Papers

The fast ignition paradigm for inertial confinement fusion (ICF) allows for extremely high gains but requires fuel to be heated very quickly to outpace hotspot disassembly and energy losses. This demands lasers with high power and intensity, posing engineering challenges that have called into question the fundamental practicality of fast ignition. Magnetized liner inertial fusion (MagLIF) circumvents these problems through its large-aspect-ratio cylindrical geometry and strong axial magnetic fields that allow for ignition at lower areal densities. Furthermore, MagLIF's large aspect ratio and higher yields relax other constraints on energy deposition and repetition rate while its axial magnetic fields can be used to collimate ignitor electrons and thereby increase allowed standoff distance and save on ignitor energy. This tremendous overall relaxation of the engineering constraints that have historically limited the practicality of fast ignition suggests that the paradigm may be considerably more viable in a MagLIF context.

Using clean numerical simulation (CNS) which can give very accurate spatiotemporal trajectory of Navier-Stokes turbulence in a finite but long enough interval of time, we give some numerical evidences that the Navier-Stokes equations admit distinct global solutions from almost the same initial conditions whose difference is very small, i.e. even at the order $10^{-40}$ of magnitude. Hopefully these examples could provide some enlightenments for the uniqueness and existence of Navier-Stokes equations, which are related to one Millennium Prize Problem of Clay Institute.

Low pressure gaseous Negative Ion Time Projection Chambers (NITPCs) have been used previously by the DRIFT experiment to search for a directional Dark Matter (DM) signature. The main challenge with using a Negative Ion Drift (NID) gas target is the significantly lower gas gains to which they are typically limited. Recently, a MMThGEM device has been successfully demonstrated as an excellent gain stage device in the NID gas SF$_6$; capable of producing gas gains comparable with the electron drift gas CF$_4$. The next major challenge is to extend this high gain capability to multi-dimensional readout for the purpose of particle track reconstruction. The MMThGEM is therefore ideal for coupling to a strip readout detector like a Micromegas to achieve a high gain multi-dimensional Negative Ion (NI) readout plane, which is potentially suitable for the scale up required by future searches proposed by the CYGNUS consortium. In this paper, the first high gain demonstration of such a MMThGEM-Micromegas detector in low pressure SF$_6$ is described. This includes detector characterisation in a small test vessel resulting in the largest NI gas gain ever reported, 1.22 $\pm$ 0.08 $\times$ 10$^5$ , and directionality with alpha particles. Finally, this gain characterisation and tracking capability is leveraged to measure the energy and range of events, and identify those consistent with Nuclear Recoils (NRs), in a large cubic metre scale volume of SF$_6$ for the first time.

We demonstrate in vivo dynamic optical coherence tomography (DOCT) imaging of zebrafish development from 2 weeks to 12 months post-fertilization, integrated with polarization-sensitive OCT (PS-OCT), OCT angiography (OCTA), and histological validation. Two DOCT algorithms were utilized: logarithmic intensity variance and late OCT correlation decay speed, which characterize the occupancy of dynamic scatterers and their motion speeds, respectively. Our results show that skin stripes exhibit high DOCT signals and it varies among the pigment-cell types. Furthermore, the combination of DOCT and PS-OCT captures the maturation of these stripes. In addition, DOCT and OCTA successfully visualized the developmental progression of blood and lymphatic vessels, as well as spinal tissues.

Radio relics in merging galaxy clusters are widely interpreted as synchrotron emission from relativistic electrons accelerated at large-scale shocks. However, the efficiency of diffusive shock acceleration (DSA) is expected to be suppressed in the low-Mach-number, weakly turbulent environments of cluster mergers; furthermore, recent theoretical insights suggest that DSA may not constitute a viable physical mechanism for such environments. In this work, we investigate ballistic surfing acceleration (BSA) as an electrodynamically grounded alternative for electron energization that bypasses the need for prescribed diffusion coefficients. We formulate BSA under typical cluster shock conditions, deriving the balance between coherent acceleration by the convective electric field and radiative losses from synchrotron and inverse-Compton cooling. This equilibrium defines the maximum reachable electron energy and constrains the resulting steady-state spectrum. By forward-modeling the associated synchrotron emission and comparing it with integrated radio observations of the `Sausage' (CIZA J2242.8+5301) and `Toothbrush' (1RXS J0603.3+4214) relics, we find that the observed spectral curvature and high-frequency steepening are consistent with BSA-limited energies, provided that active acceleration involves only a minute participation fraction ($10^{-9}$-$10^{-8}$) of the radiating electrons. Despite this high selectivity, BSA effectively produces Lorentz factors of $\gamma \sim 10^4$-$10^5$. Our results suggest that radio relics serve as prime astrophysical laboratories for probing coherent acceleration, with the BSA framework providing a robust and physically consistent explanation for electron energization in cluster shocks.

We examine dust acoustic (DA) solitary structures in an unmagnetized, collisionless dusty electron positron ion (epi) plasma in which electrons and positrons are described by finite temperature Fermi Dirac statistics and ions obey a superthermal kappa distribution. A normalized fluid Poisson model is formulated using polylogarithm based expressions for the partially degenerate electrons positrons, while cold negatively charged dust grains provide the inertial response. Linear dispersion analysis yields a modified DA phase speed. Nonlinear solitary structures are investigated using the Sagdeev pseudopotential method. The system is found to support only negative potential (rarefactive) DA solitary waves within a bounded subsonic Mach number interval. The critical Mach number, consequently, the corresponding linear DA speed show an explicit dependence on the degeneracy parameters and the ion spectral index. The amplitude and width of the solitary structures are shown to be highly sensitive to the electron (positron) degeneracy strength, the ion positron concentration ratios, the ion temperature ratio, and the superthermality of the ions. A small amplitude approximation of the pseudopotential reduces the system to the Korteweg de Vries limit, providing closed form expressions for the soliton characteristics, in agreement with the Sagdeev predictions. The results clarify the combined roles of finite temperature degeneracy and superthermal ions in shaping nonlinear DA dynamics in space and astrophysical dusty plasmas.

Employing both electrodynamics coupled with molecular dynamics (ED-MD) simulations for atomistic models and the dynamic instability theory of electrocapillary wave, we investigate the structure evolutions and thermal runaway process of Cu, Ti and W nanotips with radii of curvature of 1 nm and 5 nm under various radiofrequency electric field conditions. The associated critical parameters including the critical electric field, spatial and temporal scales of the electrohydrodynamic instability of molten apexes are obtained by proposing the workflows that utilize the atomistic models in ED-MD simulations to calculate kinematic viscosity tensor components and mass density spatial distributions for the nanomelts with electric fields. Our current ED-MD simulations for nanotips show a non-monotonical variation of the time delay versus the electric field frequency for metal nanotips, and the presence of a critical rf electric field amplitude triggering the thermal runaway regardless of the field frequency. The calculated mass densities and kinematic viscosities of nanomelts for metal nanotips are found to be drastically different to those of bulk liquid metals at the melting point. Specifically, the viscosity of nanomelt under the rf electric field is revealed to be several orders of magnitude higher than the bulk liquid metal, resulting in substantial increase of spatial and temporal scales in the instability theory of electrocapillary wave within the viscosity-dominated regime, compared to the results of ED-MD simulations for Cu and Ti metals, while good agreement between the two methods on the critical wavelength and time delay of thermal runway is found for W nanotips.

The long-time evolution of decaying homogeneous turbulence is a fundamental building block of the subject. We investigate the problem by using a comprehensive suite of Direct Numerical Simulations. The simulations cover initial Taylor microscale Reynolds numbers $Re_\lambda$ from $30 \text{ to } 145$, with multiple independent realizations obtained at each $Re_{\lambda}$ to ensure statistical robustness. The energy spectrum is initialized with the Birkhoff-Saffman (BS) form (with $E(k)\sim k^2$ for small $k$) in one case, and the Loitsianskii-Kolmogorov-Batchelor (LKB) form (with $E(k)\sim k^4$ for small $k$), in another. Simulations are performed for unprecedented durations, of the order of 200,000 initial eddy-turnover times in some instances. For both BS and LKB, the turbulent kinetic energy $En$ shows, after an initial transient, unambiguous power-law decay, $En\sim t^{-n}$, with nearly constant decay exponents $n$, whose values are consistent with past theoretical results (and thus not universal). We compute various length scales, second-order structure functions, and the spectral form at large wavenumbers; we note that an initially set $-5/3$ slope disappears quickly, while a perceptible $-1$ power region appears. In particular, we compare the present findings with predictions from the recent theory for decaying turbulence developed by Migdal 2026 Philos. Trans. R. Soc. A 384, 20250032. (doi:10.1098/rsta.2025.0032). The agreement for the BS case is excellent except for the large-wavenumber spectrum. A general discussion and assessment of results is provided in terms of the putative universality of energy decay. A main conclusion is that the energy decay is significantly influenced by “boundary effects", and that universality likely manifests only when those effects are removed. Alternatively, it may be more useful to discuss the universality of enstrophy decay.

We formulate seasonal rainfall prediction as a reduced-order nonlinear forecasting problem, embedding coupled Indian-Pacific Ocean variability into a low-dimensional state space and projecting it forward using deep neural networks. Variables include Nino 3.4, the Indian Ocean Dipole (IOD), the Indian Ocean meridional SST gradient, and selected empirical orthogonal functions. Monthly time series of the variables then form the input into deep neural networks which project rainfall further into the future. Forecasts for the 2025 austral spring were generated and archived in the Mendeley database during the winter. Subsequent rainfall data demonstrated a high level of agreement with the forecasts, providing a validation of the method and supporting the hypothesis that chaotic yet conditionally predictable dynamics underpin spring rainfall variability in southeastern Australia.

Atmospheric ammonia emissions are harmful to ecosystems and human health. These emissions have traditionally been monitored using thermal infrared spectrometers, though such techniques are limited by thermal contrast requirements, the coarse spatial resolution of existing satellite sensors, and low measurement frequency of higher-resolution aerial surveys. Here, we show that ammonia emissions can be quantified using shortwave infrared imaging spectroscopy, circumventing these challenges by using reflected sunlight instead of thermal emission for signal and by enabling a large class of existing and future imaging spectrometers to enter the ammonia observing system. As a proof of concept for this newly discovered capability, we use Tanager-1 satellite data to quantify emissions from industrial point sources of ammonia in Pakistan and Uzbekistan.

The discovery of constitutive laws for complex materials has historically faced a dichotomy between high-fidelity data-driven approaches, which demand prohibitive full-field experimental data, and traditional engineering fitting, which often yields numerically unstable models outside calibration regimes. In this work, we propose an Engineering-Oriented Symbolic Regression (EO-SR) framework that bridges this gap by leveraging Large Language Models (LLMs) as "Physics-Informed Agents." Unlike unconstrained symbolic regression, our framework utilizes an LLM Agent to zero-shot synthesize executable physical constraints – specifically thermodynamic consistency and frame indifference – transforming the search process from mathematical curve-fitting into a physics-governed discovery engine. We validate this approach on the hyperelastic modeling of rubber-like materials using standard Treloar datasets. The framework autonomously identifies a novel hybrid constitutive law that combines a Mooney-Rivlin linear base with a rational locking term. This discovered model not only achieves high predictive accuracy across multi-axial deformation modes (including zero-shot prediction of pure shear) but also guarantees unconditional convexity. Finite element validation demonstrates that while industry-standard models (e.g., Ogden N=3) fail due to numerical singularities under severe transverse compression, the EO-SR-discovered model maintains robust convergence. This study establishes a generalized, low-barrier pathway for discovering simulation-ready constitutive closures that satisfy both data accuracy and rigorous physical laws.

The thermoelastic metamaterial based on a bimaterial hybrid-honeycomb structure, exhibiting simultaneously negative Poisson's ratios and negative thermal expansion coefficients is very promising for various application. This work is dedicated to the machine learning (ML)-enabled inverse design of such structure, on the basis of high-throughput simulation and neural network models. A large dataset is generated through computational homogenization of structures with varying geometrical features and base material properties. A forward ML model is first trained to efficiently and accurately predict the effective thermoelastic properties for a given structure design. Subsequently, inverse ML models are developed to suggest geometrical features and base materials for desired target properties. To address various inverse design scenarios, six different models are proposed, each defined by different combinations of target effective properties and structural design variables. The trained forward model is integrated into the loss functions of the inverse models and is also employed to generate additional datasets for cases with fixed base materials. The good predictive performance of the forward and inverse ML models is demonstrated by representative design examples. These ML models can be applied to efficiently solving specific inverse design tasks involved in the practical application of the thermoelastic metamaterial in novel engineering systems.

Efficient transmission of elastic waves across interfaces is central to several applications, including medical imaging, seismic isolation, and transducer design. Interfaces with abrupt changes in the material properties significantly impede wave transmission, leading to reflections. This limitation, known as impedance mismatch, becomes even more prominent for mode conversion between different wave types due to polarization mismatch. In this study, we investigate a mechanism employing two-dimensional functionally graded anisotropic metamaterials to facilitate longitudinal–shear mode conversion as waves propagate from a stiff to a compliant medium. By embedding density and anisotropic shape gradients within the functionally graded metamaterial, polarization-induced impedance mismatch is mitigated and efficient mode conversion is enabled. We use unit cell dispersion analysis to tailor the frequency range for mode conversion through gradation in the dispersion behavior and coupling between modes. Using frequency-domain finite element analysis, we demonstrate broadband mode conversion across interfaces with large stiffness contrast operating in the 1–10 kHz range. We then experimentally validate and quantify mode conversion through full-field velocity measurements on an additively manufactured specimen. We further apply the methodology to design a device capable of converting radial–tangential wave modes.

Radiative transfer in absorbing-scattering media requires solving a transport equation across a spectral domain with 10^5 - 10^6 molecular absorption lines. Line-by-line (LBL) computation is prohibitively expensive, while existing approximations sacrifice spectral fidelity. We show that the Young-measure homogenization framework produces solution tensors I that admit low-rank tensor-train (TT) decompositions whose bond dimensions remain bounded as the spectral resolution Ns increases. Using molecular line parameters from the HITRAN database for H2O and CO2, we demonstrate that: (i) the TT rank saturates at r = 8 (at tolerance e = 10^-6) from Ns = 16 to 4096, independent of single-scattering albedo, Henyey-Greenstein asymmetry, temperature, and pressure; (ii) quantized tensor-train (QTT) representations achieve sub-linear storage scaling; (iii) in a controlled comparison using identical opacity data and transport solver, the homogenized approach achieves over an order of magnitude lower L2 error than the correlated-k distribution at equal cost; and (iv) for atomic plasma opacity (aluminum at 60 eV, TOPS database), the TT rank saturates at r = 15 with fundamentally different spectral structure (bound-bound and bound-free transitions spanning 12 decades of dynamic range), confirming that rank boundedness is a property of the transport equation rather than any particular opacity source. These results establish that the spectral complexity of radiative transfer has a finite effective rank exploitable by tensor decomposition, complementing the spatial-angular compression achieved by existing TT and dynamical low-rank approaches.

The size dependence of the equilibrium droplet contact angle is governed by line tension. In this work, we identify a contribution to line tension arising from gravitational effects and pressure-induced changes in volume-fraction-dependent interfacial tensions within an adsorption layer. This mechanism addresses a multiscale problem of line tension in droplets ranging from nanometric to millimetric sizes that change sign and span several orders of magnitude, in agreement with experimental and simulation results. The sign of the apparent line tension is controlled by surface wettability, the initial volume fraction in the adsorption layer, and the droplet size, which also strongly influences its magnitude. Our results provide a unified physical interpretation of the experimentally observed variability in both the sign and magnitude of line tensions.

The Copernicus Atmospheric Monitoring Service provides reanalysis products for atmospheric composition by combining model simulations with satellite observations. The quality of these products depends strongly on the availability of the observational data, which can vary over time as new satellite instruments become available or are discontinued, such as Carbon Monoxide (CO) observations of the Measurements Of Pollution In The Troposphere (MOPITT) satellite in early 2025. Machine learning offers a promising approach to compensate for such data losses by learning systematic discrepancies between model configurations. In this study, we investigate machine learning methods to predict monthly-mean total column of Carbon Monoxide re-analysis from a control model simulation.

The physical nature and origins of the newly discovered class of Odd Radio Circles (ORCs) remain unclear. We investigate a model whereby ORCs are synchrotron-emitting vortex rings formed by the Richtmyer-Meshkov instability (RMI) when a shock interacts with a low-density fossil radio lobe in the intergalactic medium using 3D magnetohydrodynamic simulations. These rings initially exhibit oscillatory behavior that damps over time. We implement a new method to model Inverse-Compton cooling and synchrotron cooling at high frequencies in a scale-free manner, enabling us to test a wide range of model parameters against the observational constraints. We find that shock strengths of Mach 2-4 are consistent with the data, as expected in accretion, merger-driven, or active galactic nuclei-driven shocks. We find that the initial size of the bubbles required to explain the rings ranges from 140 to 250 kpc, with initial energy in the bubble of order $10^{57}-10^{59}$ erg, consistent with fossil lobes inflated by moderately powerful radio galaxies. Derived ambient pressures and densities place ORCs in low density environments, such as the outskirts of galaxy groups with ages of order 70-200 Myr. Our synthetic radio maps match the polarization properties of ORC1 and predict a dependency of the tangential magnetic field angle on the aspect ratio of ORCs. A key distinguishing trait of the RMI-driven vortex ring model is that it does not require the ORC to be centered on its host galaxy and is therefore redshift agnostic.

In SEP events, velocity dispersion (VD) is characterized by the earlier arrival of faster, higher-energy particles relative to slower ones, assuming negligible acceleration time and transport effects. The "Labor Day event" at Parker Solar Probe (PSP) on 2022 September 5 provided a unique arrival profile, in which the medium energy (~ few MeV) particles arrive earlier than both lower and higher energy particles. This created a so-called "nose" structure in the intensity spectrogram formed by measurements from the two energetic particle instruments, EPI-Lo and EPI-Hi, of the Integrated Science Investigation of the Sun (ISOIS) suite. Unlike typical VD, the delayed arrival of higher energy particles compared to medium energy particles, i.e., the "inverse velocity arrival" (IVA), could be caused by various acceleration, transport, and instrumental effects, including shock acceleration. By applying a new method based on the contour-line of the intensity, we found 14 IVA events in the ISOIS observations up to the end of 2024. Several parameters that may modify velocity dispersion characteristics are further explored including the spacecraft radial distance, the speed of corresponding CMEs and shocks, the angle between the shock normal and the upstream magnetic field, and the spacecraft magnetic footpoint longitudinal separation from the flare location. The energy of the early arriving particles, i.e., the nose energy, can be grouped into low (L, <0.5 MeV), medium(M, 0.5 - 5 MeV), and high(H, >5 MeV) categories. Most (11/14) of the IVA events have medium nose energies. This SEP list provides ingredients for examination of shock acceleration in the inner heliosphere, and the existence of IVA events sheds new light on the acceleration and propagation of SEPs.

Quantum-chemical calculations often make use of point-group theory to exploit molecular symmetry, resulting in a reduction of the computational cost and in insights into the electronic structure. This exploitation is often limited to subgroups of $D_{2h}$ which are Abelian with real characters. Here, we extend the symmetry exploitation to Abelian point groups with complex characters. Such point groups are often encountered in calculations that involve finite magnetic fields, though their occurrence is not limited to these cases alone. We present the evaluation of integrals over symmetry-adapted orbitals using the double-coset decomposition, as well as the use of these symmetries in the contractions needed within post Hartree Fock calculations in the context of block tensors. Efficiency gains are discussed for four simple hydrocarbons that exhibit a complex Abelian point group in the presence of a magnetic field.

In this work, a convergent low-temperature cluster expansion of the one-dimensional long-range ferromagnetic Ising model with polynomial decay $\alpha\in (1,2]$ is developed; that is, $J(r)=r^{-\alpha}$. As an application, the $n$-point correlations are studied and the two-point correlation is shown to be algebraic with rate of decay exactly $\alpha$.

Properties of crystalline materials are closely linked to microstructure arising from the spatial arrangement, orientation, and phase of nanocrystals. Rapid characterization of crystalline microstructure can accelerate the identification of these links and the development of materials with desired properties. Here, we combine a machine learning framework with four-dimensional scanning transmission electron microscopy (4D-STEM) to enable fast inference of crystalline microstructure over large fields of view. The framework employs a transformer-based architecture to predict crystallographic orientations and phases from 4D-STEM diffraction patterns, yielding spatially resolved maps of microstructural features at the nanoscale. With this framework, crystallographic orientations are inferred up to two orders of magnitude faster than widely used correlative template-matching approaches. This capability enables high-throughput characterization of complex crystalline materials and facilitates the establishment of structure-property relationships central to materials design and optimization.

Identifying systematic patterns in seismicity that precede large earthquakes remains a central challenge in statistical seismology. In this work, we present a methodological framework for detecting spatiotemporal anomalies in seismicity using the evolution of gridded b-values. Focusing on the Japanese subduction zone, we construct daily b-value fields on a fine spatial grid by aggregating local seismicity over moving time windows, yielding a continuous 2+1D representation of seismic-state evolution. We formulate the problem as a binary classification task in which spatiotemporal blocks extracted from these $b$-value fields are labeled according to the occurrence of a target earthquake with \Mw $\geq 5$ in the central region within the next day. To model this data, we introduce a hybrid deep-learning architecture that combines a spatial convolutional encoder with a temporal convolutional network, enabling joint learning of spatial structure and temporal dynamics. A progressive meta-epoch training scheme is employed, in which the model is iteratively updated using a time-forward strategy that mirrors operational deployment and mitigates issues related to nonstationarity. This paper is strictly methodological in scope. It describes the construction of b-value fields, the spatiotemporal sampling strategy, the network architecture, and the progressive training and internal validation framework used for model development and parameter selection.

We report a $k$-point extension of the second-order co-iterative augmented Hessian (CIAH) algorithm, termed $k$-CIAH, for Pipek-Mezey (PM) localization of Wannier functions (WFs). By exploiting an efficient evaluation of the Hessian-vector product, $k$-CIAH achieves $O(N_k^2 n^3)$ scaling in both CPU time and memory, matching that of previously reported first-order $k$-space approaches while improving upon the $O(N_k^3 n^3)$ scaling of $\Gamma$-point CIAH, where $N_k$ denotes the number of $k$-points sampling the first Brillouin zone and $n$ characterizes the unit-cell size. Benchmark calculations on a diverse set of solids – including insulators, semiconductors, metals, and surfaces – demonstrate the fast and robust convergence of $k$-CIAH-based PMWF optimization, which yields an overall computational efficiency approximately 2-3–fold higher than first-order $k$-space methods and orders of magnitude higher than $\Gamma$-point CIAH for localizing 1000-5000 orbitals. The quality of the resulting PMWFs is further validated by accurate electronic band structures obtained via PMWF-based Wannier interpolation.

We incorporate a solver for the fragment problem with accuracy beyond coupled cluster singles and doubles (CCSD) into the previously proposed static embedding framework, MPCC. To this end, we employ a CCSDT solver for the fragment subsystem. For the environment subsystem, we construct a perturbative estimate of the triples amplitudes, explicitly accounting for feedback from all fragment amplitudes. The resulting approach is denoted MPCCSDT(pt). We further introduce a more complete formulation in which feedback from the environment amplitudes to the fragment amplitudes is also included. This scheme involves an iterative treatment of the environment triples amplitudes and is denoted MPCCSDT(it). In addition, we assess the accuracy of the previously proposed low-level method by introducing a modified low-level approach that incorporates a lowest-order treatment of selected long-range effects, including spin fluctuations and charge polarization. All resulting approaches may be viewed as post-CCSD(T) methods. We therefore consider test cases for which CCSD(T) exhibits substantial deviations from CCSDT. Our results demonstrate that inclusion of triples amplitudes at the fragment level alone is insufficient; a perturbative treatment of the environment triples amplitudes is required. For many energy-difference applications, feedback from the environment triples amplitudes to the fragment amplitudes, is not essential, but it does play a role in the very challenging molecules. A very interesting finding from our study is that in some challenging cases, we need an improved (second-order) perturbative method for the SD amplitudes, going beyond the first-order one used in our earlier work.

We present comprehensive determinations of the diffusion coefficients $D$ at $T=24\,\degree$C for trace amounts of naturally abundant Rb atoms in inert, naturally abundant He, Ne, N$_2$, Ar, Kr, and Xe buffer gases using a single measurement technique. We establish a spatially periodic population grating in the Rb sample using two laser beams that intersect at a small angle $\theta$ of a few milliradians. The atomic population grating decays exponentially in time due to diffusive motion induced by momentum-changing elastic collisions between Rb and buffer gas atoms or molecules, and is monitored by observing the scattered field from a read-out beam. We distinguish the contribution of diffusion from other collisional processes by measuring the characteristic $\theta^2$ dependence of the decay rate. We also measure the systematic dependence of the decay rate on the buffer gas pressure over a range of $7\,000$ Pa to $90\,000$ Pa. In this manner, we obtain diffusion coefficients at standard atmospheric pressure of $101\,325$ Pa and at a temperature of 24.0(5)~$^\circ$C. We obtain weighted averages of $0.33(5)$ cm$^2$/s, $0.214(14)$ cm$^2$/s, $0.132(7)$ cm$^2$/s, $0.123(9)$ cm$^2$/s, $0.093(9)$ cm$^2$/s, and $0.073(4)$ cm$^2$/s for Rb in He, Ne, N$_2$, Ar, Kr, and Xe, respectively. We compare this data with diffusion coefficients obtained using quantum, classical, and semi-classical theoretical methods based on the most accurate interatomic interaction potentials from the literature. Our computed diffusion coefficients based on the quantum theory agree with the experimental determinations when systematic effects are taken into account. Our measurements and modeling are relevant to the optimization of magnetometers, imaging using spin-polarized noble gases, tests of collision models based on interatomic potentials, and the development of pressure sensors.

Accurate characterization of subsurface flow is critical for Carbon Capture and Storage (CCS) but remains challenged by the ill-posed nature of inverse problems with sparse observations. We present Function-space Decoupled Diffusion Posterior Sampling (Fun-DDPS), a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling. Our approach learns a prior distribution over geological parameters (geomodel) using a single-channel diffusion model, then leverages a Local Neural Operator (LNO) surrogate to provide physics-consistent guidance for cross-field conditioning on the dynamics field. This decoupling allows the diffusion prior to robustly recover missing information in parameter space, while the surrogate provides efficient gradient-based guidance for data assimilation. We demonstrate Fun-DDPS on synthetic CCS modeling datasets, achieving two key results: (1) For forward modeling with only 25% observations, Fun-DDPS achieves 7.7% relative error compared to 86.9% for standard surrogates (an 11x improvement), proving its capability to handle extreme data sparsity where deterministic methods fail. (2) We provide the first rigorous validation of diffusion-based inverse solvers against asymptotically exact Rejection Sampling (RS) posteriors. Both Fun-DDPS and the joint-state baseline (Fun-DPS) achieve Jensen-Shannon divergence less than 0.06 against the ground truth. Crucially, Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines, achieving this with 4x improved sample efficiency compared to rejection sampling.

Bragg coherent diffraction imaging (BCDI) phase retrieval becomes rapidly difficult in the strong-phase regime, where a crystal contains distortions beyond half a lattice spacing. An important special case is the phase domain problem, where blocks of a crystal are displaced with sharp jumps at domain walls. The strong-phase, here defined as beyond $\pm \pi/2$, generates split Bragg peaks and dense fringe structure for which classical iterative solvers often stagnate or return different solutions from different initialisations. Here, we introduce an unsupervised Fourier Vision Transformer (Fourier ViT) to solve this block-phase, multi-domain phase-retrieval problem directly from measured 2D Bragg diffraction intensities. Fourier ViT couples reciprocal-space information globally through multiscale Fourier token mixing, while shallow convolutional front and back-ends provide local filtering and reconstruction. We validate the approach on large-scale synthetic datasets of Voronoi multi-domain crystals with strong-phase contrast under realistic noise corruptions, and on experimental diffraction from a $\mathrm{La}_{2-x}\mathrm{Ca}_x\mathrm{MnO}_4$ nanocrystal. Across the regimes considered, Fourier ViT achieves the lowest reciprocal-space mismatch ($\chi^2$) among the compared methods and preserves domain-resolved phase reconstructions for increasing numbers of domains. On experimental data, with the same real-space support, Fourier ViT matches the iterative benchmark $\chi^2$ while improving robustness to random initialisations, yielding a higher success rate of low-$\chi^2$ reconstructions than the complex convolutional neural network baseline.

Accurate many-body treatments of condensed-phase systems are challenging because correlated solvers such as full configuration interaction (FCI) and the density matrix renormalization group (DMRG) scale exponentially with system size. Downfolding and embedding approaches mitigate this cost but typically require prior selection of a correlated subspace, which can be difficult to determine in heterogeneous or extended systems. Here, we introduce a stochastic cluster expansion framework for efficiently recovering the total correlation energy of large systems with near-DMRG accuracy, without the need to select an active space a priori. By combining correlation contributions from randomly sampled environment orbitals with an exactly treated subspace of interest, the method reproduces total energies for non-reacting and reactive systems while drastically reducing computational cost. The approach also provides a quantitative diagnostic for molecule-solvent correlation, guiding principled embedding decisions. This framework enables systematically improvable many-body calculations in extended systems, opening the door to high-accuracy studies of chemical processes in condensed phase environments.

Standard harmonic-to-anharmonic thermodynamic integration (TI) is known to develop a near singularity in the integrand for solids exhibiting diffusive degrees of freedom, such as rotating functional groups or migrating defects. This pathology results in numerical challenges for estimating absolute free energies within a single thermodynamic cycle. In this work, we introduce a simple regularization that removes this singularity and yields a well-behaved integrand that can be accurately evaluated on a uniform grid. The approach – termed Regularized End-point Gradient (REG) TI – is demonstrated on a model system and on predicting the relative stability of paracetamol polymorphs for which quasi-free methyl rotations lead to a near singularity in standard TI. We expect REG TI to simplify anharmonic free energy calculations for solids and to potentially enable their automation.

Operating matter-wave interferometers as quantum detectors for fundamental physics or inertial sensors with unprecedented accuracies relies on noise rejection, often implemented by correlating multiple sensors. They can be spatially separated (gradiometry or gravitational-wave detection) or consist of different internal states (magnetometry or quantum clock interferometry), with a signal-amplitude modulation serving as a signature of a differential phase. In this work, we introduce Parameter Estimation from Amplitude Collapse (PEAC) by applying statistical inference techniques for different magnetically sensitive substates of an atom interferometer. We demonstrate that PEAC provides higher trueness, resulting in a substantially reduced bias compared to standard methods for perfectly correlated signals, while achieving competitive precision near, but not at, vanishing amplitudes. This indicates that vanishing signals do not constitute the most favourable working point for high-accuracy sensing, relevant to quantum clock interferometry. PEAC presents a generally applicable complementary evaluation method for correlated interferometers without phase stability, increasing the overall accuracy and enabling applications beyond atom-based interferometry.