Papers

We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm–Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchange-space truncation, omission of hydrogen atoms from the auxiliary basis, and a host memory assisted Davidson solver. On the EXTEST42 benchmark set, a conservative 40 eV exchange cutoff yields excitation-energy errors relative to standard TDA of about 0.03–0.05 eV for low-lying states. For systems of 300 to 3000 atoms, we demonstrate that TDA-risp calculations of 15 low-lying excited states with $\omega$B97XD/def2-SVP complete on a single A100 GPU with wall times ranging from minutes to hours. These results position GPU-TDDFT-risp as a practical route toward excited-state calculations for large organic and biomolecular systems with thousands of atoms.

The Low-Gain Avalanche Diode (LGAD) is a semiconductor detector capable of achieving excellent timing resolution (~20 ps) for minimum ionizing particles (MIPs). To realize a pixelated detector with both high timing precision and spatial resolution, we have been developing Capacitive-Coupled LGADs (ACLGADs) for future collider experiments, such as the latter phase of the High-Luminosity LHC. We have successfully fabricated a pixelated ACLGAD (ACLGADpix) with a 100 $\mu$m %\times% 100 $\mu$m pixel pitch, maintaining uniform timing performance across the active area. In this presentation, we will report recent measurement results from ACLGADpix prototypes using beta rays, an infrared laser, and a 3 GeV electron beam. We will also discuss potential readout electronics for future collider applications.

The high frequency of satellite launches, particularly over the last few years, has been a subject of significant concern, particularly relating to the future of observational astronomy, the stability of low Earth orbits, and environmental impacts. We call attention to the insufficiently-addressed silver lining of this looming satellite cloud. If the high rates of satellites continue as we model, we can expect the solar flux received by the Earth to significantly decrease in the relatively near future. We address how this decrease in flux could provide a solution for another major problem, anthropogenic climate change. This would allow us to solve one problem with another problem as early as late March 2031.

Nonequilibrium heat transport and quantum thermodynamics in light-matter interacting systems have received increasing attention. Quantum thermal devices, e.g., heat valve and head diode, have been realized. Recently, it has been discovered that the anisotropic light-matter interactions can greatly modify the eigenvalues and eigenvectors of hybrid quantum systems, leading to nontrivial quantum phase transitions, quantum metrology, and nonclassicality of photons. To explore the influences of anisotropic light-matter interactions on quantum transport, we investigate heat flow in the nonequilibrium anisotropic Dicke model. In this model, an ensemble of qubits collectively interacts with an anisotropic photon field. Each component interacts with bosonic thermal reservoirs. Quantum dressed master equation (DME) is included to properly study dissipative dynamics of the anisotropic Dicke model. Within the eigenbasis of the reduced anisotropic Dicke system, strong qubit-photon couplings can be properly handled. Our results demonstrate that anisotropic qubit-photon interactions are crucial for modulating steady-state heat flow. In particular, it is found that under strong coupling the heat flow is dramatically suppressed by a large anisotropic qubit-photon factor. While under moderate coupling, the anisotropic qubit-photon interactions enhance the heat flow. Moreover, the increase in the number of qubits amplifies the flow characteristics, with the peaks increasing and the valleys decreasing. Besides, we derive two analytical expressions of heat flows in thermodynamic limit approximation with limiting anisotropic factors. These heat currents exhibit the cotunneling heat transport pictures. They also serve as the upper boundaries for the heat flows in the finite-size anisotropic Dicke model. We also analyze the thermal rectification effect in the anisotropic Dicke model.

Chemical reaction sampling critically depends on collective variables (CVs) that capture the slow degrees of freedom governing reactive transformations. However, existing reaction CVs are often defined in geometric space or learned in a system-specific manner, which limits their transferability and leaves open the more fundamental question of how reaction progress should be represented. From a physical perspective, chemical reactions are defined by electron redistribution. Here, we introduce a charge-space electronic collective variable that describes the electronic component of reaction progress in a common linear form based on atomic charges. To enable its use in enhanced sampling, atomic charges and the corresponding CV gradients are provided by a neural-network model trained on QM/MM data within an iterative sampling-training workflow. Across multiple reactions in aqueous and enzymatic environments, we show that this electronic CV can be constructed in a common charge-space form, with the corresponding coefficients assigned in a simple manner from charge differences between relevant states. Our simulations further show that reaction progress generally involves coupled electronic and conformational components, and that the same framework can also be extended to restrain side reactions. These findings support charge-based electronic CVs as a physically motivated framework for describing the electronic component of chemical reaction progress with reduced reliance on handcrafted geometric descriptors.

Photosynthetic antenna complexes achieve high quantum efficiency through exciton transport in coupled pigment networks. Conventional Frenkel-exciton models treat each chromophore as a structureless site and neglect internal electronic degrees of freedom that can influence coherence and delocalization. Here we develop an extended excitonic network model that preserves the pigment-pigment coupling topology while introducing tunable intrachromophoric electronic mixing within the single-excitation manifold. Using a Lindblad open-quantum-system framework, we quantify coherence, delocalization, and trapping efficiency across parameter space. We show that intrachromophoric mixing plays a time-dependent role: enhanced mixing on the antenna side promotes short-time coherent delocalization and improves excitation injection, whereas excessive mixing near the trapping site induces persistent delocalization and suppresses transfer efficiency. Simulated two-dimensional electronic spectra reveal enhanced cross peaks and systematic blue shifts, providing spectroscopic signatures of coherence-modulated transport. These results establish a microscopic connection between internal electronic structure and quantum transport performance in excitonic networks.

Nonlinear computation is essential for various information processing tasks. Optical implementations are attractive because passive light propagation can manipulate high-dimensional signals with extreme throughput and parallelism; yet realizing nonlinear mappings in optical hardware remains challenging due to the weak nonlinearity of optical materials and the large intensities required to induce nonlinear interactions. This challenge is further amplified in many systems that operate with incoherent illumination, motivating a coherence-aware framework for scalable optical nonlinear processing. Here, we show that linear optical systems, in particular, optimized diffractive processors comprising passive surfaces, can perform large-scale nonlinear function approximation under spatially incoherent or partially coherent illumination, when preceded by intensity-only input encoding. We quantify how the accuracy of the nonlinear function approximation varies with the degree of parallelism, the number of diffractive layers, and the number of trainable diffractive features. Numerical results demonstrate snapshot computation of up to one million distinct nonlinear functions in a single forward pass through a diffractive processor, with the function outputs spatially multiplexed and read out using densely packed detectors at the output. We further provide a proof-of-concept experimental demonstration under incoherent illumination from a liquid crystal display (LCD), enabled by a model-free in situ learning strategy that jointly optimizes the diffractive profile and detector readout geometry in the presence of hardware imperfections and misalignments. Our findings establish diffractive processors as a massively parallel universal function approximator for both spatially incoherent and partially coherent illumination.

We show that optical forces perpendicular to the direction of the incident light, generated on structures with asymmetric optical scattering, can manipulate longitudinal elastic waves traveling in that same perpendicular direction. When the radiation pressure acts unidirectionally, reciprocity and hence Newton's Third Law are effectively broken. As a result, the waves grow exponentially with position, an instance of the non-Hermitian skin effect. The effect can be enhanced by orders of magnitude to measurable scales in optically dispersive nanostructured membranes. These findings are particularly relevant in the context of lightsails, spacecraft propelled by radiation pressure from high-power lasers. Our discovery showcases a new interaction between radiation pressure and elastic waves, which taps into the rich field of non-Hermitian physics.

Empirical observations together with theoretical analyses are being used to argue that the classical phenomenon of gravitational redshift – namely, the redshift of light in a static gravitational potential – may be in tension with the Heisenberg uncertainty principle. In particular, in the Pound-Rebka experiment, the emitter-absorber pair interact with each other by exchanging a Mossbauer photon, a process that is fully describable within quantum mechanics but how the photon interacts with the spacetime metric remains unclear. Since the uncertainty principle is incompatible with local realism, as per general relativity, we propose to study continuous-variable photonic entanglement within the Einstein-Podolsky-Rosen (EPR) framework in a weak gravitational field. We outline a thought experiment, realizable on the surface of the Earth, that could shed further light on this problematic seamline between general relativity and quantum mechanics in the weak-field regime.

Many natural systems posses, and can transition between, multiple alternative states. For example, a climate “tipping element” is a climate component that can transition to an alternative steady state due to an external perturbation such as global warming. Despite the potential impact, existence of alternate states in realistic, complex simulations (e.g. climate models) remain poorly understood. Arguably a reason for this is the lack of applicable methodology that explicitly targets finite yet high-dimensional datasets. In this work we utilize recent progress in computational nonlinear dynamics to formulate a workflow that analyses potentially multi-state simulation data and decides algorithmically what are the alternate states contained within, if any are clearly distinguishable. The framework undergoes an optimization routine that showcases which observables in the data best differentiate the alternate states, and which ones do not differentiate at all, which could be used to guide monitoring and early-warning for multistable components in climate or ecosystems. Finally, once the alternate states have been found, we define an indicator called “intermingledness”. It quantifies differences and similarities between alternate states, as well as for their basins of attraction (if applicable), across various diagnostic variables. We analyse and present results using three diverse climate datasets: Atlantic ocean circulation, atmospheric midlatitude flow, and habitability of exoplanets. The method is not exclusive to climatic data, but applicable to a variety of cases, including complex networks such as power grids or biological networks. We also provide easy-to-use open source code for applying the workflow to new data.

Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications. In recent years deep learning (DL) has been used to construct data-driven fluid-dynamic surrogate models. In this work we consider a different learning paradigm and embrace generative modelling as a framework for constructing scalable fluid-dynamics surrogate models. We introduce FluidFlow, a generative model based on conditional flow-matching, a recent alternative to diffusion models that learns deterministic transport maps between noise and data distributions. FluidFlow is specifically designed to operate directly on CFD data defined on both structured and unstructured meshes alike, without the needs to perform any mesh interpolation pre-processing and preserving geometric fidelity. We assess the capabilities of FluidFlow using two different core neural network architectures, a U-Net and diffusion transformer (DiT), and condition their learning on physically meaningful parameters. The methodology is validated on two benchmark problems of increasing complexity: prediction of pressure coefficients along an airfoil boundary across different operating conditions, and prediction of pressure and friction coefficients over a full three-dimensional aircraft geometry discretized on a large unstructured mesh. In both cases, FluidFlow outperform strong multilayer perceptron baselines, achieving significantly lower error metrics and improved generalisation across operating conditions. Notably, the transformer-based architecture enables scalable learning on large unstructured datasets while maintaining high predictive accuracy. These results demonstrate that flow-matching generative models provide an effective and flexible framework for surrogate modelling in fluid dynamics, with potential for realistic engineering and scientific applications.

The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements.

We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble forecasts into high-resolution ensembles by learning the conditional distribution of finer-scale residuals, defined as the difference between the high-resolution fields and the interpolated low-resolution inputs. The system is trained on reforecast pairs from ECMWF IFS, using coarse fields at 100 km to reconstruct fine-scale variability at 30 km resolution. The bulk of the training focuses on recovering small-scale structures, while fine-tuning in high-noise regimes enables the generation of extremes. Evaluation against the medium-range IFS ensemble target shows that the model increases probabilistic skill (FCRPS) for surface variables, reproduces target power spectra at small scales, captures physically consistent multivariate relationships such as wind-pressure coupling, and generates extreme values consistent with those of the target ensemble in tropical cyclones.

We introduce a method for measuring the velocity of turbulent fluid flow passing through a pipe using piezoelectric tiles without penetrating the pipe, and without having previously designed the pipe to easily allow monitoring. To measure the flow, the vibrations induced on the pipe by the fluctuating pressure loading induced by the turbulent flow are measured and compared across flow speeds to establish effective invertible relationships from vibration to velocity. Measurements are reported for instrumented pipes transporting, in separate experiments, water and air. The water experiment was able to resolve linear velocity differences on the order of 1~cm/second, while the air experiment was able to resolve on the order of 15~cm/sec. Turned inside out, a similar system might be used to assess external flow velocity, determine differential velocities on opposite sides of a body traveling through air and water, and thus provide navigational data in the form of speed and attitude/angle of attack information. Although this approach is prototyped for a single sensor, it is likely to benefit substantially from the noise suppression possible when employing an array of sensors.

The imitative verb "chirp" is thought to originate from 16th-century Middle English. Meanwhile, this same word has been used to describe the gravitational waves (GWs) emitted from the merger of compact objects, such as black holes and neutron stars, since at least the 1990s. Motivated purely by this linguistic overlap, we study whether the chirps of birds can be modeled by compact binary waveforms. In particular, we consider a test case of the Northern cardinal (Cardinalis cardinalis), finding that its time-reversed chirp can be approximately modeled by that of a high mass ratio, precessing black hole binary, with a number of indications towards extreme matter effects or beyond the Standard Model physics. Importantly, this waveform correspondence is not so straightforward for all bird species, as some chirp morphologies are far more akin to glitches seen in GW observatories. With these comparisons made, we propose an alternative solution to the longstanding philosophical conundrum: rather than the chicken or the egg, perhaps it was the Big Bang which truly came first.

The 2023 Lahaina wildfire killed 102 people on a peninsula served by a single two-lane highway, making exit lane capacity the binding constraint on evacuation time. We model the evacuation as a system of hyperbolic scalar conservation laws on a directed graph with game-theoretic junction conditions that maximize total network flux, an evacuation-calibrated piecewise linear-quadratic flux function, and a loss-driven optimization framework that tunes traffic distribution toward priority corridors. Analytical results on a toy network and numerical simulations of the Lahaina road network reveal a phase transition in exit lane capacity. Additional lanes improve throughput linearly until a computable critical threshold, beyond which no route optimization yields further benefit. For Lahaina, reversing one southbound lane captures nearly all achievable improvement, and a fourth lane can be reserved for emergency vehicles with negligible impact on civilian clearance time. These results provide a rigorous mathematical basis for contraflow recommendations in wildland-urban interface evacuations.

We present a data-driven framework that extends the predictive capability of classical lifting-line theory (LLT) to a wider aerodynamic regime by incorporating higher-fidelity aerodynamic data from panel method simulations. A neural network architecture with a convolutional layer followed by fully connected layers is developed, comprising two parallel subnetworks to separately process spanwise collocation points and global geometric/aerodynamic inputs such as angle of attack, chord, twist, airfoil distribution, and sweep. Among several configurations tested, this architecture is most effective in learning corrections to LLT outputs. The trained model captures higher-order three-dimensional effects in spanwise lift and drag distributions in regimes where LLT is inaccurate, such as low aspect ratios and high sweep, and generalizes well to wing configurations outside both the LLT regime and the training data range. The method retains LLT's computational efficiency, enabling integration into aerodynamic optimization loops and early-stage aircraft design studies. This approach offers a practical path for embedding high-fidelity corrections into low-order methods and may be extended to other aerodynamic prediction tasks, such as propeller performance.

Large Language Models (LLMs) are now widely used in astrophysics, but do they actually make our lives easier, or do they merely invent new physics with enough confidence to hide a minus sign? In a specialized field where checking fluent hallucinations is itself labor-intensive, AI assistance can demand as much work as the task it claims to simplify. To evaluate where AI genuinely improves scientific workflows, we bypassed human trials and instead forced AI agents to cosplay as astrophysicists. We simulated 144 synthetic researchers, varying in career stage, AI awareness, and willingness to verify outputs, across 2,592 daily astrophysics research assignments. Comparing solo work against four styles of AI assistance produced 12,960 scored episodes. No assisted policy universally outperformed unassisted work in the primary Qwen production run. Instead, performance depends strongly on the task, the style of AI use, and the identity of the actor. While cautious assistance helps on creative, extractive, and critique-oriented tasks, it can fail catastrophically on derivation-heavy physics. A full actor-swap DeepSeek rerun changes that picture materially: verification-heavy use becomes the strongest assisted policy, two assisted modes enter the higher-utility/lower-risk quadrant, and the derivation-heavy fragility that dominates the Qwen production run largely disappears. In its current form, AI is useful, but only conditionally, its value is uneven, task-specific, and shaped jointly by workflow, usage policy, and which LLM you are using.

We identify a new class of non-Hermitian causal processes that produce statistically significant temporal correlations invisible to conventional spectral methods. Using a generative model with a strictly causal memory kernel, we demonstrate that time-asymmetric stochastic processes naturally yield sharp transitions at characteristic scales that appear as localized structures in similarity space but leave no trace in power spectra. The model predicts an asymmetric transition profile with orientation-dependent asymmetry parameter $A(\theta)=A_0\cos(\theta+\delta)$ and achieves quantitative agreement ($\chi^2/\mathrm{dof}=0.50$, $p=0.86$) with high-precision counting experiments exhibiting $p<10^{-15}$ significance. These results establish a fundamental limitation of spectral analysis for non-Hermitian, non-stationary processes and provide experimentally testable signatures of causal memory in open quantum systems.

Astrology has long been used to interpret human personality, estimate compatibility, and guide social decision-making. Zodiac-based systems in particular remain culturally influential across much of the world, including in South Asian societies where astrological reasoning can shape marriage matching, naming conventions, ritual timing, and broader life planning. Despite this persistence, astrology has never established either a physically plausible mechanism or a statistically reliable predictive foundation. In this work, we examine zodiac-based personality prediction using a controlled machine-learning framework. We construct a synthetic dataset in which individuals are assigned zodiac signs and personality labels drawn from a shared pool of 100 broadly human traits. Each sign is associated with a subset of 10 common descriptors, intentionally overlapping with those assigned to other signs, thereby reproducing the ambiguity characteristic of practical astrological systems. We then train Logistic Regression, Random Forest, and neural-network classifiers to infer personality labels from zodiac-based features and nuisance covariates. Across all experiments, predictive performance remains at or near random expectation, while shuffled-label controls yield comparable accuracies. We argue that the apparent success of astrology arises not from measurable predictive structure, but from trait universality, category overlap, cognitive biases such as the Barnum effect and confirmation bias, and the interpretive flexibility of astrologers and pundits. We conclude that zodiac-based systems do not provide reliable information for predicting human behavior and instead function as culturally durable narrative frameworks. This paper is intended as a humorous academic exercise.

We present the observation of electrically tunable quantum coupling of orbital states in individual InAs/InGaAs quantum dot molecules emitting in the telecom O-band (~1300 nm). By tuning the static electric field along the growth axis of the QD-molecule, we observe pronounced anticrossings between excitonic transitions and determine the dependence of the interdot electron tunnel coupling on the interdot separation. As the electric field applied along the growth axis of the QD-molecules increases, positively charged exciton complexes sequentially emerge in the time-integrated emission spectra due to electron escape from the system while holes remain trapped. Moreover, for strong pumping, biexciton emission from the O-band molecules is identified. We demonstrate single-photon emission from the InAs/InGaAs QD-molecule emitting around 1300 nm with a g(2)(0) = 0.017(2) and explore the impact of tuning orbital coupling on the second-order correlation function.

This paper presents results from systematic investigations conducted in the HYPERION facility to quantify permeabilities of hydrogen isotopes in FLiBe over a temperature range of 773K - 973K. To address the knowledge gap resulting from widely scattered transport parameters reported in the earlier studies, HYPERION experiments incorporate specific provisions to probe unsubstantiated assumptions, including a one-dimensional permeation, membrane surface coverage by the salt, and ideal wettability of the salt-metal interface. Suppression of the isotopic transport behaviour for metal-side charging highlights the permeation barrier characteristics of a bubble-laden Ni-FLiBe interface, impacting permeability by up to 77%. This work attempts to resolve this issue via salt-side charging and informs constraints on the permeant charging methodology for future studies. The initial insights from HYPERION experiments call into question the design choices and estimated transport properties in earlier works. This study provides a plausible explanation for the observed scatter in H/D/T transport in FLiBe.

Understanding how reliable information emerges in interconnected populations is a challenge in social science, network theory and data analysis. Many existing approaches model treat truth as an external reference or a property of individual statements, rather than a global consistency feature of the network itself. We introduce a network-based approach in which truth arises from global relational coherence in a multiplex system of interacting individuals. Nodes are individuals with internal states, while edges capture different types of interactions, including declared relations, observed behavior, influence asymmetries and information exchange. We evaluate how well node states align with cooperative or antagonistic interactions, incorporating coercion, variability and mismatches between what individuals say and what they do. Simulations on synthetic networks of one thousand nodes show that the minimum global inconsistency does not coincide with majority opinion or simple averaging. Nodes contributing most to inconsistency create conflicting constraints across interaction layers, defining a measurable distortion field. For example, in online social media during an election, a small number of accounts spreading inconsistent or manipulative information across groups can disrupt overall coherence, even when most users appear to agree. These results suggest possible applications in assessing relational coherence, identifying irreducible inconsistencies and analyzing constraints on collective states. Therefore, truth can be seen as the state of maximal relational coherence, rather than simple agreement or correctness of individual statements.

In a companion paper we derived a unique time-reversal-invariant stochastic generalization of the Liouville equation and showed that it coincides with the evolution equation for the Husimi $Q$-function in a broad class of bosonic quantum field theories. Here we investigate the prospects for interpreting that evolution equation in terms of underlying stochastic trajectories. Drawing on Drummond's time-symmetric stochastic action formalism, we show that the traceless diffusion Fokker-Planck equation defines a natural measure over stochastic trajectories conditional on mixed-time boundary conditions. However, we identify a significant gap: it has not been established that every $Q$-function can be represented as a weighted average of these conditional probabilities over boundary values. The trajectory interpretation holds for ensembles with fixed boundary conditions but does not straightforwardly extend to arbitrary quantum states. Despite this limitation, we show that Drummond's trajectory dynamics are fundamentally non-Markovian – a natural consequence of combining stochasticity with time-reversal invariance. This non-Markovianity places the dynamics outside the scope of the ontological models framework and thereby explains why the major no-go theorems for hidden-variable theories do not rule out the approach. These results clarify both the achievements and the remaining challenges in the project of understanding quantum field theory as the statistical mechanics of time-symmetric stochastic processes.

We develop a conservative finite-volume Buckley–Leverett solver equipped with a bounded-interval multiwavelet state-analysis layer. Because non-capillary Buckley–Leverett transport is a nonlinear hyperbolic conservation law with entropy-admissible shocks, the saturation equation is advanced by a conservative finite-volume method with monotone numerical fluxes. The accepted finite-volume state is then embedded in a bounded-domain multiwavelet hierarchy, reconstructed back to cell averages, and used for multiresolution diagnostics. The formal transport accuracy is therefore governed by the underlying finite-volume discretization, while the multiwavelet layer is used for representation, compression, and front-localization diagnostics. Its purpose is instead to quantify whether the deterministic physical-space saturation state can be represented faithfully, compressed in a controlled manner, and used to identify dynamically active front regions. Validation against reference Buckley–Leverett profiles for a Berea benchmark shows accurate saturation histories, spatial profiles, front-position diagnostics, and mass balance. The multiwavelet reconstruction tracks the internal finite-volume state with essentially exact fidelity. Additional thresholding tests show that a substantial fraction of detail coefficients can be discarded while maintaining small reconstruction errors and negligible global mass defect, and fine-level detail activity localizes the moving displacement front. The resulting formulation provides a conservative and reproducible first stage toward future transport-active adaptive multiwavelet solvers for porous-media flow.

We develop a general framework for the electrostatic analysis of point charges in multilayer planar structures with arbitrary layer thicknesses and material parameters. Starting from a Hankel-transform analysis, we derive alternative representations of the solution and establish a Stokes-like formulation based on “generalized reflection coefficients,” yielding a systematic and physically transparent treatment of multilayer media. This approach extends classical image theory to parameter regimes in which the conventional image-charge series (which has an infinite number of terms) diverges. The formulation applies to arbitrary permittivity values, including negative permittivities, where overscreening effects and plasmon-resonant conditions may occur. In these regimes, we show that the boundary-value problem no longer has a unique solution because homogeneous (source-free) modes appear; and we derive Cauchy-principal-value integral representations for the particular solution. We also introduce an asymptotic “phantom-image” method that replaces a divergent infinite image series by a finite set of effective sources, thus providing a computationally efficient approximation in large-reflection regimes. These results furnish both practical computational tools and additional mathematical insight into the structure of electrostatic image theory in layered media.

Spin-orbit, charge-transfer intersystem crossing (SOCT-ISC) allows for the efficient production of triplet excited states in donor-acceptor (DA) dyads without the involvement of heavy atoms, for use in a myriad of technologies. This process is commonly believed to proceed optimally when the dihedral angle between donor and acceptor moieties is orthogonal. Here, we challenge this idea through a theoretical study unveiling a scenario where spin-orbit couplings (SOCs) are minimized under orthogonal conditions. This scenario is rationalized based on an analysis of the structure-imposed symmetry properties of the involved singlet and triplet states. Notably, in this scenario, finite SOCs demand oblique orientation angles, which in turn requires molecular chirality, suggesting chirality to be a prerequisite for activating the involved SOC pathways.

The heating, ventilation and air conditioning systems serving the experimental halls of the Virgo gravitational wave interferometer generate low-frequency noise - namely below 100 Hz - of seismic, acoustic, and electromagnetic origin. Such disturbances have repeatedly affected the interferometer sensitivity throughout its operational history, with particularly notable impacts during the third observing run. In preparation for the fourth run, a comprehensive investigation was carried out to identify the most critical noise sources within this infrastructure and to trace their transmission paths into the experimental areas. This manuscript presents the methodology and results of the noise characterization campaign, together with the design, implementation and assessment of targeted mitigation measures. The technical solutions adopted, along with the operational best practices developed, provide valuable guidance for the design of low-noise environments in future gravitational-wave observatories.

The artificial intelligence industry is not an isolated economic phenomenon; it is the current physical substrate for a broader, multi-billion-year process: the evolution of an abstract intelligence on Earth. As the scale of computation accelerates planetary-wide, the academic and popular discourse must address the physics aspect of this phase transition, beyond algorithmic architectures, alignment, and silicon supply chains. From first principles, it is clear that if the current exponential trajectory of computation holds, the ultimate bottleneck of the coming decades will be neither data nor capital, but the laws of thermodynamics and the finite heat capacity of the Earth. The evolution of intelligence is fundamentally a problem of non-equilibrium thermodynamics, bound by strict hardware limitations, and ultimately, an absolute ecological boundary condition. Civilization itself is an exceedingly rare and highly expensive thermodynamic algorithm actively fighting the default settings of the cosmos. To better understand AI's future and our future, we must rigorously examine the physical laws governing computation, complexity theory, and the narrow thermodynamic tightrope our civilization must walk to survive it

The location of the hydrogen-helium (H/He) immiscibility boundary controls whether and where helium rain occurs in giant planets, yet it remains uncertain because high-pressure experiments are challenging and ab initio simulations are limited in system size and simulation time. We map this boundary by computing composition-dependent chemical potentials from large-scale molecular dynamics driven by machine learning potentials trained on three density functional approximations (PBE, vdW-DF, and the hybrid HSE). Across the pressure range of 100-1000 GPa, the demixing temperatures are typically ~2000 K lower than previous ab initio simulations using small system sizes, and this discrepancy is larger than the estimated error from the chosen density functionals, machine learning potentials, neglect of nuclear quantum effects, and statistical uncertainties. Fitting the H/He mixing free energy to a Redlich-Kister regular solution model rationalizes the thermodynamic driving force for phase separation and provides a predictive representation of the boundary. Comparing with current planetary interior profiles indicates that helium rain is plausible in Saturn but unlikely in the warmer interior of Jupiter. Our results narrow the uncertainty in the H/He immiscibility boundary and provide inputs for planetary models that couple demixing, heat transport, and composition gradients in gas giants.