Papers

In 1928, Chapman generalised Einstein's theory of diffusion for non-uniform fluids to show the presence of a non-Fickian diffusion current, which he considered important in thermodiffusion (Ludwig-Soret effect). In 1941, Kiyosi Itô proposed the formal methods of stochastic calculus in the presence of spatially dependent diffusion, yielding the same non-Fickian diffusion current as shown by Chapman. The phenomenon of thermodiffusion and thermophoresis happens in the presence of a temperature gradient, which makes diffusion space-dependent. The role of solvation forces in thermophoresis will only be clearer once that of diffusion is understood properly. In this paper, we investigate the importance of Chapman's non-Fickian diffusion current on the thermophoretic motion of colloidal particles in water (with weak salt concentration). We show that all the general features of variations of the Soret coefficient $S_T$ with temperature can be captured using Chapman's non-Fickian diffusion current. We compare our theoretical results with experimental plots of the Soret coefficients for three polypeptides in aqueous solution: Lysozyme, BLGA, and Poly-L-Lysine, and find a strong match. We emphasise that, in addition to the yet-to-be-understood details of solvation forces, Chapman's non-Fickian diffusion current is an indispensable element that needs to be taken into account for a complete understanding of thermophoresis and thermodiffusion.

Classical mechanics unfolds within absolute time and Euclidean space, yet our knowledge of where events occur, when they occur, and how motion evolves is inherently uncertain. The special Galilean group provides a natural setting for describing classical spacetime, combining absolute time, Euclidean space, and inertial motion within a single Lie group structure. Although this framework is well known, representing and propagating uncertainty on the group has received comparatively little attention. In this work, we bring together existing results on the structure of the Galilean group and use this unified framework to express uncertainty directly on the group manifold. A main contribution is a compact, closed-form expression for the Galilean group Jacobian, which enables principled uncertainty propagation when composing Galilean transformations. We show that uncertainty in spatial position and orientation, temporal displacement, and inertial motion are intrinsically coupled through the underlying group structure. To illustrate the usefulness of the Galilean framework, we consider the problem of estimating a time-varying transformation between inertial frames from noisy observations collected at distinct instants in time. We show that performing estimation directly on the Galilean group yields substantially more statistically consistent estimates than formulations that treat time independently. Together, these results provide a geometric foundation for reasoning about uncertainty in space, time, and motion in classical mechanics, navigation, and robotics.

Despite three decades of experimental study, optical gain in colloidal quantum dots still lacks a microscopic theory capable of explaining gain thresholds approaching one exciton per dot, their size dependence, or the anomalously small effective stimulated-emission cross sections observed across materials. Existing descriptions treat quantum dots as effective two-level systems comprised of an exciton and a biexciton, attributing gain thresholds to biexciton Auger recombination. This assumption is inconsistent with state-resolved optical pumping experiments and basic spectroscopic constraints. Here we present a microscopic theory of optical gain explicitly anchored in the Einstein relations governing absorption and stimulated emission. Within this framework, gain is determined by a spectral balance between stimulated emission from single excitons and excited-state absorption into biexcitonic manifolds, rather than by biexciton lifetimes. Using a spin-boson description of excitons coupled to a lattice bath, we show that gain thresholds and effective gain cross sections are controlled by the interplay of biexciton stabilization and exciton-lattice dressing. The theory unifies disparate materials by quantitatively explaining all longstanding gain phenomenology in CdSe quantum dots and predicts a continuous crossover to effective four-level, near-thresholdless gain in dynamically disordered lattices such as perovskite quantum dots.

Standard reduced models often fail to adequately describe the complex geometric response of tokamak plasmas to strong toroidal rotation. In this work, we present VEQ-R, a computationally efficient spectral solver designed to calculate fixed-boundary equilibria with arbitrary toroidal flow. In contrast to computationally intensive grid-based codes, our model employs a 12-parameter shifted Chebyshev spectral expansion to explicitly resolve radial variations in high-order shaping profiles–such as dynamic elongation and triangularity. This capability allows the solver to accurately capture differential flux surface distortions (non-rigid effects) even in challenging sonic regimes ($M \sim 1.0$). By synergizing this compact variational formulation with a novel “Matrix-Kernel” acceleration technique, we transform the problem into pre-computed algebraic matrix operations. This approach achieves convergence in approximately 5 ms, maintaining exceptional geometric fidelity compared to high-resolution benchmarks while balancing speed and accuracy. Our analysis reveals that rotation-induced flux compression leads to a monotonic decrease in the core safety factor $q_0$, pushing it dangerously close to unity–a structural deformation mechanism effectively captured by this approximate yet robust solver.

Integrated photonics provides a powerful approach for developing compact, stable and scalable architectures for the generation, manipulation and detection of quantum states of light. To this end, several material platforms are being developed in parallel, each providing its specific assets, and hybridization techniques to combine their strengths are now possible. This review focuses on AlGaAs, a III-V semiconductor platform combining a mature fabrication technology, direct band-gap compliant with electrical injection, low-loss operation, large electro-optic effect, and compatibility with superconducting detectors for on-chip detection. We detail recent implementations of room-temperature sources of quantum light based on the high second- and third-order optical nonlinearities of the material, as well as photonic circuits embedding various functionalities ranging from polarizing beamsplitters to Mach-Zehnder interferometers, modulators and tunable filters. We then present several realizations of quantum state engineering enabled by these recent advances and discuss open perspectives and remaining challenges in the field of integrated quantum photonics with AlGaAs.

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Quantum-state-controlled reactivity is a long-standing goal in the field of physical chemistry. In this work, we explore the vibrational-state-dependent behavior of the ion-molecule reaction between O2+ in distinct vibrational states and two isomers of C3H4, allene (H2C3H2) and propyne (H3C3H). While most products are formed regardless of the vibrational state of O2+, the branching ratios are influenced by vibrational excitation, and a new product, C2O+, appears exclusively in the excited-state reactions. This selective formation of C2O+ demonstrates that vibrational excitation can effectively activate a reaction pathway, providing direct evidence of quantum-state control in reactivity. These results represent an important step towards the goal of quantum-state-controlled chemistry in molecular systems.

Projection-based Reduced Order Models (ROMs) are often deployed as static surrogates, which limits their practical utility once a system leaves the training manifold. We formalize and study adaptive non-intrusive ROMs that update both the latent subspace and the reduced dynamics online. Building on ideas from static non-intrusive ROMs, specifically, Operator Inference (OpInf) and the recently-introduced Non-intrusive Trajectory-based optimization of Reduced-Order Models (NiTROM), we propose three formulations: Adaptive OpInf (sequential basis/operator refits), Adaptive NiTROM (joint Riemannian optimization of encoder/decoder and polynomial dynamics), and a hybrid that initializes NiTROM with an OpInf update. We describe the online data window, adaptation window, and computational budget, and analyze cost scaling. On a transiently perturbed lid-driven cavity flow, static Galerkin/OpInf/NiTROM drift or destabilize when forecasting beyond training. In contrast, Adaptive OpInf robustly suppresses amplitude drift with modest cost; Adaptive NiTROM is shown to attain near-exact energy tracking under frequent updates but is sensitive to its initialization and optimization depth; the hybrid is most reliable under regime changes and minimal offline data, yielding physically coherent fields and bounded energy. We argue that predictive claims for ROMs must be cost-aware and transparent, with clear separation of training/adaptation/deployment regimes and explicit reporting of online budgets and full-order model queries. This work provides a practical template for building self-correcting, non-intrusive ROMs that remain effective as the dynamics evolve well beyond the initial manifold.

Wall-modeled large-eddy simulation (WMLES) is performed for flow over a wing with a focus on documenting grid resolution requirements to predict both the laminar and turbulent regions accurately. Flow over a spanwise extruded NACA0012 airfoil at 0-degree angle of attack and freestream chord-based Reynolds numbers of 3 million is simulated using an unstructured-grid finite-volume solver. An equilibrium wall function with the first off-wall grid point as the exchange location is used. Two scenarios are simulated wherein either the entire airfoil surface, or only a portion of it, is assumed turbulent based on linear stability calculations. For the latter scenario, the regular no-slip wall boundary condition is imposed in laminar regions. Using the same grid that was employed in the fully turbulent case, WMLES captured the skin friction in the turbulent region close to the RANS results. However, the skin friction is not well captured in the laminar region, due to far few grid points inside the boundary layer. When the near-wall grid was refined in the wall normal direction, the laminar region was captured accurately, but the turbulent region was not resolved well because the first off-wall point moved below the buffer-layer. To reconcile differing resolution requirements in the laminar and turbulent regions, a grid was generated based on the varying boundary layer thickness estimated from a precursor RANS simulation with a specified transition location. This grid produced improved results in the laminar region but resulted in a delayed transition location. Unsteady disturbances with the most amplified frequencies were introduced upstream of the neutral point. The transition process in the laminar region and the skin friction in the turbulent region were then captured satisfactorily.

Hybrid photon counting detectors (HPCDs) have unlocked new capabilities for x-ray-based measurements at synchrotrons around the world in the last 30 years. By leveraging independently optimized sensor and readout layers, they offer high quantum efficiency ($> 80 \%$), ultra-low dark counts, sub-pixel point-spread function, and high count rates ($> 10^{6}$ counts per pixel per second). Furthermore, their small pixel size and large active area endow them with excellent coverage and resolution for both real-space and reciprocal space imaging. Here, we demonstrate that HPCDs are also well-suited for laboratory-based nanoscale x-ray tomography (nano-xCT). We perform nano-xCT on an integrated circuit fabricated at the 130-nm node and produce a 3D reconstruction with 40 times more photons collected 20 times faster than in this group's previous work, for an overall speedup of 800$\times$. We review the technical considerations of using an HPCD for tabletop tomography. We quantify our reconstruction image quality using well-established metrics, including the modulation transfer function (MTF), Fourier shell correlation (FSC), and contrast-to-noise (CNR), to validate our choice of experimental parameters that provide sufficient resolution and imaging speed. Using these metrics, we determine that even under current experimental conditions, 160 nm wiring features are reconstructed at 75-80 nm spatial resolution.

Experience shows that disciplinary science cannot describe life without contradictions. We show that one of the fundamental reasons is the disciplinarity itself: the disciplines deal with a limited set of quantities. This way some 'outlaw' quantities are not measured and the discipline does not have laws about them. All laws of science are based on approximations and the approximations must be different for inanimate and life sciences. Studying ions is special because ions belong simultaneously to thermodynamics and electricity, but neither of those disciplines alone can describe biological processes. One needs a cross-disciplinary discussion and maybe a new scientific discipline. We provide a method for handling the different interaction speeds characterizing the ion transport. Electrolytes in living matter introduce further peculiarities with their closed volumes, internal structure, and slow processes. Their meticulous analysis led to the appropriate approximations, leading to the correct scientific description. As a success story, the cross-disciplinary theory of neuronal operation has been developed.

The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of interactions per bunch crossing in recent years. To address this challenge, a series of machine-learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022$-$2023, corresponding to an integrated luminosity of 62 fb$^{-1}$.

Molecular polaritons have drawn great interest in recent years as a possible avenue for providing optical control over chemical dynamics. A central challenge in the field is to identify physical phenomena that require a quantum rather than a classical treatment of electrodynamics. In this work, we use our recently developed mean-field quantum (mfq) and full-quantum (fq) real-time nuclear–electronic orbital (RT-NEO) time-dependent density functional theory methods to simulate polaritonic dynamics for a molecule under vibrational strong coupling when a quantized cavity mode is initialized in a Fock state rather than a coherent state. Our previous work showed that a coherent state initial condition for the cavity mode leads to polariton formation for both the mfq-RT-NEO and fq-RT-NEO methods. Herein, we show that the mfq-RT-NEO method, which does not allow light–matter entanglement, does not predict polariton formation for a Fock state initial condition. Similar to the mfq-RT-NEO method, the fq-RT-NEO method does not predict oscillations of the cavity mode coordinate and molecular dipole operator expectation values for a Fock state initial condition. However, the fq-RT-NEO method does predict oscillations of the expectation values of even powers of these operators as well as light–matter entanglement, implicating polariton formation with a Fock state initial condition. All these observations can be explained with model systems. These results suggest that using a quantized cavity mode initial condition that does not have a direct analogy to an initial condition in classical electrodynamics can lead to physical phenomena that can only be described by a quantum treatment of the cavity mode.

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored.

Maxwell's demon elucidates the value of information in thermodynamics, using measurement and feedback: he evolves an equilibrated gas into a nonequilibrium state, from which one might extract work. The demon can evolve the system farther from equilibrium, on average, if the particles obey Bose-Einstein statistics than if they are distinguishable. We experimentally support this decade-and-a-half-old prediction by comparing indistinguishable with distinguishable photons. We use a fully programmable linear-optics platform, whose local photon statistics were shown recently to behave thermally. Our demon nondestructively measures the number of photons in a subset of the modes. Guided by the outcome, he conditionally interchanges the measured and unmeasured modes. This interchange creates a positive temperature difference between a mode in a particular subset and a mode in the other. The temperature difference is greater, on average, if the photons are indistinguishable. This result bolsters a long-standing prediction about the interplay among thermodynamics, information, and quantum particle statistics. Additionally, this work suggests a thermodynamic means of weakly validating boson-sampling platforms.

Modern experimental techniques can generate magnetic fields of the form H(t) = H0 z-hat + H1 [x-hat cos({\omega}t) + y-hat sin({\omega}t)], at frequencies within an order of magnitude of the nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) frequencies, {\omega}n0 and {\omega}e0, respectively, when acting on atoms or molecules. We derive simple closed-form expressions for the exact nuclear- and electronic-spin wave functions that enable controlled transitions between entangled states, allowing an atom or molecule to function as a quantum computer. These solutions also enable precise NMR or EPR measurements of nuclear moments in atoms and molecules. We present examples relevant to measurements of the nuclear moments of 14N, 7Li, and 133Cs. Because existing hyperfine measurements of the lowest three nuclear moments of 133Cs are mutually inconsistent, the proposed NMR/EPR experiments provide a route to measuring all seven of its nuclear moments with high precision.

We demonstrate that a background rotation significantly catalyzes the chiral magnetovortical instability in chiral magnetohydrodynamics. The rotation splits the linearly polarized Alfven wave into two circularly polarized magneto-Coriolis waves, one of which exhibits a lower frequency than the original Alfven wave. We find that this low-frequency magneto-Coriolis wave is always unstable in the presence of even a weak chiral vortical effect. This instability may enable new dynamo mechanism applicable to various rotating chiral plasmas.

Scale invariance has received very little attention in physics. Nevertheless, it provides a natural conceptual foundation for a relational understanding of the universe, where absolute size loses meaning and only dimensionless ratios retain physical significance. We formalize this idea through the $N$-body problem, introducing a scale-invariant function–the variety, $V$–built from the square root of the center-of-mass moment of inertia and the Newtonian potential. Critical points of $V$, known as central configurations, correspond to special particle arrangements that preserve their shape under homothetic collapse or expansion. Numerical exploration of these critical points reveals that even slight deviations from the absolute minimum of $V$, which corresponds to a remarkably uniform configuration, lead to the spontaneous formation of filaments, loops, voids and other patterns reminiscent of the cosmic web. This behavior is a consequence of the intrinsic structure of shape space–the space of configurations modulo translations, rotations and dilatations–in which regions of higher variety act as attractors. Our results suggest that scale-invariant dynamics not only captures the relational nature of physical laws but also naturally generates organized patterns, offering a novel perspective on the formation of cosmic structures and on the emergence of a gravitational arrow of time from scale-invariant, relational dynamics.

Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as an alternative, learning surrogates of molecular distributions either from data or through interaction with energy models. While these methods enable efficient sampling, their transferability across molecular systems is often limited. In this work, we show that incorporating auxiliary sources of information can improve the data efficiency and generalization of transferable implicit transfer operators (TITO) for molecular dynamics. We find that coarse-grained TITO models are substantially more data-efficient than Boltzmann Emulators, and that incorporating protein language model (pLM) embeddings further improves out-of-distribution generalization. Our approach, PLaTITO, achieves state-of-the-art performance on equilibrium sampling benchmarks for out-of-distribution protein systems, including fast-folding proteins. We further study the impact of additional conditioning signals such as structural embeddings, temperature, and large-language-model-derived embeddings on model performance.

We formalize a generalized form of the Pareto principle - “fraction $p$ of inputs yields fraction $1-p$ of outputs” - as a property of non-negative gain densities $\ell \in L^1([0,1])$, working with the decreasing rearrangement to obtain a unique characterization. For probability distributions, the resulting $p$ coincides with $1 - k_F$, where $k_F$ is the Kolkata index of the corresponding Lorenz curve. Within this framework we analyze both constructed gain densities and commonly encountered distribution families. We derive closed-form expressions for $p$ for truncated power-law, exponential, and normal distribution families. Combining these with estimates of the truncation parameter as a function of sample size $N$, we predict that datasets of size $N \in [10^2, 10^5]$ from exponential and normal families concentrate $p$ near $[0.15, 0.26]$ and $[0.20, 0.29]$ - values close to the canonical 0.2/0.8-rule, and strictly below the saturation $k \approx 0.865$ conjectured earlier by Ghosh and Chakrabarti. We discuss the implications of the structural ubiquity of Pareto-type imbalances for their use as prescriptive targets.

The K-condition introduced by Shizuta and Kawashima provides a sufficient criterion for the global existence of smooth solutions to dissipative hyperbolic systems. For genuinely nonlinear characteristic fields, a weaker K-condition becomes necessary, although not sufficient. In this paper, we analyze this weaker K-condition through the study of acceleration waves propagating in an equilibrium state. We investigate two classes of hyperbolic models: one describing viscoelasticity with linear dissipation, and the other non-Newtonian fluids asymptotically converging to a power-law behavior. For viscoelastic models, the weaker K-condition is always satisfied and acceleration waves remain bounded. For non-Newtonian fluids, the validity of the condition depends on the power-law index $m$: it holds for Newtonian fluids ($m=1$), is violated for shear-thinning fluids ($m<1$), and leads to an instantaneous regularization of acceleration waves for shear-thickening fluids ($m>1$).

Many biological processes involve numerous coupled degrees of freedom, yet free-energy estimation is often restricted to one-dimensional profiles to mitigate the high computational cost of multidimensional sampling. In this work, we extend Fokker–Planck Score Learning (FPSL) to efficiently reconstruct two-dimensional free-energy landscapes from non-equilibrium molecular dynamics simulations using different types of collective variables. We show that explicitly modeling orthogonal degrees of freedom reveals insights hidden in one-dimensional projections at negligible computational overhead. Additionally, exploiting symmetries in the underlying landscape enhances reconstruction accuracy, while regularization techniques ensure numerical robustness in sparsely sampled regions. We validate our approach on three distinct systems: the conformational dynamics of alanine dipeptide, as well as coarse-grained and all-atom models of solute permeation through lipid bilayers. We demonstrate that, because FPSL learns a smooth score function rather than histogram-based densities, it overcomes the exponential scaling of grid-based methods, establishing it as a data-efficient and scalable tool for multidimensional free-energy estimation.

We study the training and performance of physics-informed learning for initial and boundary value problems (IBVP) with physics-informed neural networks (PINNs) from a statistical learning perspective. Specifically, we restrict ourselves to parameterizations with hard initial and boundary condition constraints and reformulate the problem of estimating PINN parameters as a statistical learning problem. From this perspective, the physics penalty on the IBVP residuals can be better understood not as a regularizing term bus as an infinite source of indirect data, and the learning process as fitting the PINN distribution of residuals $p(y \mid x, t, w) q(x, t) $ to the true data-generating distribution $\delta(0) q(x, t)$ by minimizing the Kullback-Leibler divergence between the true and PINN distributions. Furthermore, this analysis show that physics-informed learning with PINNs is a singular learning problem, and we employ singular learning theory tools, namely the so-called Local Learning Coefficient (Lau et al., 2025) to analyze the estimates of PINN parameters obtained via stochastic optimization for a heat equation IBVP. Finally, we discuss implications of this analysis on the quantification of predictive uncertainty of PINNs and the extrapolation capacity of PINNs.

Coherent virtual absorption refers to time-limited storage of optical energy in lossless configurations due to excitation of a complex zero frequency through proper temporal engineering of the incident wave. Given the dynamics underlying the effect and the storage-release mechanism occurring for finite excitation pulses, studying and understanding the associated time dynamics are crucial for enabling future applications. In this work, we carefully investigate this phenomenon in symmetric and asymmetric geometries, shedding light on practical considerations in situations when a closed-form analytical solution is not readily available. Combinations of time domain analysis and spectral filtering are used to enable systematic analysis of these structures. Our approach can be generalized to more complex structures, including multilayered and inhomogeneous cases, providing new opportunities for optimized energy storage and advanced sensing applications utilizing complex-frequency dynamics in lossless designs.

Anisotropic, non-thermal, and multi-temperature distributed particle momenta are commonly observed in collisionless space plasmas, such as the solar wind. Using Liouville's theorem, we argue that anisotropic compression or expansion of the plasma, followed by a relaxation of the resulting anisotropic stress must lead to non-equilibrium states that exhibit either anisotropic, non-thermal distribution functions, different electron and ion temperatures, or a combination of these effects. We present arguments showing that a plasma in thermal equilibrium undergoing anisotropic compression or expansion cannot return to thermal equilibrium in the absence of particle collisions. Since most astrophysical plasmas are practically collisionless and experience significant anisotropic compression or expansion, we expect anisotropic, non-thermal, and multi-temperature particle distributions to be ubiquitous, in agreement with solar wind measurements.

Binding energies (BEs) of adsorbates on interstellar dust grains critically control adsorption, desorption, diffusion, and surface reactivity, and therefore strongly influence astrochemical models of star- and planet-forming regions. While recent computational studies increasingly report full distributions of BEs rather than single representative values, these distributions are typically derived for either bare grain surfaces or thick water-ice mantles. In this work, we bridge these regimes by systematically investigating the BE distributions of water on partially and fully ice-covered dust grain surfaces. We employ machine-learning interatomic potentials (MLIPs) based on graph neural networks to model water adsorption on graphene and on the Mg-terminated (010) surface of forsterite, representing carbonaceous and silicate grains, respectively. The models enable extensive sampling of adsorption sites on water clusters, monolayers, and bilayers generated under both crystalline (thermally processed) and amorphous (low-temperature) growth conditions. At submonolayer coverage, the chemical nature of the underlying grain strongly affects both ice morphology and binding energies, with Mg-O interactions on silicate surfaces producing particularly deep binding sites. From monolayer coverage onward, adsorption on both substrates is dominated by hydrogen bonding within the ice, reducing the influence of the grain material. Across all coverages, amorphous ice structures systematically shift the BE distributions toward stronger binding compared to crystalline ice, introducing highly stable defect and pocket sites. These results demonstrate that BE distributions in the submonolayer to few-layer ice regime are broad and highly surface dependent, and they provide physically motivated input for next-generation astrochemical models incorporating surface heterogeneity.

Isomerization in molecular systems almost invariably occurs through 3-dimensional motion due to the nature of chemical bonding. Pseudorotation is an unusual type of isomerization that occurs in some high symmetry systems that gives the appearance of rigid-body rotation yet only involves atom rearrangements. This paper demonstrates that pseudorotation occurs in 2-dimensions in an optical matter (OM) system of metal nanoparticle constituents. The difference in dimensionality of the dynamics arises from the electrodynamic field-interference nature of optical binding vs. quantum mechanical bonding in polyatomic molecules. The 8-nanoparticle OM "kite" structure we study in experiments and simulations has D2 (D2h) symmetry and a D4 symmetric transition state. The mechanism for pseudorotation involves correlated motion of all 8 nanoparticles with smooth (continuous) evolution of their interactions and without particles jumping in or out of the OM array. While the OM kite structure only occurs with 10% probability vs. other OM isomers, its rate of pseudorotation is rapid relative to transitions to other structural isomers (e.g., "teardrop"). The other isomers have structures that lie on a trigonal lattice with inter-particle separations at distances that enhance field interference and induced polarizations. Even though the kite isomer has inter-particle separations that would manifest destructive interference on a particle pair (i.e., 2-body) basis, the kite structure is the slowest to rearrange into any other isomer. We show that the unusual structure and dynamics of the kite optical matter system result from N-body interactions and forces demonstrating that N-body effects are important in this class of active matter and presumably more generally.

Agent-based models typically treat systems in isolation, discarding environmental coupling as either computationally prohibitive or dynamically irrelevant. We demonstrate that this neglect misses essential physics: environmental degrees of freedom create memory effects that fundamentally alter system dynamics. By systematically transforming linear update rules into exact generalized Langevin equations, we show that unobserved environmental agents manifest as memory kernels whose timescales and coupling strengths are determined by the environmental interaction spectrum. Network topology shapes this memory structure in distinct ways: small-world rewiring drives dynamics toward a single dominant relaxation mode, while fragmented environments sustain multiple persistent modes corresponding to isolated subpopulations. We apply this framework to covert influence operations where adversaries manipulate target populations exclusively via environmental intermediaries. The steady-state response admits a random-walk interpretation through hitting probabilities, revealing how zealot opinions diffuse through the environment to shift system agent opinions toward the zealot mean - even when zealots never directly contact targets.

The convergence of self-consistent field equations in mean-field nuclear-electronic orbital methods strongly depends on the choice of initial guesses for quantum nuclei. Although several such guesses have been proposed in the literature, a systematic comparison of their performance as well as attempts of constructing novel approximations based on model tasks of quantum mechanics were not reported to date. In this work, we address both issues by introducing novel nuclear initial guesses derived from the analytical solutions of the three-dimensional quantum harmonic oscillator and benchmarking them against existing approaches. We demonstrate that the isotropic variant of our guess outperforms existing approximations in nuclear-electronic orbital density functional theory calculations employing a simultaneous self-consistent field convergence protocol. Although our guess requires the computation of partial Hessians, we demonstrate that these can be evaluated with low-cost methods without affecting the accuracy of resulting protonic density matrices. Our results demonstrate that the proposed guess is robust and efficient and could provide a route to improved convergence in mean-field nuclear-electronic orbital computations.

Multiscale simulations utilizing high-fidelity, microscopic Monte Carlo models to provide the nonlinear response for continuum models can easily become computationally intractable. Surrogate models for the high-fidelity Monte Carlo models can overcome this but come with some challenges. One such challenges arise by the sampling noise in the underlying Monte Carlo data, which leads to uncontrolled errors possibly corrupting the surrogate even though it would be highly accurate in the case of noise-free data. Another challenge arises by the 'curse of dimensionality' when the response depends on many macro-variables. These points are addressed by a novel noise-balanced sparse grids interpolation approach which, in a quasi-optimal fashion, controls the amount of Monte Carlo sampling for each data point. The approach is complemented by a multilevel on-the-fly construction during the multiscale simulation. Besides its efficiency, a particularly appealing feature is the ease of use of the approach with only a single hyperparameter controlling the whole surrogate construction - from the surrogate's accuracy with guaranteed convergence to which data needs to be created with which accuracy. The approach is demonstrated on challenging examples from heterogeneous catalysis, coupling microscopic kinetic Monte Carlo models into macroscopic reactor simulations.