Optics
Material selection for mid-infrared thin-film coatings and windows
Jin-Woo Cho, Tanuj Kumar, Hongyan Mei et al. (+1 more)
We summarized the room-temperature optical properties for infrared-transparent materials, defining transparency windows for two different applications: thin-film coatings (absorption coefficient $\alpha < 10 cm^{-1}$) and windows ($\alpha < 1 cm^{-1}$). The transparency requirements for thin films are substantially less stringent, enabling the use of many more optical materials for a given wavelength range. To make an easy-to-use reference, we categorized materials by chemical group, crystallinity, and typical deposition technique, and discussed practical pros and cons such as chemical and thermal stability, mechanical hardness, and cost. The
datasets and plotting scripts are provided so that users can regenerate transparency-window maps for different applications. If you do optical design at infrared wavelengths, we recommend that you print out the figures and stick them on your wall.
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All authors Jin-Woo Cho, Tanuj Kumar, Hongyan Mei, Mikhail A. Kats
Biological Physics
Effective roles of the head and flagellum in sperm motility
Robert L. Scott, Shobitha Unnikrishnan, Ayomide Bolaji et al. (+4 more)
In the low Reynolds number fluid environment that microswimmers encounter, back-and-forth motion cannot lead to net displacement. In mammalian sperm, the mechanical wave propagating along their single flagellum breaks the cancellation between back-and-forth motion and, therefore, is assumed to define the movement direction. Here, we show experimentally that the sperm head movement direction is not opposite to the wave propagation direction when sperm move in a viscoelastic fluid next to a solid substrate. The oscillation of the movement direction is out of phase with the oscillation of the wave direction, and the movement has a larger amplitude than the wave direction. When we tried to reconstruct the movement direction as a linear combination of the head orientation and the wave direction, we found that the contributions from these two changed in time. Further, the last bend of the flagellum does not move in the lab frame (as observed under the microscope) on a deformable polydimethylsiloxane (PDMS) substrate, which likely results from the flagellum indenting the surface, providing a new mechanism for locomotion. Overall, our results highlight the appearance of the sperm head in steering motility and provide quantitative tests on sperm chemotaxis and flagellar bending mechanism.
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All authors Robert L. Scott, Shobitha Unnikrishnan, Ayomide Bolaji, Luke Achinger, Tomer Avidor-Reiss, Kenta Ishimoto, Chih-kuan Tung
Optics
Multimodal Optical Feature Extraction with a Free-Space Photonic Extreme Learning Machine
Anushka Kumari, Anushree Khisti, Abhinav Choube et al. (+3 more)
Photonic extreme learning machines (PELMs) replace a digitally trained hidden layer by a fixed optical transformation, allowing a high dimensional feature map to be generated by physical propagation while only the final readout is learned. Existing free-space PELM demonstrations have established this principle for image and tabular
benchmarks, but a unified
multimodal optical feature extractor spanning structurally different data types has remained largely undeveloped. Here we demonstrate a single free-space PELM platform for image, audio derived, binary tabular, and regression tasks using phase only SLM encoding,
Fourier like free space propagation, and camera intensity detection. The same optical apparatus achieves 96.56% accuracy on MNIST, 95.67% on spoken digit audio from log-Mel spectrograms, 100.00% on Mushroom classification, and 0.0699 NRMSE on Abalone regression. To our knowledge, this is the first free space PELM spanning image, audio derived, and tabular tasks in one physical pipeline, and the first PELM implementation of spectrogram based spoken digit classification. Empirical distance preservation and kernel
alignment diagnostics reveal two operating regimes: geometry preserving for image and regression tasks, and distributed class mean accumulation for audio derived spectrograms. These results establish
multimodal PELMs as a practical route toward general purpose optical machine learning.
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All authors Anushka Kumari, Anushree Khisti, Abhinav Choube, Devansh Satra, Srivatsa Murali, Anshuman Kumar
Optics · Cross-listed from cond-mat.soft
Self-Assembly of Lipid-Biopolymer Periodic Nanostructures on Photonic Length Scales
Rushna Quddus, Meron Debas, Stefan Salentinig et al. (+2 more)
The self-assembly of photonic nanostructures in insects involves chitin,
proteins, and lipids. While synthetic photonic systems have been extensively studied, current lipid-based self-assembly systems are limited in periodicity to $68\,\text{nm}$ compared to photonic length scales ($\approx 450\,\text{nm}$) observed in biological organisms. We hypothesise that lipids facilitate how structural colour arises in vivo by acting as templates for the self-assembly of biopolymers via lipidic lyotropic
liquid crystal mesophases. Here, we aim to understand and identify how structural colour is produced in insects by the co-assembly of lipids and biopolymers. We study the effect of biopolymers, pH, temperature, surface charge, and stability on lipid vesicles using dynamic light scattering, X-ray scattering, and
zeta potential analysis. Using cryo-electron microscopy, we demonstrate that these vesicles interact with the biopolymers and generate periodic nanostructures with periodicities ranging from $700\,\text{nm}$ to $1.2\,\mu\text{m}$ (more than ten times larger than for purely lipidic systems) and dimensionalities ranging from 1D to 3D. Our results establish that lipid mesophases and biopolymers can induce reorganisation into ordered nanostructures, overcoming key limitations of periodicities achieved by lipid-only systems, and providing a methodology for recreating the physicochemical mechanisms underlying biophotonic structural colour.
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All authors Rushna Quddus, Meron Debas, Stefan Salentinig, Ullrich Steiner, Viola Vogler-Neuling
Atomic Physics
Universal response of Rydberg manifolds to standing light waves from the microwave to the X-ray regime
Homar Rivera-Rodríguez, Matthew T. Eiles and Jan M. Rost
Standing light waves structure the electronic density of a Rydberg atom in a rich but surprisingly systematic fashion. We uncover these systematics, which are nearly universal across a large range of principal
quantum numbers n, by varying the wavelength of the standing light over several orders of magnitude. Thereby, we identify five qualitatively different regimes and give their transition criteria in terms of specific critical wavelengths. The bandwidth of the
lattice spectrum, manifested in the difference of energies between the states on the edges of the degeneracy-lifted n-
manifolds, as well as the organization of the electron density in coordinate and momentum space are used to rationalize the systematics. A experimental setup is proposed to measure the features in the different regimes.
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Applied Physics · Cross-listed from cond-mat.soft
Interaction mechanics of acoustic cavitation with fibrin networks
Aarushi Bhargava, Gaurav Gardi and Metin Sitti
Stiff and dense fibrin networks in chronic blood clots impede drug penetration, limiting the efficacy of thrombolytic therapies. Acoustic cavitation of microbubbles is a promising strategy to enhance drug delivery in soft tissues. However, the interaction of these bubbles with stiff fibrin networks has yet to be investigated. Here, we show that ultrasound-driven bubbles undergoing periodic oscillations can penetrate and alter dense fibrin networks. The penetrated bubbles create three-dimensional paths that enable nanobeads (
matrix transport markers) to infiltrate up to 200 \textmu m deep into the mesh. Radial bubble oscillation is found to be the dominant forcing mechanism on fibrin fibers. Combining mechanical measurements with these observations reveals that the radial stress from a single bubble oscillation is far below the fracture strength of fibrin fibers. Instead, repeated sub-fracture loading from thousands of oscillations progressively accumulates damage and softens the network until it yields - the plausible mechanism for bubble penetration through the fiber mesh. We further explored this fibrin softening at a range of peak applied forces. At low force, the fibrin network initially softens, but is resistant to further damage after hundreds of cycles. At higher forces, networks continue to soften without reaching a stable state, indicating the progressive accumulation of damage. These results show that cavitation can enhance
matrix transport in dense fiber mesh by softening and structurally altering fibrin networks. The underlying physics is governed by the viscoplastic mechanics of bubble-fibrin interactions. These findings establish a mechanistic framework to design comprehensive treatment strategies for fibrotic aged clots.
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Optics
Collective Radiative Enhancement of Rare-Earth Ions in Lithium Niobate via Engineered LargeArea Nanohole Arrays
Ali Najjar Amiri, Trevor Kling, David Barton et al. (+1 more)
Conventional approaches to light-matter interactions rely on engineering photonic density of states. More recently, tailoring the spatial geometry of atoms or emitters themselves has emerged as a powerful and complementary route to control collective radiative properties. Here we experimentally realize a geometry-engineered ensemble of rare-earth ions by fabricating a periodic array of subwavelength gold nanoholes on lithium niobate implanted with thulium ions, forming a semi-two-dimensional array of
quantum emitters embedded in a high-index crystalline thin film. The hybrid structure can simultaneously support localized and
lattice plasmon resonances from the metallic array and collective atomic resonances from the ion ensemble. Using time-resolved photoluminescence and temperature-dependent measurements, we observe enhanced radiative emission attributed to collective atomic effects mediated by the nanohole
lattice, distinct from single-emitter Purcell enhancement. Our results demonstrate a new regime of light-matter interaction opening a pathway toward broadband and scalable, geometry-controlled
quantum optical interfaces in solid-state platforms.
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All authors Ali Najjar Amiri, Trevor Kling, David Barton, Mahdi Hosseini
Optics · Cross-listed from quant-ph
Contrast enhanced imaging through weakly scattering media with spatially entangled photons
Alessio D'Errico, James Hubble, Rojan Abolhassani et al. (+4 more)
Improving the image contrast of objects immersed in weakly scattering media can be achieved using various strategies. One common approach is to reject events associated with scattered
photons in favor of the detection of ballistic
photons. While this is traditionally done via time gating or spatial filtering, we propose a different approach based on probing the object with spatio-temporally entangled
photon pairs. We show that coincidence detection, followed by post-selection on spatially correlated events, allows us to isolate ballistic from scattered bi-
photons, thereby enhancing image contrast relative to a single-
photon detection strategy, and simultaneously removes events due to background light. Our predictions are obtained via numerical simulations and confirmed by experiments conducted in two configurations where either both
photons or only one illuminates the scene. In both scenarios, correlation post-selection shows an improvement in image contrast at the expense of higher shot noise due to the lower number of events. The latter can be partially compensated for by appropriately combining events from several post-selection windows. Our findings will enable extending imaging through scattering media into the
quantum imaging framework in settings where adaptive optics, time gating, and spatial filtering are impractical.
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All authors Alessio D'Errico, James Hubble, Rojan Abolhassani, Nazanin Dehghan, Yishai Klein, Yingwen Zhang, Ebrahim Karimi
Atomic Physics · Cross-listed from nlin.CD
Nonlinear Dynamics of Rapidly Driven Systems
Afshin Besharat and Alexander A. Penin
We consider systems characterized by the presence of a rapidly oscillating force. A general method is presented for the construction of the effective action governing the large-scale nonlinear dynamics of such systems order by order in inverse powers of the oscillation frequency $\omega$. The explicit expression for the effective Lagrangian is derived up to ${\cal O}(1/\omega^6)$ next-to-next-to-leading approximation in the strong-drive regime. The general structure of the high-frequency expansion reveals a broad class of nonlinear systems whose transition curves are identical to those of the linear Mathieu equation, which enables a fully nonperturbative stability analysis in the case of strong driving and nonlinearity. The method is generalized to velocity-dependent forces and configuration space with
curvature, characteristic to systems with constraints. Several applications are discussed in detail, including the dynamical
magnetic trapping of electric charges.
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Computational Physics · Cross-listed from cs.LG
The Hamilton-Jacobi Theory of Deep Learning
Jose Marie Antonio Miñoza, Erika Fille T. Legara and Christopher P. Monterola
In this paper, training a neural network is identified, exactly, as a search through Hamilton–Jacobi initial-value problems: each gradient step selects the initial data of a viscous Hamilton–Jacobi equation whose
Hopf–Cole propagator best fits the observations; at inference, the input is the spatial point at which that solution is evaluated and the initial condition is already encoded in the weights. The correspondence is exact for log-sum-exp layers, with ReLU, sigmoid, SiLU, and GELU each an exact limit, gradient, or moment of the same object, and exact in composition across depth and width, with a quantified error at finite depth that vanishes in the joint limit. It is structural for residual networks,
transformers, and recurrent networks (RNNs, LSTMs, SSMs), each discretizing the same class of equations, at a named and quantified approximation error. A single deformation parameter $\varepsilon$ unifies all four perspectives (network, tropical
algebra, viscous PDE, convex optimization) in a commutative diagram closed under Lipschitz conditions. Quantitative consequences include: the minimax optimal generalization rate $O(n^{-1/(d+2)})$ for fixed $t$;
adversarial robustness controlled by $\varepsilon$; backpropagation as the co-state equation of the
Hamiltonian system for residual networks (Pontryagin Maximum Principle); scaling exponents consistent with data intrinsic dimension via PDE quadrature; and a closed-form $O(N)$ influence function (softmax attribution weights $\pi_j$) whose
entropy landscape undergoes fold bifurcations as $\varepsilon$ increases, each merging attribution basins.
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Optics
Micro-Transfer Printing of Lithium Niobate on 200 mm Silicon Photonics: A High-Speed Heterogeneous Wafer-Scale Platform
Xiujun Zheng, Suzanne Bisschop, Arno Moerman et al. (+21 more)
The rapid growth of artificial intelligence (AI) and other data center applications is driving the demand for photonic interconnects that combine high-speed with low energy consumption, making scalability a critical requirement. Micro-transfer printing (MTP) has emerged as a promising technique for the wafer-scale heterogeneous integration of thin film lithium niobate (TFLN) onto silicon photonics (SiPho) platforms. Here, we demonstrate heterogeneous SiPho TFLN integration across four full 200 mm wafers with a 3sigma placement accuracy down to 420 nm and a printing yield of larger than 95percentage. Low insertion loss less than 2 dB over 600 phase modulators (300 amplitude modulators) is achieved. A half wave voltage of 4 V in push pull configuration, and high-speed modulation with a bandwith larger than 70 GHz is demonstrated on a subset of tested devices.
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All authors Xiujun Zheng, Suzanne Bisschop, Arno Moerman, Margot Niels, Ewoud Vissers, Athina Papadopoulou, Philip Ekkels, Patrick Nenezic, Simone Atzeni, Elif Ozceri, Tiernan McCaughery, Ali Uzun, Ye Chen, Laurens Bogaert, Nishant Singh, Sandeep Seema Saseendran, Sofie Janssen, Natarajan Rajasekaran, Sadhishkumar Balakrishnan, Philippe Absil, Gunther Roelkens, Bart Kuyken, Sarah Uvin, Maximilien Billet
Chemical Physics
How Atoms Interact Within Molecules
Adil Kabylda, Malte Esders, Matteo Gori et al. (+3 more)
Fundamental understanding of interatomic forces in molecules must emerge from
quantum mechanics, yet widely used empirical force fields rely on simplified mechanistic approximations that often fail to capture the complexity of many-body systems. Here we employ recent developments in
quantum field theory (QFT) for long-range electron correlation and machine learning force fields (MLFFs) to directly compute the depth and scatter of interatomic forces for molecular systems containing hundreds of atoms. We find that while the average interaction strength decays polynomially with interatomic separation, the interaction scatter remains robust and exhibits substantial anisotropy. Both QFT and MLFFs demonstrate that increasing the molecular size further amplifies this scatter and anisotropy – a phenomenon not considered in traditional textbook empirical models. These results provide new
benchmarks for force models, shift the focus from interacting atoms to interacting “hotspots” that might determine the folding pathways of (bio)
polymers, and rationalize why MLFFs are uniquely successful in capturing the nuances of complex molecular systems. Our findings offer a roadmap for the development of more accurate and
quantum-aware molecular force fields.
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All authors Adil Kabylda, Malte Esders, Matteo Gori, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko
Data Analysis and Statistics · Cross-listed from hep-ph
Neural Scaling Laws for Jet Generation
Oz Amram, Darius A. Faroughy, Tjarko Gerdes et al. (+5 more)
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities –
dataset size, compute, and model parameters – are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation – both relevant as a pre-training objective for foundation models and as in-situ simulation by itself. We indeed replicate the key logarithmic scaling law behavior for model-size scaling. Beyond studying the next
token prediction validation loss of the generative model, we also study the sliced Wasserstein distance of five physical quantities that are not immediately available to the model during training. Our study shows that this quantity is monotonically related to the next
token prediction validation loss, meaning that this loss is indeed a good proxy for the physics performance. For the scaling with
dataset size and compute, we observe substantially weaker scaling behavior of both the loss and the sliced Wasserstein distance. We analyze this behavior by introducing the concept of a learnable window, and argue that autoregressive next
token prediction on jet constituents exhibits comparatively rapid saturation relative to
language-model studies. We discuss possible origins of this behavior, including the
stochastic nature of QCD radiation and differences between generative and supervised learning tasks in
collider physics.
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All authors Oz Amram, Darius A. Faroughy, Tjarko Gerdes, Anna Hallin, Gregor Kasieczka, Michael Krämer, Humberto Reyes-Gonzalez, David Shih
Fluid Dynamics
On the limiting geometry of unsteady breaking waves subject to co-flowing wind: spectrally-informed versus locally-measured steepness
Rui Cao, Enrique M. Padilla, Xu Chen et al. (+1 more)
Wave steepness is a key
geometric variable for describing breaking occurrence and its consequences, including energy dissipation and air entrainment. Using three laboratory campaigns under varying spectral conditions and co-flowing wind forcing, we contrast two types of steepness commonly used for unsteady breaking waves: spectrally-informed wave-group steepness (prognostic), obtained from fixed-point surface-elevation records, and locally-measured crest steepness (diagnostic), obtained from spatial surface profiles extracted using the SDBW-I image-processing method developed herein. For the former, the long-adopted $\mathcal{S}_n$ (linear sum of
Fourier-component steepness) increases appreciably within about two dominant wavelengths upstream of breaking because of its sensitivity to evolving high-frequency content. When measured sufficiently far upstream, however, wave-group steepness remains approximately linearly related to the local zero-crossing steepness $\mathcal{S}_b$ across bulk unforced conditions. Notwithstanding this, we argue that the crest-front steepness, $\mathcal{S}_{\mathrm{front}}(t_b)$, which delineates the front-face slope at incipient breaking, is the most physically meaningful metric examined here. It exhibits a consistent breaking-onset lower-bound threshold of $\mathcal{S}_{\mathrm{front}}(t_b)\approx0.2$, while values above this threshold decrease with wind speed as crests become less forward leaning. This may be attributed to wind-modified dispersion, enhanced high-frequency spectral content and aerodynamic sheltering, suggesting that wind–wave and wave–wave interactions act as competing mechanisms in triggering breaking through kinematic and energetic processes beyond what geometry alone can explain. Even so, $\mathcal{S}_{\mathrm{front}}(t_b)$ has strong potential as a controlling variable for future studies of breaking energetics and crest-scale dynamics.
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All authors Rui Cao, Enrique M. Padilla, Xu Chen, Adrian H. Callaghan
Chemical Physics
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
Laurence I. Midgley, Chen Lin, J. Harry Moore et al. (+8 more)
We present an evaluation of CSP-MACE-Å, a machine learning interatomic potential intended to replace DFT in
crystal structure prediction (CSP). We decompose the total energy into separate intramolecular and intermolecular components. For the intramolecular component, we adopt the MACE-POLAR architecture and train it on the OMol25
dataset. The intermolecular component combines three terms: an intermolecular contribution from the MACE-POLAR model, a long-range dispersion term with the functional form of the XDM correction, and a learned delta model trained to reproduce B86bPBE-XDM intermolecular energies. The learned delta model is trained on residual intermolecular targets derived from 50,000 B86bPBE-XDM calculations on molecular
crystal structures. On an evaluation set composed of 19 compounds, including a salt, selected from AstraZeneca's previous CSP publications, CSP-MACE-Å achieves performance comparable to PBE DFT with the Neumann-Perrin dispersion correction. On a second evaluation set composed of 28 compounds, including cocrystals and salts, collated from the seven CSP blind tests, CSP-MACE-Å achieves performance close to B86bPBE-XDM DFT. In both evaluation sets, reranking with harmonic free energies substantially improves performance relative to ranking by energy alone. Across our evaluation suite, CSP-MACE-Å is shown to outperform the MACE-POLAR-1 and UMA-OMC foundation models. Lastly, on a set of five compounds, CSP-MACE-Å is shown to capture temperature-dependent trends in the relative stability of polymorphs through estimation of the free energy under the harmonic approximation. By running multiple orders of magnitude faster than DFT, CSP-MACE-Å enables energy and free energy evaluation of far more candidate structures, providing greater confidence when derisking solid forms.
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All authors Laurence I. Midgley, Chen Lin, J. Harry Moore, Flaviano Della Pia, Javier Antorán, Sten O. Nilsson Lill, Emma S. E. Eriksson, Felix A. Faber, Lars Tornberg, Anders Broo, Gábor Csányi
Chemical Physics
How reproducible are first-principles simulations of liquid water?
Niamh ONeill, Benjamin X. Shi, William J. Baldwin et al. (+5 more)
Liquid water is fundamentally important, and its accurate computer simulation has been the driving force for myriad methodological developments. Ab initio molecular dynamics with forces obtained from density functional theory (DFT) is now a standard tool widely used by researchers. However, we reveal that previous studies of
liquid water using the same widely-used density functional (revPBE-D3) exhibit significant discrepancies with one another, varying by over 20% in the
diffusion coefficient and 10% in the density, raising fundamental questions about reproducibility. By combining modern long-range machine-learning interatomic potentials that enable robust statistical sampling with carefully converged DFT training data, we resolve these discrepancies, achieving consensus across six diverse community codes. Our predictions differ markedly from previous literature: we show that most previous results overestimate the density and underestimate the
diffusion coefficient of revPBE-D3 water due to basis set incompleteness and pseudopotential inconsistencies, coupled with limitations in statistical sampling (in some cases). These
benchmark values provide a reliable reference for validating current and future implementations of DFT-based ab initio molecular dynamics. Reaching agreement establishes confidence and credibility and serves as a prerequisite for the systematic assessment of new density functionals and numerical approximations.
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All authors Niamh ONeill, Benjamin X. Shi, William J. Baldwin, Albert P. Bartok, Chris J. Pickard, Angelos Michaelides, Gabor Csanyi, Timothy C. Berkelbach
Optics
A variability-aware simulation and design workflow for wafer-scale, heterogeneously integrated lithium niobate modulators
Patrick Nenezic, Ewoud Vissers, Arno Moerman et al. (+11 more)
We present a variability-aware simulation framework for heterogeneously integrated lithium niobate traveling-wave modulators. The framework incorporates fabrication-variation data obtained from our dedicated pilot line and enables efficient optimisation of
geometric parameters to ensure stable device performance across wafer-scale manufacturing. The proposed multi-parameter optimisation method enables the efficient identification of modulator designs that simultaneously achieve target performance metrics (Vpi, optical insertion loss, and 3dB electro-optic bandwidth) while maintaining robustness against fabrication-induced variations. Using this methodology on two representative modulator architectures, we theoretically demonstrate that reliable wafer-scale integration of lithium niobate modulators on silicon photonics via micro-transfer printing is feasible and can be systematically engineered.
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All authors Patrick Nenezic, Ewoud Vissers, Arno Moerman, Laurens Bogaert, Maximilien Billet, Xiujun Zheng, Tom Vanackere, Margot Niels, Athina Papadopoulou, Sarah Uvin, Peter De Heyn, Sandeep Saseendran, Simone Atzeni, Bart Kuyken
Chemical Physics
Formal O(N3)-Scaling Second-Order Perturbation Theory by Block Tensor Decomposition: Implementation on MP2 and rPT2
Yueyang Zhang, Wei Wu and Peifeng Su
Block tensor decomposition (BTD) and canonical polyadic decomposition (CPD) are combined into a unified $O(N^3)$-scaling framework for second-order perturbation theory (PT2), demonstrated on MP2 and renormalized PT2 (rPT2). BTD constructs the tensor hyper-contraction kernel at $O(N^3)$ via a dual-grid scheme; CPD factorizes the exchange channel through a block-based two-stage ALS. An asymmetric half-kernel design applies bare Coulomb to one vertex and coupling-constant-averaged screening to the other, capturing the SOSEX component of rPT2 without a frequency-dependent CPD. For MP2, BTD-CPD reproduces canonical RI-MP2 to 0.058~kcal/mol per heavy atom. For rPT2@PBE0 on the S66x8
benchmark, the mean absolute error is 0.36~kcal/mol (ME $-$0.19, RMSE 0.46) over 528 data points. The CPD-compressed intermediates yield $O(N^2)$ storage alongside $O(N^3)$ scaling.
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Biological Physics
Heatomics
F. Ritort
Living
cells are energy- and information-processing systems that sustain a nonequilibrium steady state (NESS) by continuously consuming energy and dissipating heat, as required by the second law of thermodynamics. The rate of heat dissipation is closely related to the
entropy production rate $\sigma$, a universal primal life signal and unique descriptor of the cellular state. Living matter dissipates $P_{\mathrm{life}} \sim 1$ Watt/kilogram (W/kg), a remarkably conserved value across scales, from molecular reactions to entire organisms. Surprisingly, this high power density is $10^4$ times larger than that of the Sun and comparable to the universe's average, $P_U = c^2 H_0 \sim 1$ W/kg, where $c$ is the speed of light and $H_0$ is Hubble's constant, a striking coincidence reminiscent of Dirac's large number hypothesis. We surmise that this large $P_{\mathrm{life}}$ sets the bar for generating negentropy, which contributes negatively to the overall positive $\sigma$. Negentropy sustains biological organization, distinguishing animate from inanimate matter. Here, I introduce heatomics, the science of studying $\sigma$ at the cellular and molecular scales, and the Variance Sum Rule, an experimental-theoretical framework that extracts $\sigma$ from fluctuations of a dynamical probe combined with the equation of state for a NESS. The emerging field of heatomics aims to elucidate the fundamental principles governing heat power generation, optimization of energy resources, and negentropy in living systems.
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Chemical Physics · Cross-listed from quant-ph
Poincaré Sphere Representation of Spin-Driven Polarization Encoding in Two-Dimensional Perovskite Light Sources
Zijian Gan, Shuyue Feng, Camryn J. Gloor et al. (+2 more)
Nonlinear optical light sources enable the generation of
photons with polarization states that are intrinsically determined by underlying material dynamics, rather than imposed through external modulation. Here, we investigate the fundamental
quantum communication performance achievable using four-wave-mixing signal fields emitted by a representative two-dimensional perovskite system. The experimentally reconstructed signal field is represented by Stokes-vector trajectories on the Poincaré sphere to establish a practical framework for visualizing
spin-driven polarization encoding. An empirical nonlinear response model further connects the properties of the signal field to microscopic
exciton and biexciton electronic structure, revealing that interference between resonantly enhanced optical transitions governs the accessible polarization states. The model additionally predicts that modest stabilization of the lowest-energy biexciton could substantially improve the polarization-encoding performance and provide a route toward materials optimization. More broadly, these results motivate closer integration of nonlinear spectroscopy,
semiconductor materials, and
quantum information science in the development of novel light sources for
quantum communication.
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All authors Zijian Gan, Shuyue Feng, Camryn J. Gloor, Wei You, Andrew M. Moran
Optics
Isotropic fabrication of centimeter-scale, low propagation-loss periodically poled lithium niobate nanophotonic waveguides for efficient second harmonic generation
Guanghui Zhao, Yixuan Yang, Renhong Gao et al. (+2 more)
Periodically poled lithium niobate (PPLN) nanophotonic waveguides that simultaneously feature low propagation-loss and uniform periodic poling are essential for a wide range of applications ranging from classical nonlinear frequency-conversion to scalable integrated
quantum technology. However, fabrication imperfections have frequently limited the propagation loss of fully domain-inverted PPLN nanophotonic waveguides to a few dB/cm, primarily due to anisotropic etching issue, thereby restricting the absolute conversion efficiency and scale of photonic integration. Here, we present a fabrication approach that overcomes this challenge, yielding a 1.2-cm-long PPLN nanophotonic waveguide with low propagation loss via femtosecond-
laser photolithography-assisted chemo-mechanical etching (PLACE). By carrying out domain inversion on a planar thin-film prior to waveguide definition, electric-field distortion is minimized during poling, while isotropic etching of the waveguide is achieved by PLACE with an average surface roughness of only 0.34 nm, resulting in uniform poling of duty cycle of 50% and a record-low propagation loss of 0.042 dB/cm in the telecom band. Under continuous-wave pumping at 1525 nm, the device demonstrates a high normalized quasi-phase-matched SHG conversion efficiency of 2021%/W, and an absolute conversion efficiency of 64% at a pump power of 86 mW which represents the state of the art for single-period PPLN nanophotonic waveguides.
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All authors Guanghui Zhao, Yixuan Yang, Renhong Gao, Jintian Lin, Ya Cheng
Medical Physics · Cross-listed from cs.SD
Cross-modal characterization of infant cry: validation of a chest-surface accelerometer in extracting acoustic vocal function measures
Winko W. An, Saketh Sundar, Lisa Yankowitz et al. (+2 more)
Background: Infant cry acoustics provide a promising window into early neurodevelopment and may serve as scalable biomarkers for neurodevelopmental disorders. However, conventional microphone-based recordings are highly susceptible to environmental noise and raise
privacy concerns in real-world clinical settings. Chest-surface accelerometers may offer a robust alternative by capturing vibrations directly from the larynx. Methods: We evaluated the validity of a chest-mounted accelerometer (ACC) for infant cry analysis by comparing acoustic features derived from ACC and simultaneously recorded microphone (MIC) signals during routine vaccination visits. The final sample included 85 infants (41 at 4 months; 44 at 12 months) from a diverse pediatric population. Seven vocal measures were extracted from both modalities, including fundamental frequency (F0), jitter, shimmer, cepstral peak prominence (CPP), and harmonics-to-noise ratio (HNR). Agreement and consistency between modalities was assessed using intraclass correlation coefficients (ICCs). Results: F0 demonstrated excellent agreement between ACC and MIC recordings (ICC > 0.94). Jitter measures also showed good-to-excellent agreement, while CPP demonstrated moderate agreement. Shimmer and HNR showed lower absolute agreement and systematic bias between modalities, reflecting possible differences in signal transmission and noise sensitivity. Conclusion: In summary, chest-surface accelerometers can reliably capture several clinically relevant acoustic features of infant cry, particularly temporal measures of F0 and jitter. This approach offers a noise-robust and
privacy-preserving alternative to microphone-based recordings, supporting its potential use in scalable clinical and developmental research applications.
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All authors Winko W. An, Saketh Sundar, Lisa Yankowitz, Daryush D. Mehta, Carol L. Wilkinson
Computational Physics · Cross-listed from cs.LG
History-aware adaptive reduced-order models via incremental singular value decomposition
Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano et al. (+2 more)
Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the regime represented by offline training data. We develop a projection-based adaptive ROM framework based on incremental singular value decomposition (iSVD), in which occasional full-order operator evaluations provide correction snapshots for online basis updates. The intrusive ROMs considered here are fully parameterized by the basis, so each update naturally propagates to reduced operators and hyper-reduction machinery. Through its evolving singular structure, iSVD retains an encoded history of the observed dynamics and is history-aware in this sense. We study the method on three nonlinear problems of increasing complexity: the one-dimensional viscous Burgers equation, the Sod shock tube, and a stiff one-dimensional ten-species rotating detonation engine (RDE). The Burgers problem is used to analyze the method and compare iSVD with alternative basis adaptation rules, showing that history-aware updates outperform instantaneous updates and that iSVD gives the strongest overall performance. The Sod and RDE cases demonstrate that these advantages persist in more challenging compressible-flow settings. For the RDE problem, the iSVD adaptive ROM improves upon the current state-of-the-art Direct adaptive ROM baseline in both predictive accuracy and computational efficiency. A cost analysis shows that the dominant online cost comes from interacting with the full-order model to obtain correction snapshots, while the iSVD update itself is negligible. These results identify iSVD as an effective mechanism for online learning of reduced subspaces and suggest a path toward ROMs that remain predictive over horizons several orders of magnitude longer than their initial training window.
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All authors Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano, Cheng Huang, Karthik Duraisamy
Instrumentation and Detectors
Analysis of Multifrequency Oscillating Magnetic Fields by Neutron Spin Interferometry
Ryuto Fujitani, Masahiro Hino and Takashi Higuchi
We have developed a methodology for analyzing multifrequency oscillating
magnetic fields using
neutron spin interferometry. A theoretical formulation was derived for the contrast and the phase of the interference pattern for an input oscillating
magnetic field consisting of a series of sine functions with multiple frequencies. According to the formulation, the contrast decays because of the dispersion of the Larmor precession induced by an oscillating field. The formulation also predicts that the phase of the interference pattern is not constant in contrast to single-frequency cases. We conducted an experiment on the JRR-3 C3-1-2-2 beamline to confirm the formulation in the cases of double-frequency oscillating fields in the frequency range from 2500 to 10000~Hz. The measured interference patterns showed reasonable agreement with the theoretical predictions.
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Physics and Society
Population size and centrality effects on NO2 air pollution across and within European cities
Yufei Wei, Rémi Lemoy and Geoffrey Caruso
Understanding how nitrogen dioxide (NO2) varies both within and across cities is essential for assessing urban health inequalities, yet the joint influence of city size and internal structure remains poorly quantified. While it is expected that agglomeration size increases NO2 concentrations and that distance from major urban activities reduces them, the magnitude and form of their combined effect have not been established. Our objective is to move beyond city-specific local context effects and to characterize the general structural form of NO2 distributions, understood as their systematic variation with distance from the center across cities of different sizes. Using both ground monitoring stations and satellite data for 378 European Functional Urban Areas, we estimate parallel models for each measurement type and show that the same structural relationships hold in both cases. We find that NO2 concentrations increase with population size as a power law (with an exponent between 0.14 and 0.22) and decrease with distance from the city center (with an exponent between -0.12 and -0.18), with consistent results across measurement types and robust to local and regional controls. These effects combine into a scalable radial profile, where the total air pollution over a city is proportional to (N/r)^0.16, which generalizes the spatial distribution of NO2 in European cities. This formulation clarifies how total and per-capita pollution depend on the extent to which pollution is integrated and provides a simple framework for evaluating the health implications of urban growth.
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Instrumentation and Detectors
Spin Phase Continuous Modulation: A Method for the Measurement of Neutron Monochromaticity
Ryuto Fujitani, Masahiro Hino and Takashi Higuchi
We present
spin phase continuous modulation (SPCM), a new method for characterization of
neutron beam properties. By utilizing
spin-precession modulation induced by two oscillating
magnetic fields, the method enables precise determination of
neutron velocity and monochromaticity. We derive a theoretical formulation of the SPCM signal and experimentally validate it using measurements with a monochromatized
neutron beam, obtaining reasonable agreement between experiment and prediction. SPCM enables quantitative determination of
neutron beam velocity and monochromaticity.
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Optics
Orbital Altermagnetic Photonic Crystal
Sichang Qiu, Huichang Li, Yan Meng et al. (+6 more)
Altermagnetism features momentum-dependent
spin splitting without net magnetization, extending spintronics beyond conventional ferromagnetism and antiferromagnetism. However, the photonic realization of altermagnetism has remained a formidable challenge due to the fundamental differences between fermionic electrons and bosonic
photons. Here, we report the first experimental realization of an orbital altermagnetic photonic
crystal, based on an antiunitary $C_{4z}\mathcal{T}$
symmetry enforced correspondence between a local $p$-orbital $\sigma/\pi$ doublet and
crystal momentum. We experimentally demonstrate that the resulting system exhibits momentum-dependent
spin splitting with alternating pseudospin polarization and a $d_{xy}$-wave form factor, as confirmed by measured band structures and iso-frequency contours. Moreover, we show that the orbital altermagnetic photonic
crystal supports unique pseudospin-selective transport of electromagnetic waves, including photonic pseudospin splitting and pseudospin filtering. Our results extend the field of alternagnetism to photonic systems, opening a new avenue for designing spinphotonic devices.
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All authors Sichang Qiu, Huichang Li, Yan Meng, Xiang Xi, Zebin Zhu, Ce Shang, Zhen Gao, Tie Jun Cui, Shuo Liu
Biological Physics · Cross-listed from cond-mat.soft
Determinants of Phase-Separation Propensities, Material States, and Material Properties of Biomolecular Condensates
Huan-Xiang Zhou
Phase separation of various materials has been studied for one and a half centuries. In the last two decades, phase separation of
proteins and nucleic acids has received enormous
attention, due its relevance to cellular functions. However, many of the observations on the resulting biomolecular condensates lack a theoretical underpinning. The first goal of this Account is to put forward theoretical frameworks for the phase-separation propensities, material states, and material properties of biomolecular condensates. Using these frameworks, I rationalize mechanistic interpretations from our recent experimental and computational studies, and synthesize these studies with prior literature to draw new conclusions. For phase-separation propensities, I relate the threshold (or saturation) concentration to the excess chemical potential in the dense phase, which in turn depends on intermolecular interaction strength and valency. For material states, I posit that
liquid droplets form via complete phase separation, whereas amorphous dense
liquids, reversible aggregates, and gels arise from premature termination of spinodal decomposition, due to overly weak or overly strong interactions or directional interactions. In particular, gels and aggregates are different forms of dynamically arrested states, with gels driven by tip growth via directional interactions whereas aggregates driven by monomer addition at interior sites to maximize valency. For material properties, I highlight the crucial roles of the stress relaxation time, which is determined by the mean lifetime of intermolecular bonds in a condensate. This relaxation time dictates how the condensate manifests viscoelasticity, including shear thickening and shear thinning, and accounts for the wide variation in zero-shear viscosity among different condensates.
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Chemical Physics
Excited States from Restricted Open Shell Plane-Wave DFT
Michael J. Sahre, Marco Romanelli, Martijn Marsman et al. (+2 more)
Variational excited-state density functional theory (DFT) enables the calculation of excited states at a cost comparable to ground-state calculations, but single-configuration approaches often suffer from
spin contamination. We implement restricted open-shell Kohn-Sham (ROKS) DFT, which recovers
spin-pure singlet excitation energies via the variational minimization of a weighted combination of mixed-
spin and triplet configurations, within the plane-wave projector augmented-wave framework of VASP. The energy functional is optimized using a preconditioned conjugate-gradient or a direct inversion in the iterative subspace
algorithm, and analytical atomic forces are derived. The implementation is validated for eight organic molecules by comparison to the Q-Chem
quantum chemistry code, yielding mean deviations of approximately $30\,\mathrm{meV}$. As a solid-state application, we investigate the three lowest lying excitations of MgO with a neutral oxygen vacancy. For a dielectric-dependent hybrid functional, vertical excitation energies from ROKS and time-dependent density functional theory (TDDFT) differ on average by about $0.21\,\mathrm{eV}$. The Franck-Condon shifts deviate on average by $0.14\,\mathrm{eV}$ between the two methods and mass-weighted displacements between the excited states and the ground state by $0.12\,\mathrm{amu}^{1/2}$ Ang. Additional calculations at the PBE level reveal that these properties depend less strongly on the DFT functional for ROKS than for TDDFT. These results demonstrate that ROKS provides excitation energies and excited-state forces with an accuracy similar to TDDFT while retaining the favorable scaling of ground-state DFT, making it a promising approach for affordable excited-state simulations in extended systems.
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All authors Michael J. Sahre, Marco Romanelli, Martijn Marsman, Leticia González, Georg Kresse
Fluid Dynamics · Cross-listed from cond-mat.soft
Geometric Origin of Macroscopic Alignment in Granular Flows
Christopher Harper, Eric C.P. Breard, George W. Bergantz et al. (+1 more)
Predicting the nematic
alignment of nonspherical particles in sheared granular flows is essential for understanding the rheology, packing, and constitutive response of dense particulate media. While macroscopic fabric is typically attributed to complex multibody interactions, stress transmission, and dissipative collisions, empirical observations reveal that the steady-state nematic order parameter $S_2$ depends primarily on particle aspect ratio and remains remarkably insensitive to shear rate and interparticle friction. Here, we show that this leading-order
alignment emerges directly from single-particle boundary geometry without resolving dynamical equations of motion. Assuming uniform contact probability along a particle perimeter, we derive an analytical transform linking local boundary
curvature $\kappa(\theta)$ to the distribution of contact normals, $P(\theta) \propto 1/\kappa(\theta)$, which in turn geometrically constrains the phase space of admissible particle orientations. This minimal framework accurately predicts the magnitude of $S_2$ across the full continuum of aspect ratios for smooth ellipsoids as well as the singular limit of faceted rectangles and cylinders. Our analytical predictions capture the envelope of three-dimensional discrete element simulations and match laboratory measurements on sheared rice grains and glass cylinders across decadal variations in shear rate. By identifying particle geometry as the primary control parameter for granular
alignment, this work provides a first-principles physical foundation for
geometric saturation at the critical state, establishing a universal baseline upon which dynamical and frictional effects act as secondary modulations.
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All authors Christopher Harper, Eric C.P. Breard, George W. Bergantz, PJ Zrelak