Papers

Inspired by the concept of a generalized materials genome, we introduce the notions of lattice gene and lattice genome for crystalline materials. A lattice gene is a compact representation of the local crystalline structure obtained by encoding the Kikuchi diffraction patterns with a variational autoencoder. We show that this representation satisfies key criteria for a materials gene: compactness, experimental accessibility, existence of a distance metric reflecting structural similarity, and sufficient information content for reconstructing the original diffraction patterns. The lattice genome is the spatially resolved collection of lattice genes across a representative area mapped by electron backscatter diffraction (EBSD), which captures mesoscale heterogeneity that ultimately controls properties. We demonstrate three applications of the lattice genome: (i) latent component maps that visualize grain-scale and intra-grain heterogeneities, (ii) domain segmentation based on distance and angle metrics in the latent space, and (iii) kernel and domain latent vector spreads that quantify intragranular heterogeneity as high-dimensional analogs of kernel average misorientation and grain orientation spread. All three tools are validated on microstructures of additively manufactured and wrought Ni-base superalloys in as-built and recrystallized conditions.

Here we present ARTGEL, an actively regulated-temperature gel electrophoresis platform designed for long-duration experiments under independently controlled thermal and electrical conditions. ARTGEL combines thermoelectric regulation of the gel temperature, a large heated and circulated buffer reservoir, and an automated electrode-wiping mechanism that stabilizes the voltage across the gel during runs exceeding 24 h. The platform was developed to address a limitation of conventional electrophoretic mobility shift assays, which are commonly used to analyze reversible biomolecular association but usually aim to suppress reaction during electrophoresis by dilution, competitors, or reduced temperature so that the gel reports a pre-equilibrated bulk solution. For temperature-sensitive systems, these strategies can alter the chemical state during loading and migration and obscure whether the measured band pattern reflects the original bulk sample or a re-equilibrated state inside the porous gel. Rather than attempting to quench reactions, ARTGEL enables electrophoresis to be performed at the same temperature as complementary bulk measurements, so that reversible association can be quantified directly in the gel and compared with matched measurements in solution. Using DNA origami assemblies, we show that ARTGEL preserves distinct temperature-dependent association states, resolves reaction-dependent distortions of migrating bands, and supports extraction of in-gel kinetic and thermodynamic parameters from reaction-diffusion-advection modeling.

Bayesian inverse problems constrained by state equations are often sampled in a full parameter-state space by penalising the residual, rather than in a reduced space where the state is eliminated. We show that these formulations are not automatically equivalent as posterior measures. For finite-dimensional discretisations of equality-constrained inverse problems, assume the state equation \(c(\theta,u)=0\) has a unique solution \(u=G(\theta)\) and nonsingular state Jacobian \(\D_u c\). The reduced posterior, its graph lift, and the zero-noise residual posterior are then distinct. A local change of variables shows that an uncorrected Gaussian residual penalty converges, after marginalisation over \(u\), to the reduced density multiplied by \(\abs{\det \D_u c(\theta,G(\theta))}^{-1}\). Thus algebraically equivalent residuals can define the same feasible set but different limiting posteriors. We derive determinant corrections for unweighted, weighted, and rescaled residual penalties that have the graph-lifted reduced posterior as their hard-constraint limit. The result separates feasibility from posterior calibration: driving the residual to zero is not sufficient for exact sampling of the graph-lifted reduced posterior unless the sampling or correction step targets the corresponding corrected density.

In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial distribution function (RDF). This approach improves the accuracy of fitting classical molecular dynamics interatomic potentials to density functional theory (DFT) data for both energies and forces while requiring fewer configurations than random selection. We demonstrate this improvement by fitting an embedded-atom method (EAM) potential for titanium, using various configurational sizes from an initial set of 1800 configurations. The k-means clustering consistently achieves better precision and lower standard deviations for a smaller number of configurations than random selection. The results also suggest that only about 30 configurations are sufficient to obtain an EAM model that describes well the full set of 1800 configurations in terms of energies and forces. Additionally, t-distributed stochastic neighbor embedding (t-SNE) method was used to reduce the configuration fingerprints into 2D space, and it revealed an overlap between two configuration subsets with and without Ti vacancy, indicating similar atomic environments. This similarity is captured by k-means clustering but not by random selection. Furthermore, when the overlapping configurations with vacancies were excluded from the k-means algorithm and used only as a test set, their energy and force predictions showed similar precision to those when they were included. This indicates that the overlapping configurations in the 2D t-SNE space indeed imply potential information redundancy among the atomistic configurations.

Thin-film strontium titanate (SrTiO$_3$, STO) layers grown on silicon require accurate stoichiometry and single-crystalline order to exploit their functional properties optimally. Oxide molecular beam epitaxy can provide an epitaxial interface, but suffers from source oxidation and resulting flux instabilities, yielding only a narrow growth process window for cationic stoichiometry control. Here, we investigate post-growth annealing in oxygen as a pathway to drive the STO layer toward stoichiometry in intentionally Sr-rich epitaxial STO films on silicon (001). Annealing over a broad temperature range revealed two distinct Sr-segregation mechanisms. Below 800 {\deg}C, excess Sr segregates toward the surface, forming SrO outgrowths that progressively sublimate at elevated temperatures. Above 800 {\deg}C, a second mechanism dominates: Sr accumulates within the interfacial SiO$_2$ layer formed by oxygen diffusion at the STO/Si interface. Together, these mechanisms effectively remove excess Sr from the STO lattice, yielding a more stoichiometric perovskite layer. Our results demonstrate that growing slightly Sr-rich STO templates followed by controlled annealing provides a practical route to improve crystalline quality, offering a scalable strategy for high-quality STO integration on silicon.

Measured specific heats often exceed Debye's T^3-law, even in high-purity single crystals. Analogous excess energy fluctuations are found in molecular dynamics (MD) simulations of crystals with no defects. Here, a theory is developed for the fluctuations based on rapid modulation of the ground-state energy due to the motion of next-nearest-neighbor atoms. Crucially, the modulations must be adiabatic, with time- and phase-averaging before thermal averaging, consistent with evidence from the simulations and multiple experimental techniques showing that localized excitations are decoupled from the heat bath. Emergent nonextensivity is interpreted by analogy with anomalous diffusion. The theory modifies the standard relation between energy fluctuations and specific heat, giving good agreement with the simulations and new insight into many measurements. The theory may also provide a basis for understanding excess specific heat in amorphous materials and anomalous noise in quantum devices.

A computational scheme, which utilizes neural networks, was developed to predict properties of molecular-based materials from chemical structures. The method uses a set of simple algorithms to encode the structure and composition of organic molecules directly into numerical vectors, which is used as input for neural networks. Backpropagation type neural networks are then used to correlate these numeric inputs with a set of desired properties. Calculated results for a series of hydrocarbons, hydrofluorocarbons, and crown ethers demonstrate average accuracies of 0.2-8.1% with maximum deviations of 16-20% for a broad range of thermodynamic, physical, and physical-chemical characteristics (heat capacity, enthalpy, heat of evaporation, boiling point, density, refractive index, stability constants, etc.). In addition, a number of physical and mechanical properties were estimated for polymeric materials and compared with regression analysis. Based on the neural network capabilities of formulating accurate quantitative structure property relationships, a technique called computational synthesis is suggested for performing materials design.

We study the cumulants of work in a slow isothermal process for gapped systems. Using the Martin-Siggia-Rose-De Dominicis-Janssen (MSRDJ) formalism and the properties of connected correlation functions, we show that in this process, the $n$-th cumulant of work scales as $1/T^{n-1}$ , where $T$ is the time duration. This result holds generally for arbitrary smooth protocols. Furthermore, we derive the coefficients of the cumulants from equilibrium properties. These coefficients are found to be relevant to thermodynamic geometric tensors.

We study a Mach-Zehnder interferometer (MZI)-based optoelectronic magnonic parametric oscillator (OEMPO) incorporating a YIG-loaded magnonic branch and a tunable phase-shifter branch, enabling systematic investigation of adaptive interferometric oscillator dynamics under distributed phase perturbations. Through analysis of nondegenerate OEPO mode pairs and frequency-pulling behavior, the loop free spectral range (FSR) and effective delay time were quantitatively extracted. Despite the nominally frequency-pinned parametric operation, weak frequency pulling and OEPO mode softening were observed, revealing an additional adaptive interferometric degree of freedom introduced by the MZI architecture. By comparing local and global sampling configurations, we demonstrate that the YIG branch behaves predominantly as a local dispersive resonant subsystem governed by the complex magnonic susceptibility, whereas the phase-shifter branch primarily controls the global interferometric redistribution geometry. Nevertheless, coherent recombination and adaptive regeneration within the loop produce finite cross-coupling between the two branches, resulting in partially synchronized interferometric dynamics and branch-dependent adaptive redistribution. Quantitative complex-Lorentzian analysis further reveals substantial phase-to-amplitude conversion and distinct differences between the OEO and OEPO regimes: the phase-pinned OEPO favors strongly dispersive local YIG response, while the frequency-adaptive OEO exhibits more mixed absorptive–dispersive behavior due to spectral relaxation through frequency pulling. Broadly, the present platform establishes a versatile framework for exploring adaptive nonlinear interferometric physics, coherent phase redistribution, and branch-dependent synchronization phenomena in hybrid magnonic-photonic oscillator systems.

Multicomponent vapor-deposited organic glasses are essential in organic electronic applications, but achieving controlled component segregation at the nano- and mesoscale remains a challenge, hindering the rational development of high-performance devices. In this study, we investigate binary organic semiconductor mixtures of TPD (N,N'-Bis(3-methylphenyl)-N,N'-diphenylbenzidine) and TCTA (Tris(4-carbazoyl-9-ylphenyl)amine). Despite being miscible in the bulk liquid state, the co-deposited glassy films of these two organic semiconductors exhibit a range of segregation behaviors, from homogenous to clearly phase-separated structures. We employed differential scanning calorimetry and resonant soft X-ray scattering (RSoXS) to study the component segregation behavior and used the National Institute of Standards and Technology RSoXS Simulation Suite, paired with Atomic Force Microscopy, to interpret the energy-dependent RSoXS spectra. Our results indicate that component segregation in co-deposited TPD-TCTA films is due to a kinetically-arrested nucleation-and-growth mechanism, in contrast to the segregation mechanism of a previously reported TPD-DO37 (disperse orange 37) mixture which is strongly immiscible in bulk. This work provides a demonstration of tunable molecular aggregation in organic semiconductor glasses, enabling access to a continuum of morphologies from homogeneously mixed to segregated phases.

We introduce a unified theoretical framework for single-plasmon transport in one-dimensional nanowires, bridging the quantized electromagnetic Green's tensor formalism with effective non-Hermitian Hamiltonian models. This approach naturally incorporates propagating surface plasmon polaritons, high-order modes dissipative channels, and intrinsic losses. We investigate both the stationary regime and the spatio-temporal dynamics of a single-plasmon pulse travelling through an atomic chain coupled to a dispersive nanowire. We analyze modal contributions to reflection and transmission spectra for quantum emitter coupled to a silver nanowire, a configuration proposed as a single-plasmon transistor, and we demonstrate that optimized multi-emitter systems offer significant advantages. In case of one quantum emitter coupled to a silver nanowire at telecom wavelengths, we predict a single-plasmon transmittivity down to 7% under realistic conditions, and an atomic qubit population of 12%. Extension to multi-emitter systems using a Löwdin orthogonalization procedure enables a consistent treatment of collective interactions. We show that optimized positioning with just five emitters enhances plasmon modulation, achieving a transmittivity of 2% but also reduces coupling losses to one-third compared to the single-emitter case. Our results establish a robust foundation for analyzing and designing plasmonic waveguide quantum electrodynamics systems.

We consider the $SO(d)$ and $O(d)$ invariant sectors of the bosonic $d$-matrix harmonic oscillator with $U(N)$ gauge symmetry. The micro-canonical degeneracy $\mathcal{Z}( N , d , k )$ for fixed energy $k$ is expressed as a pairing between an $N$-dependent vector and a $d$-dependent vector in the space of partitions of the integer $k$. This pairing formula is derived by counting invariant words in multi-matrix variables $X^i_{j,a}$, using properties of Clebsch-Gordan multiplicities (Kronecker coefficients) for the symmetric group $S_k$, Schur-Weyl duality and harmonic analysis on the homogeneous space $U(d)/SO(d)$. Analytic formulae for large $N$ and $k$ with $ k \le N $ are obtained using group integrals over $U(N)$ and $SO(d)$ (or $ O(d)$). The micro-canonical heat capacity in this regime is negative and turns positive, at a critical value $k_{\rm crit}$, due to finite $N$ modifications to the counting, thus forming what we denote as a characteristic caloric fold in the $ E $ versus $T$ curve. Data from the pairing formula is well fitted by $k_{\rm crit} \sim { N^2 \over 4 }$ for small values of $d$. A derivation of this large $N$ formula is given using a matrix model approximation and semi-classical analysis of the eigenvalue density. The large $N,d$ limit of the degeneracies reveals a key role for ribbon graph combinatorics. The caloric fold is also notably a property of black hole thermodynamics in anti-de-Sitter spaces. We propose the spherically symmetric \(SO(d)\) and \(O(d)\) invariant sectors of \(d\)-matrix quantum mechanics as tractable matrix systems for capturing key features of dual descriptions of black-hole thermodynamics.

The planar Hall effect (PHE) is an anisotropic magnetotransport response generated by coplanar electric and magnetic fields. We investigate the PHE in single- and bilayer two-dimensional electron gases (2DEGs) with Rashba spin-orbit coupling and identify two distinct mechanisms: Zeeman coupling and a band geometric channel. In the Zeeman channel, an in-plane magnetic field distorts the Rashba spin-orbit-coupled band dispersion and generates anisotropic carrier velocities, producing a finite PHE. In an asymmetric Rashba bilayer, interlayer electronic delocalization generates finite planar Berry curvature and orbital magnetic moment components, giving rise to a band geometric PHE channel. Using semiclassical Boltzmann transport theory, we calculate the chemical potential and angular dependence of the planar Hall conductivity for both mechanisms. Symmetry analysis shows that the leading response is quadratic in the magnetic field and exhibits the characteristic $\pi$-periodic angular dependence. For the parameter regime considered here, the Zeeman-induced contribution dominates, while the band geometric channel provides a distinct symmetry-allowed contribution unique to asymmetric Rashba bilayers. Our results reveal microscopic origins of anisotropic magnetotransport in spin-orbit-coupled two-dimensional materials.

Ecological communities with structured interactions exhibit collective phenomena such as percolation and clustering of occupied sites. While these effects have been documented in experiments and simulations, systematic analytical understanding has remained limited. In this paper, we develop a dynamical theory of these phenomena for competitive ecological systems defined on random interaction graphs. We introduce a discrete version of the generalized Lotka-Volterra model that preserves key macroscopic features of continuous ecological dynamics while enabling analytical treatment. Within this framework, we characterize the emergence of percolating clusters and describe the spatial organization of surviving sites. Our analysis uncovers which equilibria can be reached by the dynamics and shows how this dynamical accessibility governs the onset of clustering and percolation. In doing so, our framework complements classical Lotka-Volterra theory by providing a dynamical perspective on the collective organization of structured communities.

Successfully performing direct numerical simulations of polymeric flows remains a major challenge in computational fluid mechanics. In addition to the velocity field, such simulations must resolve polymeric degrees of freedom, often expressed via the conformation tensor, $\mathbf{c}$, which captures the local stretch of polymer molecules. A key difficulty here lies in maintaining the physical requirement $\mathrm{Tr}\, \mathbf{c}>3$, which is not explicitly enforced by the governing equations. Consequently, simulations initiated from physical conditions may silently drift into unphysical states with $\mathrm{Tr}\, \mathbf{c}<0$, indicating a loss of positive-definiteness of the conformation tensor. Existing numerical methods to prevent this are costly, making direct numerical simulations of chaotic polymer flows, such as elastic turbulence, heavily reliant on high-performance computing. Here, we ask whether simulations that violate $\mathrm{Tr}\, \mathbf{c}>3$ can still yield meaningful physical insight into the underlying dynamics. We simulate a model dilute polymer solution driven through a plane channel at low Reynolds number and observe the transition to elastic turbulence. Our simulations exhibit two threshold resolutions: below the first, they become numerically unstable and exhibit a finite-time blow-up; above the second, they maintain positive-definiteness. In between, simulations remain stable and chaotic despite local violations of $\mathrm{Tr}\, \mathbf{c}>3$. Surprisingly, these violations do not affect mid-plane statistics of velocity, its gradients, or polymer stretch, which match results from fully positive-definite simulations. This suggests that resolving flow structures or key flow statistics may not require the extreme resolutions needed to preserve positive-definiteness, potentially lowering computational barriers for studying elastic turbulence.

Recently, a number of spin chain models have been discovered that are solvable via hidden free-fermionic structures, going beyond the Jordan-Wigner paradigm. However, all examples in the literature displayed degeneracies that grow exponentially with the volume and that are homogeneous in the spectrum (identical degeneracies for all energy levels). In this note we present a model that can be solved by “free fermions in disguise” (FFD), such that the spectrum is free from exponential degeneracies for generic coupling constants. The model can be seen as a particular perturbation of two Ising chains. Alternatively, it can be realized as an interpolation between a standard Jordan-Wigner solvable chain and the original FFD model of Fendley. We used ChatGPT Pro 5.4 and 5.5 as a research assistant; in the Supplemental Material we provide details about the collaboration between the AI and the human author.

We present an $\textit{ab initio}$ framework for predicting resonant inelastic X-ray scattering (RIXS) in optically pumped materials. Our methodology is based on the Kramers-Heisenberg formula for the double-differential cross section formulated using the results of the Bethe-Salpeter equation (BSE) from many-body perturbation theory. To extend this approach to the time domain, we incorporate non-equilibrium charge-carrier distributions obtained from real-time, time-dependent density-functional theory (RT-TDDFT). Generalizing the RIXS implementation with respect to arbitrary polarizations, allows us to consider different orientations of incoming and outgoing light. We demonstrate our method's capabilities by studying RIXS at the K-edge of graphite for various non-equilibrium charge-carrier distributions, representing different delay times after optical pumping. Our results reveal angular dependencies in $\pi$- and $\sigma$-orbital-derived spectral regions, in good agreement with experiment.

Traditionally, atomistic simulation has been constrained by the computational scaling limits of ab initio methods and the parameterization overhead of empirical force fields. The recent emergence of universal machine learning interatomic potentials has significantly mitigated these bottlenecks, offering near-quantum accuracy and generalizability across diverse chemical spaces at a fraction of the computational cost. However, this shift has relocated the bottleneck to the human dimension: time-consuming mechanical processes, such as input preparation and data analysis, now dominate the research lifecycle. We introduce Paimon, a Platform for Agentic Integration in Materials Optimization and Nanoscale-simulations. Through hundreds of trials on an expert-level liquid electrolyte simulation, we show that Paimon substantially improves the reliability of agentic workflows by suppressing silent errors: plausible yet physically incorrect results. Paimon accommodates a new capability without modifying the agents already present, which we demonstrate by integrating an externally developed scientific agent. As an agent harness for atomistic simulations, Paimon affords researchers a continuous, science-centric workflow throughout the entire simulation lifecycle.

Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation. This limitation makes defect classification inherently ambiguous, as similar contrasts can arise from different materials or imaging conditions. Here we develop a context-aware learning framework that integrates image-derived contrast with metadata describing composition, beam energy, and detector geometry. Using a systematically constructed dataset of ~55 million simulated patches spanning 576 cases across 96 doped monolayer transition-metal dichalcogenides, we show that conditioning on contextual variables transforms defect classification from an ill-posed image-only task into a well-posed, physically grounded problem. The framework achieves over 98% accuracy on simulations and near-human agreement on experimental data, with a 94% reduction in posterior entropy. By emphasizing contextual grounding over architectural complexity, this approach links experimental image contrast to the underlying chemical and imaging conditions, supporting physically grounded defect assignments and a general pathway toward multimodal AI models for autonomous materials characterization.

The palladium-hydrogen system is a prototype for hydrogen-metal interactions and underpins technologies such as hydrogen storage, catalysis and purification. Yet its nanoscale behaviour – where surface and interface energetics, elastic coherency strain and size-dependent thermodynamics govern phase separation – has eluded accurate atomistic simulation. Empirical potentials misrepresent the energetics of interstitial hydrogen, while existing machine-learning models are restricted to bulk phases at low-hydrogen environments. Here we introduce an atomic cluster expansion (ACE) for Pd-H that reproduces formation energies, phonon spectra, elastic constants, hydrogen migration barriers and surface adsorption with near-DFT accuracy, benchmarked directly against neutron-scattering, high-pressure and lattice-expansion experiments. Its near-linear scaling and CPU efficiency make molecular dynamics of PdH$_x$ nanoparticles exceeding 28,000 atoms ($\sim$12 nm in diameter) tractable over nanosecond timescales. These simulations resolve, at the atomic scale, the kinetic separation of $\alpha$- and $\beta$-PdH$_x$ into a core-shell architecture, reproduce the experimentally observed size dependence of the lattice parameter, and uncover a pronounced hydrogen-induced lowering of the nanoparticle melting temperature. The potential brings experimentally relevant scales of metal-hydride dynamics within quantitative reach.

Despite the wealth of experimental observations on Raman scattering in magnetic materials, the underlying selection rules have remained largely unexplored. In this work, we use Onsager reciprocity relation, other than the conventional corepresentation method, to deal with the mathematical structures of Raman tensors in magnetic groups. Using this approach, we generate Raman tensor tables for all magnetic point groups, and present a comprehensive understanding of the Raman selection rules in magnetic materials with direct product representations method. Our theoretical and numerical results match previous experiments well, and resolve a recent puzzle in the Raman spectroscopy of CrSBr. Moreover, we identify a common but overlooked phenomenon: the magneto-Raman vector can be orthogonal to the magnetic moment direction. Our method and associated Raman tensor tables will be helpful for the Raman studies in both experimental and theoretical domains.

Accurate modelling of diffraction peak shapes is essential for extracting microstructural information from nanocrystalline materials. Common-volume functions are widely used to describe finite-size and shape broadening in powder diffraction, but analytical expressions are available only for a limited set of ideal geometries. Here, we introduce a generalized Fourier-based framework in which crystal-domain shape deformation, lattice deformation, and relative shape-lattice misorientation are treated as independently refinable tensor operations within a unified formalism. The approach enables continuous affine transformations of both crystal shape and lattice base while preserving analytical evaluation of directional Fourier coefficients. As a result, complex particle shapes, anisotropic deformations, and arbitrary relative orientations between shape and lattice can be modelled within a single reciprocal- and real-space framework, including coupled shape-lattice transformations not accessible using conventional powder diffraction line-profile methods. The formalism can be applied to individual diffraction peaks, full powder patterns, and total-scattering shape corrections. Validation against virtual scattering experiment data demonstrates that crystal size, shape, lattice deformation, and relative shape-lattice orientation can be simultaneously recovered with high accuracy.

Fast, highly sensitive, and broadband detectors operating in the mid-to-far-infrared (MIR-FIR) spectral region are essential for applications ranging from astrophysics to time-resolved spectroscopy using laboratory-based sources and beamlines at Infrared Free-Electron Laser (IR-FEL) facilities. Conventional Lumped Element Kinetic Inductance Detectors (LEKID) achieve high optical efficiency using resonant quarter wavelength ({\lambda}/4) backshort cavities. However, this cavity-based approach is inherently narrowband and becomes optomechanically challenging at the mid to far infrared (MIR-FIR) wavelengths. Here, we present a completely backshort-free LEKID absorber architecture that achieves broadband absorption exceeding 50%. The meander absorbers were fabricated from a superconducting Ti-V alloy and characterized using IR radiation from the IR-FEL at RRCAT, India. By illuminating through the silicon substrate and matching the meander's sheet resistance to the silicon substrate's wave impedance, the front-side reflection is strongly suppressed. Simultaneously, the sub-wavelength periodicity of the dense meander inhibits propagating transmission into free space via evanescent wave confinement. This combined mechanism drives efficient Ohmic dissipation within the metallic meander, yielding an experimental absorption efficiency ranging from 50% to 90% across the 12.5-30 micrometer wavelength range. The cavity-free design greatly simplifies the fabrication of the focal plane array while providing the broadband response required for next-generation detectors. To assess the suitability of the Ti40V60 alloy as an active superconducting detector material, a test LEKID resonator was also designed, fabricated, and experimentally characterized.

We study energy transport between two quantum systems coupled through a random interaction. The central feature of our approach is to model the coupling as a Gaussian random matrix, which enables a simple and systematic perturbative expansion. In the large-$N$ limit, we derive explicit expressions for the energy transfer rate and heat conductance up to second order in the coupling strength. Using spectral methods and diagrammatic expansions, we obtain the leading- and next-to-leading-order contributions to the energy transfer rate. We illustrate our results through explicit calculations for Gaussian, constant, semicircular, and Gamma densities of states.

Reservoir engineering can stabilize states inaccessible to unitary dynamics. Directed particle-conserving dissipation creates Lindbladian skin states, where Pauli exclusion turns edge accumulation into a many-body density imbalance. In a monitored fermion chain with tunable hopping range, we identify, within a Gaussian trajectory approximation, two finite-size scaling regimes: short-range hopping is consistent with complete skin accumulation and area-law entanglement, whereas sufficiently long-range hopping produces a finite bulk tail and effective algebraic sub-volume-law entanglement. Dissipation and coherent hopping thus jointly control skin localization and quantum entanglement, highlighting their close interconnection.

We theoretically explore a phase transition between controlled and chaotic dynamics in a classical spin chain model subject to chaotic Hamiltonian dynamics and a contractive "control map", which alternate in time. The control map drives the system toward a target configuration that is an unstable fixed point under the chaotic dynamics. When the control is strong enough, the target configuration is the globally attracting stable fixed point of the dynamics; for weaker control, the many-body dynamics remains chaotic for almost all initial states. The phase transition between controlled and chaotic phases has a mixed character: As the transition is approached from the chaotic phase, the fraction of the spins that are far from the target configuration goes continuously to zero, and there are diverging spatial and temporal correlation lengths; however, the leading Lyapunov exponent is discontinuous across the transition, jumping from a positive value in the chaotic phase to a negative value in the controlled phase. We present evidence that this transition is in the same universality class as directed percolation in the presence of temporal randomness, a universality class for which we obtain results that are consistent with the dynamical Harris criterion but do not saturate the bound.

We present a combined X-ray diffraction and transmission electron microscopy study of superconducting FeSe/FeTe multilayers grown by molecular beam epitaxy on SrTiO$_3$(001) substrates. While X-ray diffraction confirms perfect in-plane epitaxial alignment between FeSe, FeTe, and the substrate, scanning transmission electron microscopy reveals a surprising lack of atomic registry at the FeSe/SrTiO$_3$ interface. Instead of adapting to the substrate lattice, FeSe retains its own in-plane lattice spacing. A periodic lateral shift between the atomic positions of FeSe and SrTiO$_3$ is observed, with a registry recurrence length that matches the lattice mismatch determined by X-ray diffraction. No misfit dislocations or other relaxation features are detected at the interface. This coexistence of directional alignment and registry-free growth suggests an unconventional regime of epitaxy in which crystallographic orientation is maintained without atomic matching. The findings offer insight into strain accommodation in layered systems and may have implications for interface engineering in Fe-based superconductors.

The intrinsic symmetry breaking in the breathing kagome lattice of layered Nb$_3$Cl$_8$ provides a unique mechanism for realizing electrically polar surfaces. In each monolayer, the trimerization of Nb atoms breaks inversion and mirror symmetries, generating an out-of-plane electric dipole. The AB-stacked $\alpha$ phase arranges adjacent layer dipoles antiferroelectrically, leaving the uncompensated surface polarization strictly governed by layer parity. Here, using atomic force microscopy operated in Kelvin probe force microscopy mode, we directly visualize layer-dependent polarization states in exfoliated Nb$_3$Cl$_8$ flakes and resolve a pronounced odd-even oscillation of the surface electrostatic potential. Beyond this parity-locked antiferroelectric order, we further identify intralayer polar domains in which local atomic reconstructions of the breathing kagome network reverse the out-of-plane dipole of the surface layer, producing ferroelectric-like stacking configurations. By interfacing monolayer MoSe$_2$ with Nb$_3$Cl$_8$, we demonstrate that these surface-polarization textures effectively modulate adjacent excitonic emission through domain-dependent interfacial band alignment and charge transfer. Our findings establish Nb$_3$Cl$_8$ as an intrinsic layer-polarized van der Waals platform and show that layer parity provides powerful structural degree of freedom for programming excitonic and optoelectronic responses at van der Waals interfaces.

We define a winding number within the Non-Bloch band theory framework that captures the emergence of magnon edge modes in a one-dimensional antiferromagnetic spin chain, even when the conventional Bloch winding number is trivial. Within linear spin-wave theory, magnon excitations are governed by a non-Hermitian dynamic matrix, despite the underlying Hamiltonian being Hermitian. The symmetry classification of this matrix yields a trivial bulk invariant, however, finite systems exhibit boundary-localized modes, signaling a breakdown of the conventional bulk-boundary correspondence. We further show that these edge modes can be controlled via boundary perturbations. By tuning the boundary potential, the modes can be driven into or out of the bulk spectrum. To resolve the bulk-boundary mismatch, we develop a non-Bloch framework based on a generalized Brillouin zone and a winding number that correctly predicts the presence of edge states. Our results establish boundary-controlled topological transitions that are experimentally accessible through local Zeeman fields or modified edge anisotropy in antiferromagnetic van der Waals nanostructures.

In soft condensed matter, curvature does more than simply distort an ordered medium: it helps select defect structures, redistribute elastic stress, bias chirality, and guide self-assembly. This review examines how curved, multiply connected, and knotted boundaries in liquid-crystal colloids and confined nematics generate topological defects and localized solitonic textures, and how these structures mediate interactions between mesoscale building blocks. We introduce a unifying framework based on genus, Euler characteristic, anchoring, and chirality, and use it to discuss spherical, handlebody, and boundary-bearing colloids, together with droplets and polymer-dispersed nematics of nontrivial topology. Particular emphasis is placed on the interplay of geometry and topology in determining boojums, disclination loops, hedgehog charges, and linked and knotted defect structures. We then turn to chiral systems hosting skyrmions, torons, hopfions, and related localized textures, highlighting how chirality and confinement stabilize three-dimensional topological states. Finally, we discuss how these concepts translate into design principles for controlled self-assembly, templating, and functional composite materials. More broadly, we argue that liquid-crystal colloids and confined nematics provide experimentally accessible model systems in which curvature, topology, and chirality can be harnessed as programmable tools for designing organized soft matter.