bandgaps at the Dirac points, position-dependent effective mass terms are introduced into the Dirac Hamiltonians, thereby synthesizing two in-plane PMFs. This results in the emergence of chiral LLs, which is confirmed both numerically and experimentally. The unidirectional propagation of the chiral LLs and their robustness against defects are also demonstrated. In addition, we achieve flexible manipulation of underwater ultrasonic energy carried by SSAWs, including beam splitting and arbitrary wave steering. Dual-band chiral LLs are also observed in small-scale underwater topological metamaterials. Our work provides a new route toward SSAW-based underwater ultrasonic control, opening opportunities for multiband underwater acoustic signal processing and detection, as well as underwater acoustic energy harvesting.
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All authors Jiao Shen, Zhiyong Chang, Xinzong Wang, Cheng Lin, Tuo Liu, Xiaoxiao Wu, Yifan Zhu, Haiyan Fan, Hui Zhang

Plasma Physics

Boronization-enabled I-mode on EAST tokamak with an expanded density window and favorable-configuration access

X.M. Zhong, X.L. Zou, A.D. Liu et al. (+19 more)

I-mode is a promising confinement regime for future fusion reactors because it combines enhanced energy confinement with L-mode-like particle transport and naturally ELM-free operation. Previous EAST I-mode studies were performed exclusively under lithium-conditioned wall conditions. Here we report the first systematic experimental investigation of I-mode under boronized wall conditions on EAST and compare it with an existing lithium-conditioned I-mode database at the same toroidal field, $B_t = 2.47$\,T. The boronized-wall dataset exhibits a substantially broader accessible density range, with the Greenwald fraction extending from $f_{\mathrm{GW}} = 0.26 - 0.77$ , compared with $f_{\mathrm{GW}} = 0.35 - 0.54$ under lithiation. A higher normalized $\mathrm{D}_\alpha$ emission suggests that enhanced edge recycling may contribute to this density extension. A striking increase in favorable-configuration I-mode is also observed: $51\%$ boronized-wall discharges are obtained in favorable-configuration, compared with only $8\%$ lithium-conditioned discharges. These favorable-configuration cases are concentrated at high density and exhibit a deeper radial electric-field($E_r$) well and stronger $\mathbf{E_r}\times\mathbf{B}$ velocity shear. When ETRO is present, the associated transition between electron and ion turbulence is similar under the two wall conditions, although ETRO occurs less frequently ($15\%$) under boronization. An empirical EAST I-mode energy confinement scaling at fixed $B_t$ is obtained, $\tau_E = 3.29 I_p^{0.51 \pm 0.10} P_{\mathrm{loss}}^{-0.53 \pm 0.05} \bar{n}_e^{0.08 \pm 0.07}$, indicating weaker power degradation than IPB98(y,2) H-mode scaling and a weak density dependence. These results show that boronization can broaden the operational space of EAST I-mode and support the development of reactor-relevant ELM-free scenarios.
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All authors X.M. Zhong, X.L. Zou, A.D. Liu, L.Q. Xu, B. Zhang, C. Zhou, J.P. Qian, X.Z. Gong, Y.T. Song, G. Zhuang, W.X. Shi, L.T. Gao, S.F. Wang, Y.H. Guan, G.Z. Zuo, T.Q. Jia, Y.X. Cheng, S.X. Wang, K.N. Geng, H.L. Zhao, EAST I-mode Working Group, EAST Team

Computational Physics · Cross-listed from math.NA

Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs

Jiale Linghu and Yangshuai Wang

Random feature collocation fixes a randomly generated trial space and determines its coefficients from a linear least-squares system. Stability then depends on whether the sampled residual equations represent the geometry induced by the differential operator. We construct an operator-aware discretization in which the operator-applied features determine both the collocation measure and a coefficient whitening map. The randomized scheme combines a residual-Christoffel density with inverse-density weights, while a deterministic scalar-row alternative maximizes successive regularized log-determinant increments. Conditional on the realized trial space, the sampled whitened interior Gram is a spectral approximation to the reference Gram on the retained residual space, with sample complexity linear in the retained dimension up to a logarithmic factor. For uniformly analytic residual kernels, the associated operator has stretched-exponentially decaying eigenvalues and ridge effective dimension that is polylogarithmic in the inverse ridge scale. Experiments on scalar and vector equations, varied geometries, and one to three spatial dimensions show that residual-space sampling and whitening produce numerically full-rank transformed systems with substantially smaller condition numbers and iteration counts. The deterministic construction attains the lowest errors at the smallest scalar sample sizes. Residual-space geometry therefore yields a principled design for stable strong-form random feature collocation.
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Fluid Dynamics · Cross-listed from math.NA

Data driven non-equilibrium moist phase exchanges for atmospheric convection within a discontinuous Galerkin model of the compressible Euler equations

David Lee, Kieran Ricardo and Junwei Lyu

A discrete formulation of the multiphase moist compressible Euler equations is presented for which the energy and tracer variance conserving dynamics is coupled to the non-equilibrium moist phase exchanges in a thermodynamically consistent manner. A neural network is then trained to learn the mass exchanges between vapour, liquid and ice phases in atmospheric convection. The network is trained on convection resolving output from a regional configuration of the LFRic model with a multi-moment microphysics parameterisation (CASIM). The loss function for learning these phase exchanges is formulated under the assumptions of thermal and mechanical equilibrium (same temperature and pressure for all phases), and chemical dis-equilibrium (different Gibbs free energies for all phases). The network outputs determine the exchanges of vapour, liquid and ice so as to conserve mass, and the resulting change in entropy is determined from the network outputs so as to conserve energy. The neural network is implemented in a thermodynamically consistent manner within the discontinuous Galerkin moist compressible Euler model in order to simulate the formation of three-phase clouds for convection at sub-km resolution. The results are compared to those from a physics based representation of three-phase moist processes at thermodynamic equilibrium, as well as to an idealised configuration of LFRic model.
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Chemical Physics

How Well Can Frontier Large Language Models Generate Structures? High Quality Prediction of Molecular Geometries with Help from Fine-Tuning

Joseph M. Cavanagh, Jonathan B. Arnold, Giovanni Battista Alteri et al. (+2 more)

The power of Large Language Models (LLMs) has led us to investigate how they might be fine-tuned for learning the "language of molecular geometry". The fine-tuning of the LLMs using Cartesian coordinates or Z-matrices provides an extremely simple method for accurately predicting equilibrium structures and diverse sets of conformers of small organic and drug-like molecules with excellent accuracy and outperforming specialized deep learning models. While the most common representation of molecular geometries are Cartesian coordinates performs adequately, we find that the inherent invariances and relational nature of geometries represented as Z-matrices provides a better grammar for LLM adaptation. Finally, we show that enhancing an LLMs capabilities for robust prediction of small molecule geometries still retains nearly all of its pre-trained language abilities by randomly mixing in small quantities of natural language prompt-response pairs into the fine-tuning.
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All authors Joseph M. Cavanagh, Jonathan B. Arnold, Giovanni Battista Alteri, Andrew Gritsevskiy, Teresa Head-Gordon

Tuesday 14 July

Physics and Society

Shifting Social Dispositions, Stable Prosocial Traits: A Global Age-Period-Cohort Analysis of Human Personality

Paul X. McCarthy, Xian Gong, John A. Johnson et al. (+3 more)

Generational stereotypes are widespread, but they often rely on anecdotes, and it remains challenging to disentangle true birth-cohort differences from the universal effects of ageing and historical periods. Using a statistical approach that separates effects of age, calendar time, and cohort, we analyzed Big Five personality data (five broad dimensions of personality) from N=773,714 individuals assessed across 30 years. Traits that shape social interaction diverged between generations, whereas a prosocial core comprising morality, discipline, and emotional awareness was stable across cohorts. Generation Z (born between 1995 and 2012) showed lower excitement seeking and gregariousness alongside higher self-consciousness and anxiety. These findings suggest that cohort change is selective rather than global: the dispositions through which people engage with the social world may be adapting to contemporary cultural conditions, while core prosocial tendencies remain stable across generations.
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All authors Paul X. McCarthy, Xian Gong, John A. Johnson, Marian-Andrei Rizoiu, Margaret L. Kern, Jean M. Twenge

Plasma Physics

Optimized finite-$\beta$ tokamak-stellarator hybrid configurations achieved by planar dipole-field coils

Yihui Liang, Hengqian Liu, Guodong Yu et al. (+3 more)

Tokamak–stellarator hybrids seek to combine tokamak-like compactness and confinement with stellarator-like externally generated rotational transform and steady-state operation. In this work, we build on the recent tokamak–stellarator hybrid study using planar dipole-field coils (PDCs) [Yu et al., arXiv:2605.03599], in which the fixed-position, programmable coils on an axisymmetric winding surface generate flexible three-dimensional shaping fields. Using single-stage free-boundary optimization of coil currents and plasma-equilibrium parameters, we construct vacuum and finite-$\beta$ configurations. The vacuum cases show controllable external transform and magnetic well. The finite-$\beta$ cases accommodate various density, temperature, and pressure profiles, producing quasi-axisymmetric (QA) equilibria with self-consistent bootstrap current, favorable Mercier stability, and reduced demand for external current drive. Re-optimization enables $\beta$ ramp-up and access to different field-period QA branches with moderate coil-current changes. At large rotational transform, a toroidally omnigenous (TO)-like configuration exhibits more favorable infinite-$n$ ideal-ballooning behavior than a QA reference with matched profiles, even though ballooning stability is not directly optimized for. These results demonstrate that PDCs provide a flexible platform for achieving optimized finite-$\beta$ hybrid configurations.
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All authors Yihui Liang, Hengqian Liu, Guodong Yu, Zhenyu Zhou, Caoxiang Zhu, Yao Zhou

Biological Physics

Robust topological oscillators govern a tunable phase transition to synchronized circadian rhythms

Chongbin Zheng, Peter Thomas and Evelyn Tang

While synchronization has been well-studied in deterministic oscillators, most underlying oscillators are stochastic in both natural and man-made systems. Yet, the effects of intrinsic stochasticity remain poorly understood. Here, we develop a new mechanism for synchronizing circadian KaiC molecules that have topologically protected cycles. We find a phase transition to synchronization that depends only on the single-oscillator coherence, across a range of molecular changes that determine this coherence. Examining both mesoscopic and macroscopic numbers relevant for cellular and in vitro conditions respectively, we find different scaling properties above and below the phase transition. Our results shed light on several existing experiments and further predict that external changes can be offset by compensatory changes that improve the single-oscillator coherence - demonstrating a tunable pathway between stochastic single oscillators and their robust collective rhythms.
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Optics

Filter shaping in integrated photonics using non-adiabatic control

Abdullah Al-Abiad and Pierre Colman

We demonstrate that an array of integrated coupled waveguides can be designed to simultaneously perform the spectral filtering and routing of light. The framework that we present here operates in a regime opposite to the shortcut-to-adiabaticity techniques, but we show it nevertheless also only requires smooth, slow, and small, modulations of the array's parameters. Our design strategy rely on the analogy which exists between the temporal evolution of an Hamiltonian and the propagation of light in an array of coupled integrated waveguides, mimicking spatially in photonics phenomena analogue to the electronic transitions. Interestingly, the chromatic dispersion restores in photonics the correspondence between spatial propagation effect and wavelength dependence. Therefore our framework is able to fully reproduces temporal effects such as resonant transitions, making it suitable for filtering applications. As a design involving modulations, it shares similarities with Bragg-Gratings (BGs): but it adds some extra features that make it complementary in terms of applications and functionality. First it adds routing capability with the seamless management of multi-waveguide configuration. And secondly the framework we present requires only wavelength-scale modulations, order of magnitudes slower than for BGs. The effects we present here may not interfere with BGs geometry and functionality, and could even be combined with the latter.
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Biological Physics · Cross-listed from cond-mat.soft

Fundamental Relation between Conductance of Biomolecules and the Fukui Function

Gabor Vattay

The finite-temperature conductance of a molecule coupled to metallic leads is derived entirely within the framework of density functional theory (DFT) and its time-dependent extension for open quantum systems. Starting from the Mermin grand potential, the foundational Kohn-Sham equations, the Fukui function, and the open-system master equation for the single-particle density matrix are systematically formulated. The non-equilibrium electron-phonon dissipator is obtained from the partial trace over the phonon bath. By applying Wick's theorem for non-interacting fermions, a fully exchange-symmetric collision integral is obtained that strictly preserves Pauli exclusion at the operator level. Performing a double perturbation expansion, initially in the applied voltage (linear response), and subsequently in the molecule-lead coupling (weak coupling), it is demonstrated that under the fast-thermalization condition, the complex exchange-correlation self-consistent field response is analytically projected out by the diagonal structure of the slow Liouvillian mode. Consequently, the thermal conductance is governed by the finite-temperature Fukui function, the central reactivity descriptor of conceptual density functional theory. This condition is satisfied in proteins, whose wave functions are extended and multifractal due to quantum criticality at the Anderson metal-insulator transition. This derivation establishes a fundamental link between electronic transport and chemical reactivity, identifying conducting paths with reactive sites. It opens new technological avenues connecting drug design to conductance experiments and also provides a foundation for designing next-generation bioelectronic sensing and computing architectures.
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Plasma Physics

Structural Requirements for Ion-Acoustic Double Layers: A Parametric Perturbation Analysis of the Maxwellian Limit

Hamdi M. Abdelhamid, Abeer A. Mahmoud and Naoki Sato

Standard Maxwellian plasmas exhibit a mathematical rigidity, possessing insufficient degrees of freedom to support electrostatic double layers (DLs) and yielding only soliton solutions. This study investigates the hypothesis that the formation of DLs is a generic consequence of breaking this structural rigidity through parametric perturbation. By introducing two independent continuous control parameters, $\delta_1$ and $\delta_2$, into the electron distribution, we demonstrate that DLs are a structural property of any plasma model that relaxes the strict Maxwellian constraint. Through a Gardner small-amplitude expansion, we analytically prove that a perturbation must modify both the quadratic and cubic density coefficients to decouple the nonlinear structure and generate physical, supersonic double layers, deriving small-amplitude acoustic-limit threshold conditions of $\delta_1 > 1$ and $\delta_2 > 7/3$. We show that these theoretical boundaries broaden for large-amplitude, nonlinear structures. By mapping the exact existence regions of DLs in phase space, we demonstrate how higher-order terms relax the weak-amplitude limits, confirming that the Maxwellian state represents a singular point where the DL solution collapses.
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Optics

Unveiling Structured Optical Coherence in Nonlinear Optics

Zihao Pang and Ady Arie

Nonlinear optical interactions are typically driven by coherent sources. Recently, however, incoherent sources have also been shown to offer new opportunities in nonlinear optics. Here we study the effects of the source incoherence in second harmonic generation. We derive and experimentally validate an analytic expression for the far-field coherence function at the second harmonic frequency, which is a generalization of the well-known van Cittert-Zernike theorem. Unlike the linear optics case, here the results depend not only on the source spatial distribution, but also on the nonlinear structure of the crystal. We further show, by performing Young's double-slit measurement, that the coherence at the second harmonic increases as the light propagates inside the nonlinear crystal. Our results establish nonlinearity as a degree of freedom for optical coherence, opening a new research area at the interface of statistical optics and nonlinear optics.
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Optics

Engineering Nanophotonic Modes via the Radiation Continuum

Francesco Pisani, Baptiste Fix, Thomas Krieguer et al. (+5 more)

We demonstrate, experimentally and theoretically, a universal mechanism for combining nanophotonic modes relying on radiative-loss-mediated couplings. For the case of two modes this mechanism leads to a BIC-type phenomenon characterized by the emergence of a high-quality factor subradiant mode. This mode is experimentally observed in the mid-infrared range, in arrays of double-metal patch antennas where the radiation loss rates are controlled by the geometry of the system. As the mechanism described here is independent of the specific nature or number of the interacting modes, it can be used to combine physically different resonating structures, without requiring fine symmetry tuning or specific modal configurations, opening new opportunities for resonance-based nanophotonic devices.
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All authors Francesco Pisani, Baptiste Fix, Thomas Krieguer, Alexis Jouan, Cheryl Feuillet-Palma, Isabelle Sagnes, Patrick Bouchon, Yanko Todorov

Optics

Exactly Solvable Diffraction-Grating Scattering Problems for TE and TM waves

Farhang Loran and Ali Mostafazadeh

In J.~Phys.~A 31, 3493 (1998), Berry provides an analytic solution of the problem of determining the diffracted beam intensities for atoms incident upon a grating given by the potential, $v(x,y):=iV_0(e^{i\mathfrak{K}\,y}-1)$ for $0\leq x\leq\ell$ and $v(x,y):=0$ otherwise, where $V_0, \mathfrak{K}$, and $\ell$ are positive real parameters. This result, which relies on the paraxial approximation, readily applies to the diffraction of transverse electric (TE) waves by the nonmagnetic optical grating given by the relative permittivity, $\hat\varepsilon(x,y):=1-v(x,y)/k^2$, where $k$ is the incident wavenumber. We show that Berry's grating belongs to a larger class of possibly magnetic diffraction gratings whose scattering problem for both TE and transverse magnetic (TM) waves are exactly solvable. Our analysis makes use of a recently developed dynamical formulation of stationary scattering that is based on the idea of mapping the scattering problem to the quantum dynamics generated by an effective non-Hermitian Hamiltonian operator. {We use this formulation to derive explicit analytic expressions for the diffracted beam amplitudes that are valid beyond paraxial approximation.} As a concrete example, we offer the exact solution of the scattering problem for TE and TM waves incident upon a generalization of Berry's grating.
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Optics

Transient Depth Thermography for Probing Heat Transport

Dmitrii Shymkiv, Chun Wang, Yan Jiang et al. (+3 more)

Directly probing heat propagation inside materials remains challenging because conventional measurements are predominantly sensitive to surface temperature. Depth thermography has enabled non-contact reconstruction of subsurface temperature profiles from spectrally resolved thermal radiation under steady-state conditions. Here, we extend this approach into the time domain, establishing transient depth thermography to resolve the evolution of internal temperature during heat transport. By exploiting wavelength-dependent optical penetration depth, time-resolved thermal-radiation spectra provide access to temperature as a function of both depth and time. Tracking this spatiotemporal temperature field enables direct probing of heat propagation and quantitative determination of out-of-plane thermal conductivity and interfacial thermal resistance. We demonstrate the approach in fused silica, obtaining thermal conductivity within 2% of established values, and measure the temperature-dependent thermal conductivity of MgF2 over a broad temperature range where existing data are sparse and inconsistent. Numerical simulations further demonstrate its extension to multilayer thin films for probing interfacial thermal resistance. By extending depth-resolved thermal spectroscopy from steady-state to transient heat transport, this work establishes a new optical route for non-contact characterization of thermal dynamics in bulk and layered materials.
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All authors Dmitrii Shymkiv, Chun Wang, Yan Jiang, Rajat Srivastava, Nandika D'Souza, Yuzhe Xiao

Chemical Physics

Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Nore Stolte, Harald Forbert, Yury Lysogorskiy et al. (+2 more)

Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots \pi$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of these pivotal interactions. We introduce a data-efficient upfitting strategy to train a machine learning interatomic potential (MLIP) based on the graph atomic cluster expansion for aqueous aromatic molecules with CCSD(T) accuracy for condensed phase simulations, using only finite molecular clusters. We apply our method to aqueous toluene (C$_6$H$_5$CH$_3$). The resulting CCSD(T)-quality MLIP reproduces coupled cluster energies and forces in bulk and reveals that commonly employed methods do not capture the crucial balance between hydrophilic and hydrophobic solvation, distorting the interactions of aromatic molecules with their environment. Representative biomolecular force fields substantially understructure the hydrophobic solvation shell and misorient interfacial water, while overestimating $\pi$-contacts, yielding an inconsistent solvation balance. Even hybrid DFT and MP2 overestimate barriers to breaking of water-$\pi$ hydrogen bonds. Our workflow provides a practical, general route to CCSD(T)-quality condensed-phase simulations of aqueous solutions, and thus constructed interaction potentials now open the door to consistent, highly accurate benchmark studies of $\pi$-contacts and hydrophobic effects in biomolecular contexts such as solvation of proteins and DNA in aqueous environments.
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All authors Nore Stolte, Harald Forbert, Yury Lysogorskiy, Ralf Drautz, Dominik Marx

Physics and Society

Conflict, mobility and fragmentation in the African interurban network

Andrew Renninger, Valentina Marin, Carmen Cabrera et al. (+1 more)

Roads make regional integration possible, but they also concentrate risk. When violence reaches a corridor that carries regional movement, the consequences can extend beyond the attacked place. We study this problem in Africa by combining interurban road data, georeferenced conflict events, and bilateral migration flows. Using a Hawkes-style self-exciting memory kernel, we estimate accumulated conflict intensity near roads from 2019 to 2022, and we route modelled movement between cities along the road network. As an empirical check on these modelled corridor flows, we show that country pairs connected by routes with higher accumulated conflict have lower bilateral migration. The association remains negative across alternative assumptions about spatial decay and is strongest for paths that cross regions, consistent with a corridor-deterrence channel rather than only displacement from violent places. We then use cascade and percolation simulations to examine how local disruptions propagate through the road network. Most simulated disruptions isolate relatively small populations, but 18.6% isolate more than one million people, and the largest isolates 13.3 million. Removing roads in order of conflict intensity reveals a tipping point, at which the giant connected component falls from 85% to 39% of the network. Our results identify the roads where violence most threatens regional integration and show how past conflict, mobility, and network structure combine to create persistent corridor vulnerability.
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All authors Andrew Renninger, Valentina Marin, Carmen Cabrera, Rafael Prieto-Curiel

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Multiwavelength Raman investigation of mono- and few-layer MoS2 grown by Pulsed Laser Deposition on SiO2

Alice Cartoceti, Paolo D'Agosta, Francesco Tumino et al. (+3 more)

Molybdenum disulfide (MoS$_2$) is a semiconductor whose vibrational and excitonic properties are highly sensitive to layer number and structural disorder. We demonstrate the growth of MoS$_2$ monolayers on inert, electronics-compatible SiO$_2$ substrates using room-temperature pulsed laser deposition (PLD). Control of the process parameters enables tuning from monolayer to multilayer films, which we investigate by multiwavelength Raman spectroscopy. The evolution of the Raman-shift difference between the $E_{2g}^{1}$ and $A_{1g}$ modes, combined with an assessment of defect density, tracks film growth as a function of the number of deposition laser pulses. Although excitonic effects strongly influence the optical response of two-dimensional transition-metal dichalcogenides, experimental reports of symmetry-selective exciton-phonon coupling remain limited. We provide experimental evidence of symmetry-dependent exciton-phonon coupling in PLD-grown monolayer MoS$_2$. Specifically, we observe modulation of the resonant behaviour of the out-of-plane $A_{1g}$ and in-plane $E_{2g}^{1}$ modes, related to their different coupling to A excitons, predominantly derived from Mo $d_{z^2}$ orbitals, and C excitons, characterized by mixed orbital contributions from Mo $d_{z^2}$ and S $p_x$ and $p_y$ states. Comparison with mechanically exfoliated monolayers reveals the role of growth-induced defects in modulating these interactions. These findings establish room-temperature PLD as a viable approach for growing two-dimensional MoS$_2$ on inert, electronics-compatible substrates and provide insight into the interplay between excitonic resonances and growth-induced disorder in two-dimensional MoS$_2$.
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All authors Alice Cartoceti, Paolo D'Agosta, Francesco Tumino, Valeria Russo, Carlo S. Casari, Andrea Li Bassi

Geophysics

Estimation of Elastic Parameters with Guidance-based Diffusion model

Anjali Dixit, Francesco Brandolin and Tariq Alkhalifah

Elastic parameters are fundamental rock properties for reservoir characterization, but their reliable estimation from angle-stack seismic data remains challenging due to strong nonlinearity and imperfect physical modeling. Conventional deterministic approaches based on linearized Zoeppritz approximations yield a single point estimate and cannot quantify solution uncertainty, while probabilistic methods are computationally expensive. To address these limitations, we present a workflow for elastic parameter inversion from angle-stack seismic data using a guided diffusion model as an implicit prior over the joint distribution of P-wave velocity, S-wave velocity, and density. The diffusion model is trained in an unsupervised manner on benchmark datasets and well-log-derived synthetic models, learning the non-Gaussian statistical coupling among the three elastic parameters. For guidance, we employ Diffusion Posterior Sampling (DPS), which approximates the likelihood function through a forward operator based on the Aki-Richards approximation and injects data-consistency gradient corrections at each reverse diffusion step, sampling from a posterior conditioned on the misfit between observed and modeled angle-stack data. We evaluate the framework on two datasets: the 2D Otway synthetic elastic model and field data from the Poseidon field, NW Shelf, Browse Basin, Australia, comparing it against two baselines: LSQR least-squares inversion and ADMM-based inversion with total variation regularization. Quantitative comparisons confirm that the diffusion-based framework recovers sharper lithological contrasts and geologically more realistic elastic profiles. Uncertainty quantification is achieved by generating multiple independent posterior realizations through repeated reverse diffusion runs, producing spatially resolved uncertainty maps for each elastic parameter.
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Optics

Towards stealthy hyperuniform networks with optimal isotropic complete photonic band gaps using a novel inverse design procedure

Joel Steinegger, Paul J. Steinhardt, Christoph Räth et al. (+2 more)

We present a two-stage inverse design procedure for producing disordered stealthy hyperuniform trivalent photonic networks in two dimensions with isotropic complete photonic band gaps (PBGs) blocking light regardless of direction or polarization (TE or TM) over a wide frequency range. Most ordinary disordered systems fail to maintain complete PBGs as system size increases. The only known exceptions that remain open in the largest simulations have been generated by mapping stealthy hyperuniform point patterns into trivalent networks. However, the resulting networks are not truly stealthy hyperuniform two-phase media. Although their PBGs remain open, they are relatively narrow due to limited overlap between the TE and TM band gaps and broad band tails caused by localized defect states. By contrast, our two-stage inverse design aims to make the final network itself stealthy hyperuniform, achieving unprecedented near-optimal overlap between the TE and TM band gaps and a small defect state density at the band edges. We obtain not only single realizations with large PBGs, but a striking homogeneity across a large ensemble, effectively probing a network with 100,000 vertices. This ensemble-based band gap is comparable in width to the complete PBG of an anisotropic honeycomb photonic crystal with the same network parameters and nearly an order of magnitude wider than the previously widest known isotropic complete PBGs. Our designs can be fabricated using additive manufacturing, offering new pathways to manipulate electromagnetic waves for photonic technologies.
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All authors Joel Steinegger, Paul J. Steinhardt, Christoph Räth, Salvatore Torquato, Michael A. Klatt

Biological Physics

Reverse translocation of a nascent polypeptide through the ribosomal exit tunnels

Felipe C. Nepomuceno, Petr Chalupský and Michal Kolář

Before they mature, all known proteins translocate through the ribosomal exit tunnel in a form of extended or partially folded nascent polypeptide. This translocation occurs in the natural direction from the peptidyl transferase centre buried deep in the large ribosomal subunit through a tunnel to the ribosomal surface. Some proteins, however, enter the ribosomal tunnel from outside and translocate in the reverse direction. In this work, we address a simple question: to what extent is the ribosomal tunnel intrinsically directional. To compare the forward and reverse peptide translocations, we performed biased molecular dynamics simulations and assessed the directionality of tunnels from four different organisms using a model poly-alanine decapeptide. Our simulations reveal that the tunnel architecture intrinsically favours the natural direction translocation from peptidyl transferase centre to the ribosomal surface. Consequently, proteins that occupy the tunnel in the reverse direction likely require dedicated structural machinery to overcome this intrinsic bias.
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Fluid Dynamics

Generating synthetic evolution of turbulent flames with an experimental data-based spatiotemporal diffusion model

Amrit Tarur and Shivam Barwey

In this study, a conditional diffusion model – a class of generative machine learning models – is developed to generate synthetic, experimental data-based trajectories of turbulent flames. Generated experimental data corresponds to simultaneous field measurements, namely OH planar laser-induced fluorescence (OH-PLIF) fields and multi-component particle image velocimetry (PIV) fields, for attached and detached flame states in a swirl combustor configuration. This is done using an x-prediction flow matching framework combined with a pixel-based spatiotemporal transformer, which is capable of generating entire spatiotemporal slabs containing synthetic flame evolution at inference time, conditioned on the flame regime. Using this framework, synthetic flames were found to preserve key flame features and statistical consistency across space and time, particularly at the large scales – deviations at high temporal frequencies and small spatial length scales were found to depend on the time-span of the generated space-time slabs. An extrapolation task of transition synthesis is also conducted, in which the conditional diffusion model is used to synthesize spatiotemporally coherent flame transitions (flame liftoff and reattachment) unseen by the model during training. This was accomplished using a model for the denoising transition velocity that relies on time-varying linear combinations of attached and detached denoising velocities, leading to an approach that (a) allows for control of the generated transition directions and timescales, and (b) retains sample-to-sample variability in the generated transitions in the process. Overall, this study provides a promising pathway for the utilization of experimental data-based generative models as a new means of data exploration in data-sparse environments, complementing both experiments and computational fluid dynamics-based approaches.
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Physics Education

Reliable isomorphic physics problem generation with large language models

Xian Wu and Lindim Ismaili

This study presents an AI-powered system for generating isomorphic physics problems using large language model (LLM)-based agent workflows. The system is designed around three practical goals: preserving the same conceptual and problem-solving structure as the original problems, varying construct-irrelevant features such as scenarios and numerical values, and producing questions that are directly usable without expert revision. The workflow combines prompt chaining, agent-based verification, and automated LaTeX compilation within a publicly accessible website hosted on a Raspberry Pi. To evaluate the system, we developed an eight-item rubric and tested the system using 13 multiple-choice questions from a calculus-based introductory Newtonian mechanics course. The evaluation results showed that 89% of the questions generated were rated as fully specified and directly usable. However, the system also showed limitations. The results suggest that LLM-based systems have significant potential for reliable instructional problem generation while also highlighting important challenges for future development.
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Computational Physics

Towards end-to-end optimization in multimaterial 3D printing

Xue-Ling Luo, Steven Yang, Jingye Tan et al. (+3 more)

Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the adjoint state method implemented with FEniCSx, efficiently circumventing the bottlenecks of applying neural network constitutive models. This pipeline is deployed on soft robotic gripper applications, demonstrating continuous composition optimization for highly anisotropic contact responses, and the concurrent optimization of macroscopic topology and material distribution under non-failure stretch constraints. This methodology could replace laborious empirical prototyping, establishing interpretable machine-learning models as practical, robust design primitives for advanced multimaterial additive manufacturing.
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All authors Xue-Ling Luo, Steven Yang, Jingye Tan, Robert F. Shepherd, Noy Cohen, Nikolaos Bouklas

Physics and Society

Human population dynamics as a Bayesian inverse transport problem

Chong Qi

Many open problems across physical, biological, and engineered systems involve non-equilibrium transport processes where the governing conservation laws are known, but the underlying constitutive relations remain latent and time-varying. Conventional data-driven approaches like deep neural networks capture statistical patterns but routinely violate fundamental mass conservation. Here, we introduce a unified Bayesian inverse transport framework that resolves this by embedding Bayesian Neural Networks (BNNs) directly within exact partial differential equations in age-time space. By evaluating this framework on complex, real-world human cohort advection across China, Japan, and South Korea, we demonstrate that this physical constraint enables consistent uncertainty propagation and missing-data reconstruction from sparse observations. Beyond demography, this framework provides a generalizable foundation for observing and forecasting non-equilibrium boundary dynamics across various fields.
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Optics

Optically Incoherent Photonic Mutual Information

Francis J. Chen, Alessio Amaolo, Pengning Chao et al. (+3 more)

While traditional evaluations of optical information transfer rely on disjointed abstractions to bridge electromagnetic propagation, coherence, and communication theory, we introduce an end-to-end framework that directly connects rigorous subwavelength wave physics to Shannon mutual information. By lifting the Maxwell current-to-field Green's function to propagate second-order field correlations (the mutual intensity), we establish a unified linear channel model that encapsulates coherent communication, phase retrieval, and incoherent imaging. Applying this framework, we demonstrate that the mutual-information-optimized photonic front end is dictated jointly by available spatial degrees of freedom, source statistics, and detection laws. For coherent sources measured by square-law detectors, we identify a structural transition: when detectors outnumber sources, topology-optimized front ends shift from point-focusing to interferometric mixing. This mixing leverages interference cross terms to make relative source phases information-bearing, yielding mutual information that surpasses the point-focusing amplitude-only baseline. Conversely, for spatially incoherent sources, the channel reduces to the Hadamard square of the Green's function. In this regime, under an isotropic source covariance, we prove that point-focusing uniquely maximizes the mutual information at fixed Frobenius norm. Under source correlations, the optimized front ends instead favor optical mixing. Finally, we derive closed-form upper bounds on achievable incoherent mutual information, governed entirely by the coherent singular values of the underlying electromagnetic operator. Potential applications include near-field microscopy, direct-detection optical datalinks, reference-free phase retrieval, fluorescence and thermal imaging, and structure-agnostic benchmarks for end-to-end-designed computational imagers.
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All authors Francis J. Chen, Alessio Amaolo, Pengning Chao, Sean Molesky, Zin Lin, Alejandro W. Rodriguez

Atomic Physics

A robust and modular cesium magneto-optical trap using divergent laser beams

Paola Luna, Kenneth Nakasone, Andrei Zhukov et al. (+4 more)

Magneto-optical traps (MOTs) are a workhorse technology for neutral-atom quantum sensing, simulation, and computing. Here, we demonstrate a MOT optimized for inertial quantum sensing and precision metrology applications. Our MOT traps $4\times10^8$ cesium (Cs) atoms in a robust, modular system that achieves stable operation through two design features: (1) we use divergent cooling laser beams to reduce unwanted reflections and (2) optical elements are mounted in a fiber-coupled, compact, and modular cage rigidly attached to the vacuum chamber. Following polarization-gradient cooling (PGC), the system produces atom samples with temperatures below 10 ${\mu}$K, similar to those achieved in conventional MOTs that use collimated laser beams. In addition, we observe trapping of $2\times10^7$ Cs atoms in a non-conventional MOT geometry, where the cooling laser beams are diagonal to the principal axis of the quadrupole magnetic field.
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All authors Paola Luna, Kenneth Nakasone, Andrei Zhukov, Philip Wondrak, Brian P. Anderson, Benjamin F. Kanzer, Cristian D. Panda