Papers

Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a general strategy for data-driven, explainable characterization of complex materials.

Quantum geometry plays a fundamental role across many branches of modern physics, yet its full characterization in nonequilibrium systems remains a challenge. Here, we propose a framework for quantum geometry in Floquet topological insulators by introducing a time-resolved quantum metric tensor, defined via the trace distance between micromotion operators in momentum-time space. For class A in two spatial dimensions, we find a general inequality linking the Floquet quantum metric tensor and the Floquet topology: the associated quantum volume is bounded below by the Floquet topological invariant. This relation is found to also hold in class AIII in one dimension, where the Floquet geometric tensor may be notably reduced due to time-reflection symmetry. This work will be useful in digesting the general aspects of quantum geometry in periodically driven systems in connection with their topological characterization.

We investigate the structural, mechanical, and thermoelectric properties of lead-free double halide perovskites Cs2TeX6 (X = Cl, Br, I) using first-principles calculations and semiclassical Boltzmann transport theory. The HSE06 band gap is incorporated using the scissor correction method along with PBE calculated electronic band structures including spin orbit coupling to accurately predict transport properties. The band gap values are 3.27, 2.50, and 1.55 eV for Cs2TeX6 (X = Cl, Br, I), respectively. The coexistence of heavy and light bands in the Cs2TeI6 band structure helps mitigate the trade-off between the Seebeck coefficient and electrical conductivity. Among these systems, Cs2TeI6 exhibits superior performance with a ZT of 1.97 at 800 K and an electronic concentration of 3.35 x 10^19 cm^-3. Such a high ZT at relatively low carrier concentration arises from high electrical conductivity combined with low lattice thermal conductivity. The lattice thermal conductivity of Cs2TeI6 is found to be 0.41 W m^-1 K^-1 at room temperature. This low lattice thermal conductivity is attributed to weak Te-I bonding and non-uniform out-of-phase displacement of Cs atoms. The presence of local TeX6 units together with weak bonds strongly resists heat conduction, leading to significant suppression of lattice thermal conductivity. In particular, transverse acoustic phonons and optical phonons play a key role in limiting lattice thermal conductivity. These results identify Cs2TeI6 as a promising candidate for high performance thermoelectric applications.

We study a single lattice polymer chain under a fixed end-to-end distance, incorporating both Domb–Joyce (DJ) soft-core self-repulsion between polymer segments and a local bending-energy cost. By decomposing the stiffness into energetic and entropic contributions, we survey the parameter space defined by the self-repulsion strength and bending-energy cost. We find that the energetic contribution to stiffness is negative across the entire explored range, whereas the entropic contribution remains positive. These results unify two previously independent mechanisms of negative energetic elasticity – solvent-induced self-repulsion and bending stiffness – and demonstrate that either mechanism alone, as well as their combination, produces the same sign. Beyond this sign-level unification, we analyze the internal-energy scaling and show that, in the absence of the bending-energy term, the DJ (self-repulsion) limit exhibits a robust $(n-r)^{7/4}/n$ scaling collapse. In contrast, the introduction of finite bending stiffness progressively disrupts this scaling, providing an internal-energy-based diagnostic to distinguish between contributions from self-repulsion and bending stiffness.

We propose a general class of open fermionic models where quadratic Liouvillians governing the dissipative dynamics feature analytically characterized higher-order exceptional points (EPs). Invoking the formalism of third quantization, we show that, among the multiple EPs of Liouvillian, an EP with its order approaching the system size arises as the dominant modes of the system at long times, leading to a gapless Liouvillian spectrum. By introducing perturbations, in the form of many-body quantum-jump processes, these higher-order EPs break down, leading to finite Liouvillian gaps with fractional power-law scalings. While the power-law scaling is a signature of the higher-order EP, its explicit form is sensitively dependent on the many-body perturbation. Finally, we discuss the long-time dynamics which can serve as detectable signals for the higher-order Liouvillian EPs.

Can superconducting order be engineered as a robust attractor of open-system dynamics in strongly correlated systems? We demonstrate this possibility by proposing a minimal dissipation-engineering protocol for the particle-hole symmetric Hubbard model. By applying a rotated quantum jump operator, specifically a locally transformed $\eta$-pair lowering operator, on a single lattice site only, we show that the Lindblad evolution autonomously pumps the system from the vacuum into a nonequilibrium steady state (NESS) exhibiting macroscopic $\eta$-pair off-diagonal long-range order (ODLRO). Crucially, this local-to-global synchronization stands in stark contrast to schemes reliant on spatially extensive reservoirs: here, a single local dissipative seed suffices to establish long-range coherence across the entire interacting lattice system. We elucidate the underlying mechanism via three core features: local dark-state selection, the controlled elimination of off-manifold excursions induced by hopping, and a Liouvillian invariant-subspace structure that yields an attractive fixed point with a finite dissipative gap. Furthermore, we systematically classify the stability of this NESS with respect to static disorder, and identify a wide regime in which the superconducting attractor remains robust against Hamiltonian perturbations that preserve the effective subspace structure. We also pinpoint specific perturbations that directly cause dephasing of the $% \eta$-pseudospin coherence and suppress ODLRO. These findings open up a disorder-tolerant pathway for stabilizing superconducting order as a non-thermal attractor through minimal local quantum-jump control.

Physical properties intermediate between constituents of alloys can be achieved as downward convex positive bowing, upward concave negative bowing, or zero bowing. Such bowing effects are essential for band gap engineering in semiconductor alloys. Upward band gap bowing effects are rather rare, hindering the exploration on half of the available physical property space of alloys. Part of the this being a rare event is related to the need to stabilize an alloy with low mixing enthalpy, so it does not phase separate. In this paper we find via density functional theory that one can satisfy the simultaneous conditions of negative mixing enthalpy and upward band gap bowing in four-component ABX3 halide perovskite alloys in the cubic perovskite structure. Such perovskite alloys have the B-site occupied by a mixture of group IVB and IIB elements that have the IVB-s and IIB-s states in the valence bands and conduction bands, respectively, leaving the delocalized s states strongly repel each other. This s-s repulsion leads to the upward band gap bowing and negative mixing enthalpies simultaneously. Remarkably, we identify a perovskite alloy that has a band gap much larger than all its components. Analogous trends of upward band gap bowing and negative mixing enthalpy also appear in the corresponding three-component and two-component ABX3 halide perovskite alloys. These observations of upward band gap bowing and negative mixing enthalpy will significantly accelerate the design of stable upward band gap bowing alloys in a broad range of material families.

We study the presence of an external magnetic field, in combination with torsional strain, over the electron-phonon interactions in a type I Weyl semimetal. This particular superposition of field and strain, modeled in the continuum approximation by an effective gauge field, leads to an asymmetric pseudo-magnetic field at each Weyl node of opposite chirality. Therefore, we also studied the role of nodal asymmetry in the properties of the system by means of the Kadanoff-Wilson renormalization group and the corresponding flow equations. By solving those, we discuss the evolution of the coupling parameters of the theory, and analyze possible fixed points and lattice (Peierls) instabilities emerging from interactions between phonons with the chiral Landau level in the very strong pseudo-magnetic field regime.

Bimetallic catalysts provide new routes toward sustainable ammonia synthesis, but the nanoscale structural dynamics under reaction-relevant conditions remain poorly understood. Here, we combine in situ gas-cell and multimodal electron microscopy to determine how temperature, gas pressure, and chemistry select among distinct restructuring pathways in AuRu nanocrystal catalysts. Initially, the AuRu nanocrystals form polycrystalline face-centered cubic (FCC) alloys with Au/Ru intermixing. Elevated temperature ($\geq 350~^\circ$C) induces intraparticle phase segregation into distinct Au-rich (FCC) and Ru-rich hexagonal close-packed (HCP) domains that exhibit localized plasmonic modes. Atmospheric-pressure 3:1 H$_2$:N$_2$ gas unlocks a distinct restructuring regime absent at lower pressures, characterized by pronounced faceting and nanovoid formation. Systematic gas variation identifies hydrogen as the dominant driver. Density functional theory-trained machine-learning interatomic potentials and grand-canonical Monte Carlo simulations reveal that H-Ru interactions enhance the Au/Ru diffusivity mismatch, promoting vacancy accumulation and nanovoid formation. Together, these results show that, rather than simply accelerating the thermally driven phase segregation observed at lower pressures, atmospheric-pressure H$_2$:N$_2$ gas redirects restructuring toward faceting and nanovoid formation through a gas-mediated Kirkendall-type mechanism.

Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a policy-gradient RL framework that operates directly on the learned velocity fields and eliminates the need for the explicit computation of the score. OMatG-IRL leverages stochastic perturbations of the underlying generation dynamics preserving the baseline performance of the pretrained generative model while enabling exploration and policy-gradient estimation at inference time. Using OMatG-IRL, we present the first application of RL to crystal structure prediction (CSP). Our method enables effective reinforcement of an energy-based objective while preserving diversity through composition conditioning, and it achieves performance competitive with score-based RL approaches. Finally, we show that OMatG-IRL can learn time-dependent velocity-annealing schedules, enabling accurate CSP with order-of-magnitude improvements in sampling efficiency and, correspondingly, reduction in generation time. The OMatG-IRL code is included in a new release of the Open Materials Generation (OMatG) framework available at https://github.com/FERMat-ML/OMatG.

The geometry and binding energy of excitons, set by electron-hole wavefunction distributions, are fundamental factors that underpin their many-body interactions and determine optoelectronic properties of semiconductors. However, in typical solid-state systems, these quantities are fixed by material composition and structure. Here we introduce a polarizable interlayer exciton hosted in a two-dimensional tetralayer heterostructure whose dipole length, in-plane radius, and binding energy can be continuously programmed in situ over a wide range, enabling direct control over the nature of excitonic many-body phase transitions. An out-of-plane electric field redistributes layer-hybridized electron-hole wavefunctions, realizing in situ control of exciton geometry through a strong quadratic Stark response. This tunability further regulates the nature of interaction-driven Mott transition, transforming it from gradual to abrupt. Our results establish exciton geometry as a continuously tunable materials parameter, opening routes to exciton-based quantum phase-transition simulators and guiding the design of emergent optoelectronic functionalities from programmable excitonic materials.

Post-wildfire mudflows are increasingly hazardous due to the prevalence of wildfires, including those on the wildland-urban interface. Upon burning, soil on the surface or immediately beneath becomes hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded soil blanket the downslope, leading to catastrophic debris flows. Soil hydrophobicity enhances erosion, resulting in post-wildfire debris flows that differ from natural mudflows in intensity, duration, and destructiveness. Thus, it is crucial to understand the timing and conditions of debris-flow onset, driven by the coupled effects of critical parameters: varying rain intensities (RI), slope gradients, water-entry values, and grain sizes (D50). Machine Learning (ML) techniques have become increasingly valuable in geotechnical engineering due to their ability to model complex systems without predefined assumptions. This study applies multiple ML algorithms: multiple linear regression (MLR), logistic regression (LR), support vector classifier (SVC), K-means clustering, and principal component analysis (PCA) to predict and classify outcomes from laboratory experiments that model field conditions using a rain device on various soils in sloped flumes. While MLR effectively predicted total discharge, erosion predictions were less accurate, especially for coarse sand. LR and SVC achieved good accuracy in classifying failure outcomes, supported by clustering and dimensionality reduction. Sensitivity analysis revealed that fine sand is highly susceptible to erosion, particularly under low-intensity, long-duration rainfall. Results also show that the first 10 minutes of high-intensity rain are most critical for discharge and failure. These findings highlight the potential of ML for post-wildfire hazard assessment and emergency response planning.

In this paper, a carbon nanotube (CNT) based single-ion detector is proposed and its performance is evaluated with atomistic quantum transport models. The sensor can detect any ion type without molecule-specific functionalization and allows for continuous real-time ion monitoring. A single ion temporarily changes the operating principle of the sensor's CNT field-effect transistor into a resonant tunneling diode. The concrete device example of this paper showed a source-drain current increase of 5 orders of magnitude induced by a single ion.

We present two new interatomic potentials for platinum (Pt) in angular-dependent potential (ADP) and modified Tersoff (MT) formats. Both potentials have been trained on a reference database of first-principles calculations without using experimental data. The properties of Pt predicted by the ADP and MT potentials agree better with DFT calculations and experimental data than the potentials available in the literature. Future applications of the MT model to mixed-bonding metal-covalent systems are discussed.

Scale-invariant avalanches – with events of all sizes following power-law distributions – are considered critical. Above the upper critical dimension of four, the mean-field solution with a robust $3/2$ size exponent describes the dynamics. In two and three dimensions, spatial constraints yield smaller yet robust exponent values governed by universality classes. However, both earthquake data and experiments often show exponent values larger than $3/2$, challenging those theoretical arguments based on critical behavior. Through extensive simulations in the classical OFC earthquake model, here we show a clear transition from the theoretical expected behavior of a robust exponent value, to a regime of quasi-critical dynamics with larger than $3/2$ exponents that depend on dissipation. While the first critical regime exhibits an inherently memoryless behavior, both the transition and the second regime are driven by memory effects provoked by the growth of avalanches over the traces left by previous events, due to dissipative mechanisms. The system hovers at a distance $d_{cp}$ from the critical point, and accounting for a power-law distribution of $d_{cp}$, validated by susceptibility measurements, captures the transition. This framework provides a unified description of both critical and quasi-critical behavior, and thus of the full spectrum of scale-free dynamics observed in nature.

Quantum chaos in isolated quantum systems is intimately linked to thermalization and the rapid relaxation of observables. Although the spectral properties of the chaotic phase in the tilted Bose-Hubbard model have been well characterized, the corresponding dynamical signatures across the transition to regularity remain less explored . In this work, we investigate this transition by analyzing the time evolution of the survival probability, the single-site entanglement entropy, and the half-chain imbalance. Our results reveal a clear hierarchy in the sensitivity of these observables: the relaxation value of the entanglement entropy varies smoothly as a function of the Hamiltonian parameters across the chaos-regular transition, while the imbalance exhibits a more pronounced distinction. Most notably, the survival probability emerges as the most robust indicator of the transition between chaos and regularity. When appropriately scaled, all three observables converge onto a common behavior as a function of the Hamiltonian parameters for different numbers of sites and bosons,enabling a universal characterization of the transition between chaotic and regular dynamics.

In order to solidify the usefulness of metadynamics in studying nucleation of crystals from supercooled liquids, we provide a specific procedure to calculate nucleation free energy barriers. After a pedagogical review of the important elements of classical nucleation theory and how metadynamics is used to find nucleation free energy barriers, we explain the benefits of local collective variables over more common global collective variables. We show how a metadynamics free energy barrier must be carefully postprocessed so that classical nucleation theory can be applied to calculate nucleation rates. We apply our procedure to a Yukawa plasma and show that a particular physically-motivated fit to metadynamics data reproduces low-temperature reference data, justifying the usefulness of metadynamics to predict nucleation rates and the nucleation critical temperature.

In this commentary, we clarify that the Landauer formula is not limited to the phonon gas model. It is fundamentally more general and applies to both particle- and wave-based descriptions of phonons, provided the transmission function is well defined. In the harmonic regime, the phonon transmission function and the resulting Landauer expression for heat current are exact. They can be rigorously derived using the atomistic Green's function method, which treats phonons as waves and does not require phonon dispersion in the interface region. In short, the Landauer framework remains valid for ideal, disordered, and defective interfaces, as long as an appropriate transmission function is used.

Nanoconfined water plays a crucial role in nanofluidics, biology, and cutting-edge technologies. The process of melting water monolayers and quasi-two-dimensional confined water involves, as an intermediate stage, the hexatic phase–a state that lies between solid and liquid and is characterized by quasi-long-range orientational order and short-range translational order. However, the influence of hydrogen bond (HB) cooperativity in this process has not been thoroughly investigated. This gap hampers our understanding of the phase behavior of confined water and limits the accuracy of our models. To address this, we extend the water model developed by Franzese and Stanley, which explicitly includes many-body interactions (MBIs) of HBs. We distinguish the contributions of three-body and five-body HB-MBIs. Our Monte Carlo calculations in the isobaric-isothermal ensemble produces a detailed pressure-temperature phase diagram, revealing polymorphism and polyhexaticity: low-density square ice and high-density triangular ice are separated from the liquid phase by distinct hexatic phases. Three-body interactions notably promote crystallization and can destabilize the low-density hexatic phase, while cooperative five-body interactions help restore it, thus modifying the thermodynamic landscape. These findings demonstrate that HB-MBIs are key in determining the phase behavior of confined water, influencing phenomena such as the non-monotonic specific heat, maximum density lines, and the accessibility of the liquid-liquid critical point. Beyond advancing theoretical understanding, these results have wide-ranging implications for nanofluidics, interfacial science, and applications in biology, food technology, and pharmaceutics, where controlling water under confinement is essential.

Path-integral molecular dynamics simulations and electronic structure-based energy decomposition analysis (EDA) are employed to connect hydrogen bond (H-bond) strength, its asymmetry, and the total delocalization energy at the water/air interface to experimentally measurable observables, such as the reorientation dynamics and the sum-frequency generation (SFG) spectrum. Using SFG spectra for distinct layers at the water/air interface, we validate the accuracy of our simulations and report a red-shift from the interface to bulk and a strongly bonded water peak at around 3250 cm$^{-1}$ in the layer closest to bulk. The reorientation dynamics of water molecules slow down from the interface to bulk, which correlates with the SFG results. From our EDA based on absolutely localized molecular orbitals, we observe a strong decline in total delocalization energy from bulk to the interface, as well as a decline in the strength of the strongest donor and acceptor interactions. The asymmetry between the two strongest interactions similarly rises towards the interface, while the importance of interactions from the outer solvation shells is greatly diminished and is lower than previously reported. Finally, we find that the strength of the strongest H-bond donor/acceptor is best correlated with the local minimum of the autocorrelation function resembling the L2 band librational motions. Following that, we propose a simple yet quantitative relationship between H-bond strength and the short-time reorientation dynamics at the water/air interface that could potentially be extended to predict H-bond strength in other hydrophobic systems from experimentally obtainable observables.

We propose a data-driven heuristic for NP-hard Ising and Max-Cut optimization that learns the update rule of an iterative dynamical system. The method learns a shared, node-wise update rule that maps local interaction fields to spin updates, parameterized by a compact multilayer perceptron with a small number of parameters. Training is performed using a zeroth-order optimizer, since backpropagation through long, recurrent Ising-machine dynamics leads to unstable and poorly informative gradients. We call this approach a neural network parameterized Ising machine (NPIM). Despite its low parameter count, the learned dynamics recover effective algorithmic structure, including momentum-like behavior and time-varying schedules, enabling efficient search in highly non-convex energy landscapes. Across standard Ising and neural combinatorial optimization benchmarks, NPIM achieves competitive solution quality and time-to-solution relative to recent learning-based methods and strong classical Ising-machine heuristics.

Correlated microwave photon sources are key enablers for technologies in quantum-limited sensing, signal amplification and communication, but the reliance on millikelvin operating temperature limits their scalability for broader applications. Here, at room temperature, we demonstrate strong correlated microwave signals emitted from a hybrid magnon-photon platform. Different from traditional parametrically induced magnons with degenerate frequencies, we achieve non-degenerate excitations by coupling magnon modes simultaneously with two cavity photon modes. Through the magnon-photon interactions in the corresponding linear and nonlinear regimes, one input microwave photon splits into a pair of magnon polaritons that possess distinct frequencies but maintain strong inter-mode correlations. The nonlinear magnon polariton dynamics empowered by this new parametric platform brings both verified true randomness and robust multi-channel correlations, from which we construct a microwave communication experiment for noise resilient signal transmission with added security. This work establishes cavity magnonics as a versatile and compact platform for generating correlated multi-mode microwave signals, opening new avenues for applications in classical and quantum domains.

In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas-surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the $\alpha$-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction strengthens up to 900 K. While structural defects can increase TMAC via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains its performance at experimentally relevant defect densities.

We establish a direct connection between the Feynman-Vernon path integral formalism for open quantum systems and the Wiener path integral used in classical stochastic dynamics. By considering a generalized influence functional in the strong decoherence limit, we demonstrate that integrating over the quantum coherence length leads to a derivation of stochastic Langevin dynamics. Specifically, we show that the quantum Feynman measure transforms into the stochastic Wiener measure. Applying this framework to the Wigner function representation, we show that the system follows a stochastic path interpretable via classical probability theory. Finally, we address the inverse problem: constructing an equivalent quantum influence functional from a given classical Langevin equation.

The thermodynamic stability of inorganic solids spans a vast compositional space, yet materials scientists have long organized their intuition around a manageable number of materials families. Here we show that this organization has a precise geometric basis. The formation-energy convex hull of all inorganic compounds from the Materials Project, spanning 92-dimensional elemental composition space, is captured to near DFT accuracy by a polyhedron with only seven facets. Each facet corresponds to a family of materials sharing similar chemical potentials. This low-dimensional structure is not merely an economical description of energies: without retraining or structural input, the same framework reproduces trends in DFT-calculated defect energies and elemental spatial correlations in high-entropy nanoparticles. These results reveal that a small number of material families, corresponding to geometric features of composition-energy space, govern bulk stability, defect energetics, and elemental mixing, and provide a unified, interpretable framework for rapid screening across diverse materials systems.

Topological insulators (TI) are highly attractive platforms for next-generation optoelectronic and photonic devices. Spin-momentum locking of topological surface states enhances their nonlinear optical responses and sensitivities, especially to circularly polarized light. Until now, theoretical investigations of nonlinear responses in TIs have been limited to microscopic calculations on analytical continuum models, or leveraging density functional theory based Hamiltonians. In this work, we expand beyond these two approaches by employing a nonlinear Kubo formalism to calculate second-order nonlinear optical conductivity in a slab geometry using symmetry informed tight binding models that accurately reproduce the conduction, valence and topological surface bands in Bi$_2$Se$_3$. Our methodology enables us to study the layer resolved contribution to injection currents coupled to the incident electric field. We demonstrate that our technique can reveal how device engineering modifies elements of the nonlinear optical response such as the circular {and linear} photogalvanic effects by breaking inversion and time-reversal symmetry. {In particular, magnetization-induced symmetry breaking enables nonlinear conductivity tensor components (e.g., $\sigma_{xyz}$) that are normally forbidden by symmetry, thereby directly modifying the circular photogalvanic effect.} We find, in line with experiments, that the photogalvanic current is sensitive to field effects, Fermi level energy, gate voltage and the energy of incident light. Our computed midwave infrared (mid-IR) responsivity $R \approx 0.170~\mathrm{\mu A/W}$ is comparable to reported TI and intrinsic 2D-material photodetectors. We simulate experimentally unexplored methods to modify the circular photogalvanic effect such as proximitizing a magnetic field to one of the TI surface materials, suggesting a mechanism for optoelectronic tuning.

Fermionic non-Gaussianity quantifies a quantum state's deviation from a classically tractable free-fermionic description, constituting a necessary resource for computational quantum advantage. Here we use fermionic antiflatness (FAF) to measure this deviation across ergodic and many-body localized (MBL) regimes. We focus on the paradigmatic disordered spin-$1\!/2$ XXZ chain and its impurity variant with local interactions. Across highly excited eigenstates, FAF evolves from typical-state behavior at weak disorder to strongly suppressed values deep in the MBL regime, with volume-law scaling in the XXZ chain and an area-law bound in the impurity setting. Rare long-range cat-like eigenstates exhibit a pronounced enhancement of FAF, making it a sensitive diagnostic of mechanisms proposed to destabilize MBL. Starting from product states, we find that in the MBL regime FAF grows slowly in time, approaching saturation via a power-law relaxation. Overall, our results show that MBL suppresses fermionic non-Gaussianity, and the associated complexity beyond free fermions, while ergodicity restores it, motivating explorations of fermionic non-Gaussianity in other ergodicity-breaking phenomena.

Polarization analysis in x-ray spectroscopy provides an orientation dependent sensitivity to local bonding environments. For a cluster of atoms, polarization sensitivity is most often discussed through the lens of point group symmetries. However, this is a discrete, qualitative method of classifying clusters, and it does little to indicate the degree of spectral anisotropy. Here we adopt a random forest model for quantitative prediction of spectral anisotropy. Its input relies on simplified local geometric and chemical information that can be obtained from any crystal structure file. The model is trained on over 10,000 experimentally realized transition metal structures from the Materials Project, with the target being VtC-XES calculated using the real space Green's function code FEFF. We find that the model can strongly predict the degree of spectral anisotropy, with the primary factors being derived from the spatial moments of ligands in a cluster.

In the present work, we revisit the problem of the inhomogeneous electron gas under the influence of a weak external potential, which allows us to calculate the gradient corrections to the density functional within linear response, an approach known as the gradient expansion approximation. To obtain the exchange ($b_x$) and correlation ($b_{c}$) contributions to the coefficient $b_{xc}$, i.e., to the prefactor of the $q^2$ term of the proper-polarization function, we revisited all the previous calculations and expose misconceptions which led to incorrect conclusions. We used various ways to apply a necessary regularization to the singular Coulomb interaction potential. We found that the separate exchange ($b_x$) and correlation ($b_c$) contributions to the coefficient $b_{xc}$ have regularization-scheme dependent values even though the regulator is set to zero at the end of the calculation. This implies that it is impossible to define such a separation meaningfully. On the contrary, we found that when the regulator is set to zero at the end of the calculation, the combination $b_{xc}$ is regularization-scheme independent and, thus, has a unique value. We conclude that it is incorrect to separate those two terms when constructing a generalized-gradient-approximation (GGA) contribution to the density functional. This appears to be a common approach in most popular GGA functionals, where various constraints are applied to each contribution separately.

Open quantum systems can be described by unraveling Lindblad master equations into ensembles of quantum trajectories. Here we investigate how the complexity of such trajectories is affected by conservation laws and other dynamical constraints of the underlying Lindblad evolution. We characterize this complexity using a data-driven approach based on the intrinsic dimension, defined as the minimal number of variables required to encode the information contained in a data set. Applying this framework to several systems, including dissipative variants of the quantum top and of the XXZ chain, we find that the intrinsic dimension is sensitive to the structure of their dynamics. The Lindblad evolution in these systems is typically chaotic; in particular, we report new signatures of autonomous chaos in the quantum top. At specific parameter values, however, additional constraints arise: the dynamics becomes integrable, exhibits Hilbert-space fragmentation, or develops a closed BBGKY hierarchy, leading to pronounced minima in the intrinsic dimension. Our approach results in an unsupervised probe of the complexity of dissipative quantum systems that is sensitive to chaos and ergodicity breaking phenomena beyond the initial transient regime.