We consider skew-commutative subalgebras in Drinfeld-Jimbo quantum groups at a root of unity $ζ$ generated by primitive power elements. We classify the centrality and commutativity of these skew-polynomial algebras depending on the Lie type and the order of $ζ$ modulo 8. We describe Hopf ideals in the quantum group induced from ideals in these subalgebras, including the non-commutative cases. Among these, we construct and analyze a family of Hopf ideals that depend on the choice of an element in the Weyl group. We show that they arise naturally both in the construction of (partial) $R$-matrices and as vanishing ideals of Bruhat subgroups. Specialization to the maximal element yields a rigorous construction of restricted quantum groups as pre-triangular Hopf algebras, independent of any choices. Our treatment also includes even orders of $ζ$, non-simply laced Lie types, and minimal ground rings. Consequently, we extend some results of De Concini-Kac-Procesi, whose work focuses on odd orders of $ζ$, which forces the subalgebra to be strictly central, and complex ground fields. This includes the identification of the subalgebras for Lie types $\mathsf{A}_n$ and $\mathsf{B}_2$ with the coordinate rings of associated algebraic groups in the commutative cases, even if $ζ$ has even order. Our descriptions are computationally explicit and do not utilize Poisson structures. As technical preparations, we discuss PBW bases over minimal rings, dependencies on choices of convex orderings, as well as various new constructions of, and relations among, automorphisms on quantum groups. The latter include formulae for the Garside element in the Lustzig-Artin group action and the family of Che-transformations.

We present a detailed study of the nucleon electromagnetic form factors in the spacelike region by combining three complementary approaches: two GPD-based contributions and a vector-meson exchange component. By fitting experimental data, we extract the optimal weights and shape parameters describing the proton and neutron form factors. Global Padé-based fits are then constructed for four distinct groups of form factors, starting from local Taylor expansions and yielding stable analytic parametrizations over the analyzed $t$ range. The combined framework provides an accurate and physically motivated description of nucleon structure within a controlled model-dependent setting across a wide range of momentum transfers.

In this letter, we discuss the determination of the $f_0(500)$ resonance by analytic continuation through Padé approximants of the $ππ$-scattering amplitude from the physical region to the pole in the complex energy plane. Using as input a class of admissible parametrizations of the scalar-isoscalar $ππ$ partial wave and imposing now the correct threshold behavior of the partial amplitude, we improve on the determinations of pole positions obtained in Ref. [1], thus empowering the Padé method as a simple and precise tool for extracting resonance poles from amplitudes.

Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable of code generation may support scientific programmers, but user studies indicate risks of over-reliance, particularly among inexperienced users. We surveyed 868 scientists who program, examining adoption patterns, tool preferences, and factors associated with perceived productivity. Adoption is highest among students and less experienced programmers, with variation across fields. Scientific programmers overwhelmingly prefer general-purpose conversational interfaces like ChatGPT over developer-specific tools. Both inexperience and limited use of development practices (like testing, code review, and version control) are associated with greater perceived productivity -- but these factors interact, suggesting formal practices may partially compensate for inexperience. The strongest predictor of perceived productivity is the number of lines of generated code typically accepted at once. These findings suggest scientific programmers using generative AI may gauge productivity by code generation rather than validation.

The emergence of commercial satellite communications networks, such as Starlink and OneWeb, has significantly transformed the communications landscape over the last years. As a complement to terrestrial cellular networks, non-terrestrial systems enable coverage extension and reliability enhancement beyond the limits of conventional infrastructure. Currently, the high reliance on terrestrial networks exposes communications to vulnerabilities in the event of terrestrial infrastructure failures, e.g., due to natural disasters. Therefore, this work proposes the joint evaluation of Key Performance Indicators (KPIs) for two non-terrestrial satellite networks (Starlink and OneWeb) and two terrestrial cellular networks to assess the current performance of these technologies across three different environments: (i) urban, (ii) suburban, and (iii) forest scenarios. Additionally, multi-connectivity techniques are explored to determine the benefits in connectivity when two technologies are used simultaneously. For instance, the outage probability of Starlink and OneWeb in urban areas is reduced from approximately 12-21% to 2% when both solutions are employed together. Finally, the joint analysis of KPIs in both terrestrial and non-terrestrial networks demonstrates that their integration enhances coverage, improves performance, and increases reliability, highlighting the benefits of combining satellite and terrestrial systems in the analyzed environments.

The Coulomb potential at an interior ion in a finite crystal of size $p$ is given by a linear superposition of contributions from displacement vectors ${\mathbf r}=(x,y,z)$ to its neighbors. This additive structure underlies universal relationships among Madelung constants and applies to both standard periodic boundary conditions and alternative Clifford supercells. Each pairwise contribution decomposes into three physically distinct components: a periodic bulk term, a quadratic boundary term, and a finite-size correction whose leading order term is $[24r^4-40(x^4+y^4+z^4)]/[9\sqrt{3} (2p+1)^2]$ for cubic crystals with unit lattice constant. Combining this decomposition with linear superposition yields a rapidly convergent direct-summation scheme, accurate even at $p=1$ ($3^3$ unit cells), enabling hands-on calculations of Madelung constants for a wide range of ionic crystals.

We derive a theoretical model to elucidate the inhibition of catalytic activity during the dehydrogenation of Liquid Organic Hydrogen Carriers (LOHC). Within our model, we account for the reversible nature of the hydrogenation-dehydrogenation reaction as well as the transport of both LOHC and produced hydrogen. Our analysis reveals that the main limiting factor for the performance of porous catalysts is the transport of dissolved hydrogen, which has been overlooked so far. In particular, we show that two distinct kinetic regimes can arise depending on whether hydrogen leaves the pellet in form of bubbles or via diffusion. Moreover, we derive the conditions for the onset of bubbling depending on hydrogen supersaturation and capillarity. Beyond LOHC systems, our findings are applicable to a broader class of reversible reactions, particularly those involving volatile products that can leave the liquid reaction medium in the form of bubbles.

We investigate the higher-order Volterra-type integral operator $T_{g,n}$ on the unit disk, defined for $n\in\mathbb N$ by \[ T_{g,n}[f](z) := \underbrace{\int_{0}^{z}\int_{0}^{t_1}\cdots\int_{0}^{t_{n-1}}}_{n\ \text{times}} f(t_n)g'(t_n)\,dt_n\cdots dt_1,\quad z\in\mathbb D, \] where $f$ and $g$ are analytic in the unit disk $\mathbb D$. We establish sharp norm and essential norm estimates, and give complete characterizations of boundedness and compactness of $T_{g,n}$ on Hardy spaces $H^p$ and weighted Bergman spaces $A_α^p$, in terms of (vanishing) Carleson measure conditions determined by $|g'|$.

We show that a two-proton emitter with a diproton-correlated initial state can act as a source of spin-correlated proton pairs. Using a time-dependent three-body model, we investigate the two-proton emission of $^{16}$Ne ($^{14}$O$+2p$) and analyze the spin correlation of the emitted protons. We find that, when the emission proceeds as a democratic three-body process from an initial state containing a spin-singlet diproton correlation, the emitted protons exhibit a pronounced spin-correlation pattern exceeding the local-hidden-variable bound. This spin correlation closely resembles that of a pure spin-singlet pair. In contrast, this pattern is lost when the process is dominated by the sequential emission or when the initial diproton correlation is absent. These results demonstrate that a certain class of two-proton emitters can deliver spin-entangled proton pairs, and their spin correlation reflects the diproton correlation embedded in the initial state.

Classical thermodynamics is a theory based on coarse-graining, meaning that the thermodynamic variables arise from discarding information related to the microscopic features of the system at hand. In quantum mechanics, however, where one has a high degree of control over microscopic systems, information theory plays an important role in describing the thermal properties of quantum systems. Recently, a new approach has been proposed in the form of a quantum thermodynamic gauge theory, where the notion of redundant information arises from a group of physically motivated gauge transformations called the thermodynamic group. In this work, we explore the geometrical structure of quantum thermodynamics. Particularly, we do so by explicitly constructing the relevant principal fibre bundle. We then show that there are two distinct (albeit related) geometric structures associated with the gauge theory of quantum thermodynamics. In this way, we express thermodynamics in the same mathematical (geometric) language as the fundamental theories of physics. Finally, we discuss how the geometric and topological properties of these structures may help explain fundamental properties of thermodynamics.

For a given twisted cartesian products of simplicial sets, we construct the corresponding twisted tensor product in the sense of Brown, with an explicit twisting function whose formula is simple without using inductions. This is done by choosing an explicit morphism of topological monoids from Kan's loop group to Moore loop spaces, following Berger's work on simplicial prisms. We follow the choice of Brown and Berger on such a morphism, which is different from that of Gugenheim and Szczarba.

Sequential recommender systems have demonstrated strong capabilities in modeling users' dynamic preferences and capturing item transition patterns. However, real-world user behaviors are often noisy due to factors such as human errors, uncertainty, and behavioral ambiguity, which can lead to degraded recommendation performance. To address this issue, recent approaches widely adopt self-supervised learning (SSL), particularly contrastive learning, by generating perturbed views of user interaction sequences and maximizing their mutual information to improve model robustness. However, these methods heavily rely on their pre-defined static augmentation strategies~(where the augmentation type remains fixed once chosen) to construct augmented views, leading to two critical challenges: (1) the optimal augmentation type can vary significantly across different scenarios; (2) inappropriate augmentations may even degrade recommendation performance, limiting the effectiveness of SSL. To overcome these limitations, we propose an adaptive augmentation framework. We first unify existing basic augmentation operations into a unified formulation via structured transformation matrices. Building on this, we introduce AsarRec (Adaptive Sequential Augmentation for Robust Sequential Recommendation), which learns to generate transformation matrices by encoding user sequences into probabilistic transition matrices and projecting them into hard semi-doubly stochastic matrices via a differentiable Semi-Sinkhorn algorithm. To ensure that the learned augmentations benefit downstream performance, we jointly optimize three objectives: diversity, semantic invariance, and informativeness. Extensive experiments on three benchmark datasets under varying noise levels validate the effectiveness of AsarRec, demonstrating its superior robustness and consistent improvements.

We discuss the symmetry aspects of quantum field theory in global four-dimensional de Sitter spacetime linked to $SO(1,4)$ isometries. For the unitary irreducible representations relevant to elementary particles, we obtain explicit transformation laws for the symmetry generators acting on one-particle states in a basis adapted to the $SU(2) \times SU(2)'$ decomposition of the Hilbert space. Using these results, we derive the corresponding Ward identities and demonstrate how global spacetime symmetries constrain de Sitter scattering amplitudes. We show that the Poincaré algebra and flat-space Ward identities are recovered in the large-momentum limit.

Space infrastructures represent an emerging domain that is critical to the global economy and society. However, this domain is vulnerable to attacks, including cyber attacks and other kinds of attacks. To enhance the resilience of this domain, we must understand these attacks that can be waged against it and the defenses that can be employed to mitigate these attacks. The status quo is that there is neither a systematic understanding of these attacks against, nor defenses for, space infrastructures, despite their clear importance in guiding systematic analysis of space security and future research. In this paper, we fill the void by proposing the first systematic taxonomy of attacks against, and defenses for, space infrastructures. We hope this paper will inspire a community effort at refining the taxonomy towards a widely used one.

We compute the Form Factors of both neutral and charged pion using a non-perturbative running of the strong coupling constant $α_s$ obtained using a double-dilaton Holographic QCD model. These form factors remain poorly understood in the intermediate-energy region, which marks the transition between low- and high-energy physics. In particular, experimental data for the neutral pion Form Factor exhibits a deviation from the expected asymptotic behavior, and the charged pion form factor remains comparatively less explored. To address these issues, we employ the pion distribution amplitude formalism to investigate the Form Factor behavior in this intermediate regime. Our results suggests that non-perturbative physics of the strong interaction is relevant even at energy scales traditionally considered perturbative, implying that the perturbative regime could occur at higher energies than previously thought. Finally, our approach allows us to study isospin-breaking effects through the quadratic pion mass difference.

Matrix isolation spectroscopy enables probing atomic properties in controlled cryogenic environments. We present a spectroscopic study on neutral barium atoms embedded in a neon cryogenic crystal at 6.8 K, extending previous investigations performed in other noble gas hosts. The visible and near-infrared emission spectra were recorded under two different laser excitation schemes. First, 10-ns laser pulses at 355 nm were used to directly excite high-lying energy levels of barium, enabling the observation of fluorescence cascades. Second, a tunable continuous-wave laser operating between 700 nm and 900 nm allowed us to determine the matrix-induced shifts of barium energy levels relative to their vacuum values, as well as the inhomogeneous linewidths of the observed transitions and to perform lifetime measurements. Our results confirm multiple radiative pathways and matrix-induced relaxation channels affecting the 5d6s and 6s6p barium manifolds. Furthermore, we present the first lifetime measurement of the barium 5d6s 3D1 state in a neon crystal, yielding 0.39 \pm 0.02 s, with a predicted increase of about 10% at 2 K. The study of fluorescence and spectroscopic properties of barium isolated in neon represents an important step toward future searches for the electron electric dipole moment using barium monofluoride in neon matrices, where neutral barium atoms may act as unavoidable impurities and potential sources of background and systematic limitations.

Gravitons naturally acquire topological masses in the 3d topologically massive gravity (TMG) theory that includes the gravitational Chern-Simons term. We present a Weyl-transformed TMG (WTMG) formulation by introducing an unphysical dilaton field through the Weyl transformation. We perform the BRST quantization of the WTMG, which reduces to the conventional TMG in the unitary gauge. We demonstrate that this WTMG theory conserves the physical degrees of freedom (DoF) in the massless limit, under which the physical massive graviton becomes an unphysical massless graviton and its physical DoF is converted to the massless dilaton. With these, we newly establish a Topological Graviton Equivalence Theorem (TGRET), which connects each scattering amplitude of physical gravitons to the corresponding dilaton scattering amplitude in the high energy limit. The TGRET provides a general mechanism to guarantee all the large energy cancellations in any massive graviton scattering amplitudes. Applying the TGRET and using the generalized gravitational power counting rule, we prove that the $N$-point massive graviton amplitudes ($N\!\!\geqq\!4$) have striking energy cancellations by powers proportional to $\frac{5}{2}N$ ($\frac{7}{2}N$) in the Landau (unitary) gauge. For four graviton scattering amplitudes, this explains their large energy cancellations of $E^{11}\to E^1$ (Landau gauge) and $E^{12}\to E^1$ (unitary gauge). We compute the four-point graviton (dilaton) amplitudes and explicitly demonstrate the TGRET and these large energy cancellations. With the extended massive double-copy approach, we systematically construct the three- and four-point graviton (dilaton) scattering amplitudes in the WTMG theory from the corresponding gauge boson (adjoint scalar) amplitudes in the topologically massive Yang-Mills theory.

In this paper, we explore the concept of metric-driven numerical methods as a powerful tool for solving various types of multiscale partial differential equations. Our focus is on computing constrained minimizers of functionals - or, equivalently, by considering the associated Euler-Lagrange equations - the solution of a class of eigenvalue problems that may involve nonlinearities in the eigenfunctions. We introduce metric-driven methods for such problems via Riemannian gradient techniques, leveraging the idea that gradients can be represented in different metrics (so-called Sobolev gradients) to accelerate convergence. We show that the choice of metric not only leads to specific metric-driven iterative schemes, but also induces approximation spaces with enhanced properties, particularly in low-regularity regimes or when the solution exhibits heterogeneous multiscale features. In fact, we recover a well-known class of multiscale spaces based on the Localized Orthogonal Decomposition (LOD), now derived from a new perspective. Alongside a discussion of the metric-driven approach for a model problem, we also demonstrate its application to simulating the ground states of spin-orbit-coupled Bose-Einstein condensates.

We developed an optically detected magnetic resonance (ODMR) setup designed for compatibility with a widely used, commercially available helium bath cryostat equipped with a variable temperature insert. The optical path extends nearly two meters, spanning the full length of the cryostat insert, enabling excitation of the nitrogen-vacancy (NV) centers and detection of the resulting fluorescence from outside the cryostat. The setup preserves optical alignment and beam quality along this extended path allowing integration into existing cryogenic systems without significant modifications. We demonstrate the setup's performance by measuring the temperature dependence of the resonance signal and its behavior under small applied magnetic fields, as well as the magnetic transition of a SrRuO$_3$ sample, thereby showcasing the feasibility of NV magnetometry on a sample in constrained cryogenic environments.

Prevalence surveys are routinely used to monitor the effectiveness of mass drug administration (MDA) programmes for controlling neglected tropical diseases (NTDs). We propose a decay-adjusted spatio-temporal (DAST) model that explicitly accounts for the time-varying impact of MDA on NTD prevalence, providing a flexible and interpretable framework for estimating intervention effects from sparse survey data. Using case studies on soil-transmitted helminths and lymphatic filariasis, we show that DAST offers a practical alternative to standard geostatistical models when the objective includes quantifying MDA impact and supporting short-term programmatic forecasting. We also discuss extensions and identifiability challenges, advocating for data-driven parsimony over complexity in settings where the available data are too sparse to support the estimation of highly parameterised models.

This paper addresses the reconstruction of sparse signals from generalized linear measurements. Signal sparsity is assumed to be sublinear in the signal dimension while it was proportional to the signal dimension in conventional research. Approximate message-passing (AMP) has poor convergence properties for sensing matrices beyond standard Gaussian matrices. To solve this convergence issue, generalized orthogonal AMP (GOAMP) is proposed for signals with sublinear sparsity. The main feature of GOAMP is the so-called Onsager correction to realize asymptotic Gaussianity of estimation errors. The Onsager correction in GOAMP is designed via state evolution for orthogonally invariant sensing matrices in the sublinear sparsity limit, where the signal sparsity and measurement dimension tend to infinity at sublinear speed in the signal dimension. When the support of non-zero signals does not contain a neighborhood of the origin, GOAMP using Bayesian denoisers is proved to achieve error-free signal reconstruction for linear measurements if and only if the measurement dimension is larger than a threshold, which is equal to that of AMP for standard Gaussian sensing matrices. Numerical simulations are also presented for linear measurements and 1-bit compressed sensing. When ill-conditioned sensing matrices are used, GOAMP for sublinear sparsity is shown to outperform existing reconstruction algorithms, including generalized AMP for sublinear sparsity.

In this work, we combine the density functional tight-binding (DFTB) approach with a light-matter Hamiltonian beyond the long-wavelength approximation to propagate the dynamics of vibrational polaritons formed by coupling molecular vibrations to confined radiation inside a Fabry-Pérot optical cavity. Here, we develop a parallelized propagation scheme with lightweight inter-CPU communication by exploiting the sparse nature of the light-matter interactions in the real space representation. We find that the computationally expensive Born charges required for our propagation can be replaced with the computationally inexpensive Mulliken charges to obtain qualitatively accurate linear spectra especially when the nonlinearity (arising from molecular vibrations) of the light-matter interaction term is not substantial. However, the same approach may not be suitable to be used for studying cavity modification of energy transport or chemical dynamics as this approximation leads to spurious heating of the light-matter hybrid system. We demonstrate the utility of this on-the-fly approach to compute angle resolved polaritonic spectra of water. We implement our approach as an open-source computational package, CavOTF, which is available on GitHub.

We investigate baryon number violating (BNV) nucleon decays into three leptons from noncontact contributions that are induced by dimension-6 (dim-6) BNV operators in low-energy effective field theory (LEFT) with an exchange of a baryon, meson, lepton, or photon field. We systematically classify all these processes that change lepton flavor by one unit and formulate their decay widths in terms of the dim-6 LEFT Wilson coefficients. By applying constraints on these Wilson coefficients derived from current experimental limits on BNV two-body nucleon decays, we obtain stringent bounds on the rates of these triple-lepton modes. These bounds vary significantly from one dim-6 operator to another under consideration. Our results for the $Δ(B-L)=0$ modes differ by several orders of magnitude from previous phase-space estimates in the literature, thereby providing a more reliable assessment of their potential occurrence. In addition, we provide improved bounds on $Δ(B+L)=0$ modes compared to the existing experimental limits.

In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from surrogate endpoints. However, a surrogate that is highly associated with the primary outcome, or even satisfies existing surrogate criteria, may not necessarily induce a treatment rule that performs well on the primary outcome, especially under treatment resource budget constraints. In this paper, we develop a principled framework for evaluating the decision-making value of surrogate endpoints. We introduce three ITR-oriented performance measures: surrogate regret, which assesses the expected loss from using the surrogate-optimal ITR instead of outcome-optimal ITR; surrogate gain, which quantifies the benefit of surrogate-optimal ITRs relative to the no-treatment baseline; and surrogate efficiency, which evaluates improvement over random treatment assignment. We also extend them to budget-constrained settings. We propose augmented inverse probability weighted (AIPW) estimators for these measures and establish their large-sample properties. We demonstrate the proposed approach on both simulations and an application to the Criteo dataset.

This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabilizes the plant despite approximation errors. In particular, we prove recursive feasibility and asymptotic stability if a proportional error bound holds, where proportional means that the bound is linear in the norm of the state and the input. For a broad class of nonlinear systems, this condition can be satisfied using data-driven surrogate models generated by kernel Extended Dynamic Mode Decomposition (kEDMD) using the Koopman operator. Last, the applicability of the proposed framework is demonstrated in a numerical case study.

We study efficient algorithms for recovering cliques in dense random intersection graphs (RIGs). In this model, $d = n^{Ω(1)}$ cliques of size approximately $k$ are randomly planted by choosing the vertices to participate in each clique independently with probability $δ$. While there has been extensive work on recovering one, or multiple disjointly planted cliques in random graphs, the natural extension of this question to recovering overlapping cliques has been, surprisingly, largely unexplored. Moreover, because every vertex can be part of polynomially many cliques, this task is significantly more challenging than in case of disjointly planted cliques (as recently studied by Kothari, Vempala, Wein and Xu [COLT'23]). In this work we obtain the first efficient algorithms for recovering the community structure of RIGs both from the perspective of exact and approximate recovery. Our algorithms are further robust to noise, monotone adversaries, and a certain, optimal number of edge corruptions. They work whenever $k \gg \sqrt{n \log(n)}$. Our techniques follow the proofs-to-algorithms framework utilizing the sum-of-squares hierarchy.

We provide a precise description of the bottom of the spectrum in the semiclassical limit of a harmonic-type Schrödinger operator with an inverse square potential. By exploiting the connection between the eigenfunctions of these operators and the Kummer and Whittaker functions, we derive accurate localization results for the non-asymptotic zeros of these functions with respect to their first parameter, uniformly with respect to the argument taken large and real. Moreover, our operators are linked to the magnetic Dirichlet Laplacian in the presence of both a constant magnetic field and an Aharonov-Bohm flux line, so that our results describe its spectrum in the strong magnetic field limit. Our spectral analysis relies on a WKB-type approach.

Unlike the rigid, high-volume automation found in industry, academic research requires process flexibility that has historically relied on variable manual operations. This hinders the fabrication of advanced, complex devices. We propose to address this gap by automating these low-volume, high-stakes tasks using a robotic arm to improve process control and consistency. As a proof of concept, we deploy this system for the resist development of Josephson junction devices. A statistical comparison of the process repeatability shows the robotic process achieves a resistance spread across chips close to 2%, a significant improvement over the ~7% spread observed from human operators, validating robotics as a solution to eliminate operator-dependent variability and a path towards industrial-level consistency in a research setting.

We analyze the impact of breaking diffeomorphism invariance in the inflaton sector. In particular, we consider inflaton models which are invariant under the subgroup of transverse diffeomorphisms and address the possibility of implementing a slow-roll phase. We obtain the corresponding expressions for relevant quantities such as the slow-roll parameters and the number of $e$-folds, and derive the primordial power-spectrum of curvature perturbations. The scalar spectral index features modifications which are confronted with CMB data from Planck and ACT. We study in detail the quadratic potential model, combining asymptotic and numerical analysis. We show that the post-inflationary behavior can be drastically different from the diffeomorphism-invariant case, exhibiting novel dynamical regimes.

Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis.