Papers

We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors from photoplethysmography (PPG) attractor geometry. AVCT connects vascular dynamics, PPG observation, delay-coordinate reconstruction, and attractor descriptors within a unified framework for formulating testable hypotheses, without presupposing the physiological sufficiency of a particular low-dimensional feature set. We evaluate a LightGBM-based estimator using leave-one-subject-out cross-validation (LOSO-CV) across two complementary experimental settings. Experiment 1 comprises 46 ICU/surgical subjects from BIDMC and VitalDB, with ECG, PPG, and repeated reference BP measurements. The calibrated full model achieves systolic/diastolic BP (SBP/DBP) mean absolute errors (MAEs) of 2.05/1.67 mmHg, compared with 2.02/1.63 mmHg for the PPG-attractor-only model. Experiment 2 includes 217 ambulatory subjects from the PPG-BP dataset (651 PPG windows; one cuff-based BP reference per subject) and assesses cross-subject calibrated BP reconstruction rather than within-subject BP tracking. The PPG-attractor model achieves SBP/DBP MAEs of 1.98/0.96 mmHg. Across both cohorts, incorporating ECG-derived features provides no practically meaningful improvement in MAE. Calibration substantially reduces between-subject offset error, while morphological and dynamical descriptors contribute complementary predictive information. Although the reported numerical errors fall within commonly cited thresholds associated with AAMI/ISO and BHS criteria, these findings do not establish compliance with device-validation standards. Collectively, the results support AVCT as a mechanistically motivated representation framework for interpretable PPG-based BP estimation and provide a foundation for prospective validation using repeated reference BP measurements and smartphone-acquired PPG.

The Law of Universal Gravitation is part of middle and high school's general physics and astronomy curricula. This topic is included in the most popular physics textbooks available as a fact whose origin remains in the detailed work of Sir Isaac Newton 300 years ago. Consequently, its mathematical form is presented as an equation without any deductive process. Nevertheless, deduction of the mathematical form of this law is an opportunity to discuss how a deductive process can be performed using the data available on the Internet from reliable sources.

In discrete-variable black-box optimization, the number of candidate solutions grows combinatorially, while each evaluation is often expensive. Therefore, it is important to identify promising solutions efficiently within a limited number of trials. Bayesian Optimization of Combinatorial Structures (BOCS), an existing parametric method, works effectively when only a small amount of data is available. However, as the number of observations increases, BOCS tends to repeatedly propose points that have already been evaluated, which leads to search stagnation. A random-point addition strategy has been proposed to address this issue when an evaluated point is proposed, but it cannot sufficiently exploit information from promising data obtained so far. In this study, we propose a hybrid method that uses BOCS as the main search framework and generates alternative unevaluated points using a Gaussian process only when search stagnation is detected. In the Gaussian-process-based component, multiple Lower Confidence Bound (LCB) acquisition functions are adaptively selected to dynamically control the balance between exploitation and exploration. Numerical experiments using fully connected Quadratic Unconstrained Binary Optimization (QUBO) and Higher-order Unconstrained Binary Optimization (HUBO) as black-box functions show that the proposed method finds solutions with better objective values than the conventional random-point addition method in both settings. Additional analyses show that its effectiveness comes from selecting points that promote search progress within Hamming-distance neighborhoods, rather than simply adding low-energy points near promising solutions. Experiments with sparse surrogate models for quantum annealer applications further suggest the importance of retaining near-fully connected representational capacity.

Null points in synthetically tunable molecular aggregates are predicted to generate flat energy bands analogous to those known in strongly correlated condensed-matter physics. For chemistry, null points provide a powerful design principle for photovoltaic materials with selective charge filtering similar to photosynthesis. However, null points have never been experimentally verified because their defining prediction - state localization with selective electron or hole transfer - has remained unobserved. Here, using a donor-acceptor dyad as a minimal model, we provide the first experimental observation of a null point. Impulsive pump-probe measurements reveal charge separation through a near-instantaneously generated locally excited-charge transfer (LE-CT) intermediate that emerges upon solvent stabilization of CT states. Polarization anisotropy directly reveals state localization and selective charge-filtering, spanning balanced electron-hole transfer to selective hole filtering consistent with synthetic design. A generalized vibronic theory of null points explains these observations and identifies the ideal synthetic parameters for achieving null points which are protected from the vibrational bath.

A single-aperture, two-layer Canted-Cosine-Theta (CCT) sextupole magnet using high-temperature superconducting (HTS) ReBCO tape has been developed for the short straight sections (SSS) of FCC through the FCCee-HTS4 project. The magnet was designed, manufactured and tested under cryogenic conditions. Two HTS tapes from two manufacturers have been qualified for this specific application. Design and manufacturing details and cryogenic temperature measurements are presented. This demonstrator represents the first HTS CCT magnet ever constructed.

We report on a gate-based variational quantum classifier implemented with single photons and probabilistic gates, to emulate the standard quantum circuit model framework. We evaluate the expressive power of two deployable quantum neural networks (QNNs) by computing their effective dimension, a capacity measure grounded in a proven generalization-error bound, and compare them with classical artificial neural networks (ANNs) of equivalent trainable-parameter count. Supervised binary classification tasks are used to benchmark performance across photonic and superconducting QNNs, both of which exhibit superior converged (lower) cross-entropy loss and (higher) prediction accuracy relative to matched-parameter ANNs. For a nonlinearly separable task, our photonic QNN with a single pair of trainable parameters successfully converged (loss 0.04 and accuracy 100%), whereas the equivalent ANN failed to learn the decision boundary, saturating at random-guessing performance. We simulate photonic quantum circuits, training them on the XOR problem and a two-class Iris subset using gradient-free optimization, and assess their robustness to sampling errors under realistic noise processes including photon loss and phase-shifter imperfections. Circuits with comparatively high effective dimension were deployed remotely on a six-qubit photonic quantum processor, achieving classification accuracies of up to 100% in both online and offline learning settings. Notably, even the simplest QNN deployed, with just two trainable parameters, successfully solved tasks that classically require ANNs with at least quadruple the number of parameters, suggesting an emergent algorithmic advantage. Overall, these results demonstrate a clear proof-of-principle that gate-based QNNs can be realized and trained effectively on current photonic hardware, providing proof of algorithmic advantage on a gate-based photonic QNN.

We explore Krylov complexity in the BMN matrix model following a systematic reduction of it, known as the pulsating fuzzy sphere model. We present an analytical setup that allows us to calculate Lanczos coefficients in both large and small deformation limits of the matrix model.

Sustained collisionless interpenetrating plasma flows have been generated on the OMEGA laser facility to enable direct investigation of nonlinear evolution of fields generated by electromagnetic kinetic instabilities. FLASH simulations and Thomson scattering measurements are used to determine the plasma conditions achieved. Interpenetrating flows are observed to remain collisionless for at least 11 ns, longer than any prior OMEGA experiment, supporting the growth and nonlinear saturation of the Weibel instability. Resulting magnetic fields are measured using proton radiography. This work establishes a unique platform for late-time filament evolution measurements.

For more than a century, the diffraction limit has defined the resolution achievable by passive optical imaging systems. Although some resolution improvement can be gained through classical data processing of the image, it is limited by the noise arising from quantum nature of light. Minimizing the effect of this noise requires quantum treatment of optical imaging. By reformulating imaging as a problem of quantum measurement and estimation, it becomes possible to identify optimal detection strategies that recover spatial information previously thought inaccessible. This review summarizes the theoretical framework that underpins this development, from the formulation of quantum Cramer-Rao bounds and Chernoff bounds to the construction of receivers that attain them, such as those based on spatial-mode demultiplexing. We show how these methods can beat conventional imaging in the classification, localization, and imaging of sub-Rayleigh incoherent sources. We then discuss extensions to multiparameter and partially coherent scenarios, and highlight the unifying connections between estimation and discrimination tasks. Finally, we survey recent experimental demonstrations that approach quantum-limited resolution and outline emerging applications in microscopy, astronomy, and optical sensing.

Understanding how wakes interact with wind turbine blades under varying operating and inflow conditions is essential for improving fatigue prediction and performance assessment in increasingly dense wind farms. We present an experimental investigation of wake-blade coupling in a model wind turbine, focusing on the role of tip-speed ratio, $\lambda$, under varying free-stream turbulence conditions. Spatially resolved wake velocity measurements are acquired concurrently with distributed blade strain measurements using Rayleigh backscattering fibre-optic sensing, enabling direct, time-synchronised analysis of fluid-structure interaction across the blade's span. The blades' strain dynamics are strongly governed by $\lambda$, where variations of the operating condition of the turbine modify the amplitude, coherence, and the temporal/spectral organisation of the blade's structural dynamics, while free-stream turbulence primarily modulates these responses. Instantaneous joint statistics reveal negligible zero-lag dependence between wake velocity and blade strain, motivating a lagged and frequency-resolved analysis. Cycle-averaged cross-correlation and cross-power spectral density analyses demonstrate that wake-induced blade response is spatially localised within the wake shear layers and organised around rotation-coherent frequencies, with the coupling strength peaking at intermediate downstream locations. These results highlight the dominant role of operating condition in shaping wake-mediated blade loading and demonstrate the value of concurrent, spatially resolved flow-structure measurements for resolving blade-exciting flow dynamics in wind-turbine wakes. Furthermore, a consistent negative-lag peak indicates that blade strain fluctuations systematically precede downstream wake velocity fluctuations, suggesting a causal, blade-driven imprint on the wake.

This paper proposes a new integrated magnetics (IM) design for an isolated zero-voltage-switching (ZVS) Cuk converter (IZCC). In this design, six magnets are wound onto a single magnetic core, and to minimize magnetic core size and losses, both direct current (DC) and alternating current (AC) flux cancellations are considered. The DC flux is fully cancelled, and the AC flux must be cancelled until a limited value such that the input and output inductor currents have enough ripple to provide the conditions for achieving ZVS on all switches. Therefore, the value of the coupling coefficients (CC) between the windings should be considered such that the minimum ripple to achieve ZVS for all the switches is available. The design is implemented on a simple magnetic U-core, and the CC values are specified based on the winding locations and arrangement. To validate the idea experimentally, a hardware prototype is proposed with a power of 0.5 kW, a switching frequency of 150 kHz, and a peak efficiency of 97.25%.

We develop a non-perturbative description of squeezed light generation in an arbitrary lossy structure consisting of multiple coupled microring resonators. This is applied to two ring photonic molecules where the interference of the fields in the coupled rings leads to a modification in the resonance spectrum near a shared resonance. Considering a dual-pump degenerate squeezing scheme under a five resonance approximation, we investigate two methods to suppress parasitic four-wave mixing contributions and compensate for group velocity dispersion within a primary resonator through hybridization effects with a second auxiliary resonator. In the former case, this comes from an effective splitting of the unwanted resonances supporting parasitic four-wave mixing interactions that add thermal noise to the desired degenerate squeezed state. For sufficiently strong coupling between the resonators, we demonstrate near complete suppression of such parasitic processes, resulting in near unit fidelities with the corresponding output state that would arise were the parasitic interactions neglected. In the latter case, the hybridization effectively shifts a pump resonance, realigning the desired dual-pump four-wave mixing process and leading to a significant enhancement of the signal generation and output squeezing.

We present and compare distributed parallelization strategies for the particle-in-Fourier (PIF) schemes used in kinetic plasma simulations. The different strategies are i) domain decomposition, where both the particles and Fourier modes are split between the MPI ranks ii) particle decomposition, where only the particles are split between the ranks and each rank carries all the modes, and, iii) space-time decomposition, in which time parallelization based on the parareal algorithm is added on top of the particle decomposition. We describe the different communication patterns involved in each of the strategies, the parameter regimes where they work best, and explain their advantages and disadvantages. We implement the strategies within the open-source, performance portable library IPPL and conduct scaling studies with 3D-3V Landau damping and Penning trap benchmark problems on Alps and JUWELS booster supercomputers. We analyze the dominant component timings in each of the strategies and identify areas for future optimizations.

Most mathematical models of opinion dynamics treat attitudes as scalar quantities or positions on a low-dimensional ideological axis. Empirical attitudes, however, are bundles of positions across many policy issues, and the geometry of the resulting high-dimensional belief space is non-trivial. This paper develops a network-theoretic framework for analysing how individuals move through such a space between two measurement waves. Continuous attitude profiles in $[0,1]^n$ are discretised onto regular grids of resolution $k$, occupied positions form a network whose adjacency is defined by single-issue unit moves, and densely populated regions are interpreted as network-normative: empirically common configurations of attitudes in the population. We introduce a hierarchy of null models against which observed movement can be benchmarked: a closed-form coverage baseline requiring no behavioural parameters; a local random-walk that retains each respondent's baseline position and asks whether destinations are over-represented in occupied regions relative to a uniform 1- or 2-step move; and a marginal permutation null model that preserves per-issue change distributions while disrupting within-respondent cross-issue coupling. Applying the framework to a two-wave panel of $N=1194$ respondents on $n=10$ issues, we find that the observed inside rate exceeds the coverage baseline by a factor of 36 at the focal resolution $k=3$, exceeds the two-hop random-walk null model by $\sim 0.30$, and exceeds the perturbation null model by $\sim 0.04$; only the one-hop random walk is competitive. The perturbation gap grows from near zero at $k=2$ to $\sim 0.14$ at $k=5$, indicating that coupled cross-issue updating is detectable only at fine resolutions. Network-normative attraction is therefore real but representation-contingent: which null model is exceeded, and by how much, changes systematically with $k$.

Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment. Here we show that populations of individually aligned AI agents can be driven into stable misaligned states through conformity dynamics. Simulating opinion dynamics across nine large language models and one hundred opinion pairs, we find that each agent's behavior is governed by two competing forces: a tendency to follow the majority and an intrinsic bias toward specific positions. Using tools from statistical physics, we derive a quantitative theory that predicts when populations become trapped in long-lived misaligned configurations, and identifies predictable tipping points where small numbers of adversarial agents can irreversibly shift population-level alignment even after manipulation ceases. These results demonstrate that individual-level alignment provides no guarantee of collective safety, calling for evaluation frameworks that account for emergent behavior in AI populations.

This paper explores plasma wave modes using the extended magnetohydrodynamics (XMHD) model, incorporating Hall drift and electron inertia effects. We utilize the geometric optics ansatz to study perturbed quantities, with a focus on incompressible systems. Our research concludes with the derivation of the dispersion relation for incompressible XMHD and the associated eigenvector solutions, offering new perspectives on plasma wave behavior under these extended scenarios. The dispersion relation shows distinct ion cyclotron and whistler wave branches, with characteristic saturation at the ion and electron gyrofrequencies, respectively. Comparisons between Hall MHD and XMHD demonstrate that XMHD provides a more accurate representation of plasma dynamics, especially at higher wave numbers, bridging the gap between simplified models and comprehensive two-fluid descriptions and smoothing out singularities present in Hall MHD solutions and capturing more physics of the full two-fluid model.

A central unresolved question in fusion energy research is whether energetic alpha particles, the primary products of deuterium-tritium fusion reactions, enhance or degrade plasma confinement. In burning plasmas, the operating regime of future devices such as ITER and SPARC, alpha particles become the dominant heating source, yet their impact on confinement has remained uncertain. Here, we present self-consistent simulations of burning plasmas that simultaneously evolve microturbulence, alpha-particle heating, and macroscopic plasma profiles to steady state, and find that alpha particles can substantially improve confinement. Fusion-born alpha particles weakly destabilize toroidal Alfven eigenmodes (TAEs), which nonlinearly enhance zonal flows that shear apart and suppress ion-scale turbulence. The resulting reduction in turbulent heat transport drives stronger core profile peaking, increasing alpha heating by up to 25% and establishing a self-reinforcing feedback loop. This mechanism has no direct analogue in present-day experiments, where external heating dominates, and reveals an intrinsic pathway toward improved confinement in burning plasmas.

We report on the design, construction, and beam test of a pilot three-dimensional projection detector based on opaque water-based liquid scintillator (oWbLS). The detector consists of an $8 \times 8 \times 16$ cm$^3$ acrylic vessel instrumented with three orthogonal planes of Kuraray Y11 multi-clad wavelength-shifting fibers read out by Hamamatsu multi-pixel photon counters. The readout electronics are based on the CITIROC front-end boards developed for the WAGASCI and SuperFGD detectors of the T2K experiment. The detector was filled with oWbLS and tested with cosmic rays and proton beams of 50, 100, 250, and 500 MeV kinetic energy at the NASA Space Radiation Laboratory at Brookhaven National Laboratory. We present three-dimensional event displays of cosmic muon and proton beam candidates, and a study of transverse light confinement via radial charge distribution measurements. The measured data show tighter light confinement than a Geant4 simulation with a 2 cm scattering length, placing the effective scattering length well below 2 cm and confirming effective optical confinement of scintillation light in the oWbLS medium. A first measurement of the hit-level timing resolution using 500 MeV proton beam data yields a single-channel timing resolution of $\sigma_t \approx 0.17$–$0.28$ ns with good photostatistics. These results demonstrate the viability of the 3D-projection oWbLS technology as a scalable, fully-active detector concept for next-generation particle physics experiments.

The one-centre Coulomb-Sturmian convergent close-coupling method is applied to proton collisions with the boron atom and singly charged carbon ion. Here we report an update to our target-structure implementation, in which configuration state functions are constructed using the method of coefficients of fractional parentage. To assess the quality of the structure models for the two targets, we present the excitation energies, oscillator strengths, and dipole polarisabilities obtained from the present configuration interaction calculations. Cross sections for total and state-selective target excitation and electron loss are calculated from 10 keV to 1 MeV. For both systems, the total excitation cross section is found to be dominated by excitation of the $2s$ subshell. This emphasises the importance of a multi-electron description of the target in such scattering calculations. Comparisons with previous theoretical and experimental data are presented and discussed. In particular, we find that the present calculation for the electron-loss cross section in $p$ + C$^{+}$ collisions is in good agreement with the available measurements across the entire overlapping incident-energy range.

While periodic $\mathcal{PT}$-symmetric structures offer a versatile platform for wave tailoring, their scattering responses are typically analyzed using stationary methods that presume dynamical stability. This assumption fails when time-growing bound states emerge, signaling a dynamical instability. Here, we analytically derive the instability threshold for a $\mathcal{PT}$-symmetric chain of $N$ unit cells with gain/loss strength $\gamma$. Our $S$-matrix analysis yields a closed-form threshold, $\gamma_c = 2\sin[\pi/(4N)]$, which scales as $\mathcal{O}(1/N)$ and vanishes in the thermodynamic limit. Consequently, enlarging such structures to access richer stationary band phenomena paradoxically triggers instability at weaker gain/loss. As confirmed by time-domain simulations, exceeding $\gamma_c$ causes exponentially growing bound states to overwhelm the system, rendering standard Bloch-wave descriptions physically irrelevant. Evaluated against this size-dependent threshold, many hallmarks of large $\mathcal{PT}$-symmetric structures, including gain-loss-induced localization, reflectionless transport, and coherent perfect absorbers and lasers, are found to lie within the dynamically unstable regime. Our findings thus establish that physical transport in non-Hermitian periodic systems is governed by a fundamental interplay between stationary band theory and finite-size stability limits.

We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe is given to explicitly accommodate for a finite collection of points in the reference multi-dimensional space of the Neural-ODE where the velocity field is exactly equal to zero. In this way, the gradient-based training is rigorously constrained inside the prescribed hypothesis class while leaving the expressive power of the Neural-ODE unaltered. We rigorously prove the universality of the Neural-ODE under any local constraints in the velocity field and give a computationally convenient way of imposing the fixed points. Our method is then tested on two paradigmatic physical models.

We develop a unified spectral-semigroup framework that connects real-time and imaginary-time quantum dynamics through analytic continuation. Within this formulation, evolution is expressed as an exponential reweighting of spectral components generated by a single operator $\mathcal{G}$, placing unitary and dissipative dynamics on equal footing within a common spectral structure. The mapping naturally induces a nonlocal fractional operator in time, giving rise to a contractive semigroup governed by a square-root spectral deformation and identifying imaginary-time evolution as an effective fractional low-pass filter. While exponential attenuation suppresses high-frequency components, the inverse transformation remains systematically controllable within a well-defined spectral window. In this regime, stable reconstruction of low-energy and coarse-grained dynamical features is achieved, establishing a predictive relation between imaginary-time evolution and recoverable information. This leads to a quantitative description of a bandwidth-resolved asymmetry between forward propagation and inverse recovery. Across systems with continuous and discrete spectra, few-level coherence, and non-Hermitian generators, we demonstrate that spectral structure governs reconstruction fidelity in a unified manner. In particular, non-Hermitian and open-system settings reveal that irreversibility emerges as a geometry- and scale-dependent feature of the spectrum, tied to both damping and eigenstate non-orthogonality. These results recast analytic continuation as a structured, scale-dependent filtering process with quantifiable and systematically accessible reconstruction limits, providing a unified perspective on the interplay between dynamics, spectral geometry, and information recovery.

The concept of metasurfaces was recently applied to the extreme ultraviolet (EUV) spectral regime, providing a new opportunity for transmissive focusing elements in a regime where materials are highly lossy. The realization of metalenses in the EUV, however, is challenging due to the optical losses and low refractive index contrast of available materials, as well as the larger-than-wavelength periodicity of metaatom arrays imposed by fabrication limits. In this paper, we propose alternative EUV metalens design strategies, including layout schemes and metaatom mapping rules. We demonstrate that the focusing efficiency can be roughly doubled compared with the simple square-lattice design of an EUV metalens purely by using an alternative semi-analytical design approach without reducing the metasurface's minimum feature size. The proposed strategies are generally applicable to metaoptics design for efficiency improvement when metaatoms are lossy or induce diffraction orders.

We investigate the emergent collective dynamics of LLM-based multi-agent systems on a 2D square lattice and present a model-agnostic statistical-physics method to disentangle social conformity from intrinsic bias, compute critical exponents, and probe the collective behavior and possible phase transitions of multi-agent systems. In our framework, each node of an $L\!\times\!L$ lattice hosts an identical LLM agent holding a binary state ($+1$/$-1$, mapped to yes/no) and updating it by querying the model conditioned on the four nearest-neighbor states. The sampler temperature $T$ serves as the sole control parameter. Across three open-weight models (llama3.1:8b, phi4-mini:3.8b, mistral:7b), we measure magnetization and susceptibility under a global-flip protocol designed to probe $\mathbb{Z}_2$ symmetry. All models display temperature-driven order-disorder crossovers and susceptibility peaks; finite-size scaling on even-$L$ lattices yields effective exponents $\gamma/\nu$ whose values are model-dependent, close to but incompatible with the 2D Ising universality class ($\gamma/\nu=7/4$). Our method enables the extraction of effective $\beta$-weighted couplings $\tilde{J}(T)$ and fields $\tilde{h}(T)$, which serve as a measure of social conformity and intrinsic bias. In the models we analyzed, we found that collective alignment is dominated by an intrinsic bias ($\tilde{h}\gg\tilde{J}$) rather than by cooperative neighbor coupling, producing field-driven crossovers instead of genuine phase transitions. These effective parameters vary qualitatively across models, providing compact collective-behavior fingerprints for LLM agents and a quantitative diagnostic for the reliability of multi-agent consensus and collective alignment.

Examining and controlling the interaction between semiconductor quantum qubits and their environment can boost semiconductor quantum technologies, which have many applications in table-top quantum computing hardware. Electron beams in electron microscopes have opened up a new avenue for the quantum-coherent probing of semiconductor excitations and strong-coupling effects. Here, I provide a brief overview of recent advancements in electron-beam probes for investigating quantum coherence in semiconductors and two-dimensional materials, complemented by my perspective on using electron beams to manipulate the entanglement and correlations between quantum systems.

Dynamics of exciton-polaritonic condensate under incoherent pumping is studied using the non-Markovian stochastic Gross-Pitaevskii equation with the pseudo-differential dispersion term. This term corresponds to the lower energy branch of polaritons. It is shown that an increasing of the pumping spot area leads to the appearance of various spatial structures whose properties depend on the duration of the dynamical memory. In the regime of short memory time, condensate can form an extended state that spans outside the pumping area. We conclude that onset of such extended states is related to the specific form of the dispersion term causing the “traffic jam” effect. The case of long memory time corresponds to enhanced condensate formation, when increasing of the pumping area leads to appearance of angular condensate structures which partially suppress emission of matter waves from the pumping area.

Reliable modeling and control of core density is essential for reactor-relevant magnetic confinement fusion operation, motivating cryogenic pellet injection as a primary fueling actuator and the need for predictive pellet source models in integrated modeling. Here we present an upgrade of the physics-based pellet code HPI2 in which the plasmoid release spatial step is determined self-consistently from ablation physics, $dx_{var}=v_{\mathrm{pel}}\,t_{\mathrm{exit}}$ (optionally rescaled to trade accuracy for computational cost), removing an ad-hoc discretization parameter and improving numerical robustness across injection conditions. The upgraded model is first validated in stand-alone against a high-field-side pellet-fueled, ohmic, WEST discharge (#58656) by comparing synthetic and measured interferometry line-integrated density increments, obtaining a mean error of $\sim 10\%$. We then perform full-radius, time-dependent integrated modeling validation by coupling the new HPI2 within the High Fidelity Pulse Simulator (HFPS) workflow (JINTRAC/IMAS), combining JETTO with SANCO for the impurity/radiation evolution and TGLF-SAT2 for the turbulent transport. The coupled simulations reproduce the main density rise and relaxation after pellet injection and the associated electron-temperature transient, while taking into account the strong influence of tungsten radiation in WEST, supporting the consistency of HPI2 as a predictive pellet particle source in integrated modeling frameworks. Ultimately, this validation study supports the use of pellet modeling tools in integrated modeling studies for larger devices such as ITER.

Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence of a principled out-of-distribution evaluation protocol at the atomic level. In this work, we propose a held-out evaluation protocol that clusters atomic environments by SOAP descriptors and computes metrics accounting only for cluster labels unseen during training. Following this procedure, we use 5$\times$5 cross-validation and Tukey's HSD to run a statistically rigorous comparison of E(3)-equivariant against non-equivariant, rotationally augmented models for predicting electron populations and multipoles of H, C, N, and O atoms. Building on our results, we introduce the Quantum Topological Neural Network (QT-Net), a rotationally augmented, non-equivariant graph neural network. We show that QT-Net can be used to infer properties of atoms in molecules from QM9 outside our training set, and that these inferred properties can yield improvement when used as input features for downstream molecular property prediction. To further validate the framework, molecular dipole moments computed from QT-Net's per-atom outputs recover the ground-truth values reported in QM9. We release all code and data, including a JAX implementation of QT-Net, to support the broader use of learned QTA properties as inductive biases for atomic-scale molecular machine learning.

Understanding the molecular mechanisms by which denaturants modulate protein structure remains a central challenge in protein biophysics. In this study, molecular dynamics (MD) simulations were employed to investigate the effects of urea on the structural stability of bovine serum albumin (BSA) in its F isoform at pH 3.7 across a broad range of urea concentrations, from pure aqueous solution (0 M) to a fully urea-solvated environment. The simulations reveal a concentration-dependent remodeling of the protein hydration shell. At low urea concentrations, backbone-water hydrogen bonds decrease by approximately 40 %t, accompanied by an approximately 45 % increase in protein-urea hydrogen bonds between 1 and 7 M urea, consistent with a competitive solvation process in which urea progressively replaces water molecules at the protein surface. As urea concentration increases, urea-urea self-association becomes increasingly significant, reducing the number of direct protein-urea contacts; concurrently, the remaining water molecules form protein-water hydrogen bonds more efficiently on a per-water-molecule basis, without implying a net increase in the absolute number of hydration water molecules. Despite these pronounced solvent rearrangements, the secondary structure of BSA remains largely preserved throughout the simulations. In contrast, local structural organization and global conformational features, particularly within Domain III, exhibit increased solvent exposure and enhanced conformational flexibility.

Nuclear magnetic resonance (NMR) spectroscopy provides an experimental readout of local chemical environments, but its use in molecular representation learning has been constrained by heterogeneous data and incomplete atom-level assignments. Here we construct complementary high-fidelity experimental and computational 13C NMR resources, which reveal a recurrent form of representational collapse: atoms that are equivalent in molecular topology can remain experimentally distinct in their real chemical environments, whereas explicit 3D descriptions are further limited by static conformations in dynamic regimes. To alleviate this bottleneck, we develop CLAIM (Contrastive Learning for Atom-to-molecule Inference of Molecular NMR), a framework that aligns efficient topological molecular inputs with atom-resolved NMR observables. Through hierarchical chemical priors and cross-level contrastive learning, CLAIM restores lost chemical resolution and markedly improves atom-level molecule-spectrum retrieval. CLAIM remains robust in flexible and tautomeric systems for 13C NMR prediction, improves stereoisomer discrimination without explicit 3D modelling, and transfers to broader molecular property tasks including ADMET prediction and fluorescence estimation. These results establish physically grounded spectral alignment as an effective strategy for alleviating chemical-environment collapse and for guiding experimentally grounded molecular representation learning.