Papers

We study the dynamics of weakly deformed interfaces separating two stable phases, starting from the fluctuating hydrodynamics of the phase-separating fields. Using a well-chosen definition for the interface and the dynamical-action formalism to represent path probabilities, we derive the linear relaxation of the interface and the fluctuations around it for a large class of models. Our method applies to equilibrium dynamics, where it recovers and complements existing results, but also extends to their non-equilibrium counterparts. We explain how non-linear terms can be systematically computed and illustrate their derivations in the case of (active) model A. We highlight the danger of a popular ansatz used to derive interface dynamics, which was rigorously established in equilibrium but is uncontrolled for active field theories.

Open quantum systems can host dark or subradiant states whose decay is highly suppressed. While these states have been extensively studied in the few-excitation regime, their impact on the many-body dynamics remains largely unexplored. Here, we study a spin chain subject to correlated dissipation on neighboring sites, which admits a single-particle dark state at zero momentum. We show that the single-particle dark state qualitatively alters the many-body dynamics at long times, and identify its distinct universal behavior. While the zero-momentum mode is dark at the single-particle level, it decays slowly as $1/\log t$ as it becomes dressed by other modes through a dissipation-induced nonlinearity. We demonstrate that the momentum distribution takes a universal scaling form in $k\sqrt{t}$, and the total density decays as $1/\sqrt{t}\log t$. Our results further elucidate the origin of the conflicting results in recent works. Finally, we corroborate the analytics with matrix product state simulations and show that the same universal behavior persists for soft-core interactions, underscoring the universality of the emergent dynamics.

Monodromy defects describe a dynamical termination of topological symmetry operators, and are sourced by a localized background magnetic flux. We study their properties in gapped SPT phases and, by inflow, in gapless theories with an anomalous symmetry. The background flux acts as a source for the anomaly, impacting the definition of the monodromy defect, which can only be defined as a domain wall between the symmetry generator and a topological order induced by the anomaly. The topological dressing has several consequences, such as the presence of protected chiral edge modes on the defect's worldvolume, and the adiabatic pumping of gapless degrees of freedom bound to the localized flux. We verify our predictions in several examples, focusing on monodromy defects for anomalous chiral symmetries, both in the continuum and on the lattice.

Polaritons are a superposition of light and matter, that combine Strong Interferences (of light) with Weak Interactions (of excitons), making them WISI (Weakly-Interacting, Strongly-Interfering) particles. Their condensation is the main highlight of a field which occupies a unique position at the intersection of quantum optics, condensed matter physics and nonlinear dynamics of driven, dissipative systems. This chapter surveys selected theoretical concepts of polariton condensates' formation, coherence and dynamics, with an emphasis on their distinctions from their atomic counterparts and on points of ongoing controversy. We argue that linear and non-interacting effects are undervalued in polariton physics, and that a significant part of the phenomenology – including bosonic correlations and coherence buildup – can often be understood without invoking strong interactions or genuine quantum effects.

The classical Einstein-Langevin theory of Brownian motion assumes a memoryless thermal bath, establishing a universal fractal dimension of $d_v = 3/2$ for the velocity fluctuations of a particle. In this Letter, we demonstrate experimentally and theoretically that fluid-inertial memory effects fundamentally redefine the fractal scaling of these fluctuations. In analyzing highly resolved measurements of Brownian microspheres in liquids, we show that the non-Markovian hydrodynamic thermal noise establishes a distinct velocity fractal dimension of $d_v = 7/4$. Coupled with theoretical analysis of non-equilibrium short-time dynamics and the initial scaling of the velocity autocorrelation function, this result establishes the non-equilibrium universality class of Brownian motion in fluid media possessing a finite non-vanishing density.

Owing to the Chapman-Kolmogorov equation for Markovian dynamics,any equilibrium trajectory of a Brownian particle in a solvent fluid can be viewed as the superposition of an uncountable number of non-equilibrium states. This property permits the unraveling of fine details of fluid-particle interactions at microscales defined by its non-equilibrium properties from the analysis of a single Brownian trajectory and to connect them to the hydrodynamics of the solvent fluid, simply considering the lower-order (second) moments of particle position in trapped conditions. In this way, the acceleration due to thermal-hydrodynamic fluctuational forces is isolated from the other factors and the short-time displacement statistics is completely determined by the correlation properties of the fluctuational thermal-hydrodynamic force. This approach not only confirms the $t^{5/2}$-law obtained by Boynewicz et al. (2026), related to fluid inertial effects, but indicates that this scaling may be superseded by a $t^4$-scaling at very short times once the correlated nature of the stochastic forcings is taken into account. The latter result is related to the regularity properties of particle velocity realizations.

Altermagnets (AMs) have recently emerged as a distinct magnetic class bridging central features of ferromagnets (FMs) and antiferromagnets (AFMs), offering new opportunities for spin-based electronics. While they possess zero net magnetization like collinear AFMs, they simultaneously exhibit momentum-dependent spin splitting long thought exclusive to FMs. Despite intense theoretical interest, experimentally accessible materials hosting both altermagnetism and nontrivial band topology remain scarce. EuAgAs, crystallizing in space group $P6_3/mmc$, was previously identified via density functional theory (DFT) as a bulk altermagnetic Dirac semimetal. Contrary to these predictions, our neutron diffraction experiments reveal that the bulk ground state adopts a $\mathbf{q} = (0,0,\tfrac{1}{2})$ AFM structure with an in-plane $\uparrow\uparrow\downarrow\downarrow$ spin sequence. Systematic DFT calculations, however, uncover a remarkable near-degeneracy among competing magnetic orders: the FM and AM configurations lie only $0.11$ and $0.40~\text{meV/f.u.}$ above the AFM ground state, respectively. We further show that while a simple Heisenberg model favors a spin-spiral ground state, the inclusion of non-Heisenberg biquadratic coupling stabilizes the observed commensurate AFM phase. This near-degeneracy renders the magnetic state highly tunable, with DFT predicting a transition to the altermagnetic phase under hydrostatic pressure at approximately $14 \text{ GPa}$, establishing EuAgAs as a controllable platform for accessing topological altermagnetism.

We theoretically investigate acoustic spin resonance in a spatially homogeneous spinor polariton condensate. A longitudinal acoustic wave generates a time-periodic strain-induced effective magnetic field acting on the condensate pseudospin. When this field is transverse to the static in-plane linear-polarization splitting, it resonantly drives polarization oscillations. We show that spin-dependent interactions shift the resonance and produce nonlinear line shapes, while gain, reservoir dynamics, and spin relaxation make the response dissipative and history-dependent, producing amplitude hysteresis. In the presence of lifetime anisotropy, the condensate can develop a bifurcated stationary state with finite circular polarization, and a resonant acoustic drive can switch between the corresponding out-of-plane branches. A Zeeman splitting provides an additional conservative knob for tuning the resonance frequency. Our results identify coherent acoustic driving as a route to resonant, nonlinear, and switchable control of polariton pseudospin dynamics.

Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials.

Motivated by recent observations of integer and fractional Chern insulators in rhombohedral graphene stacks aligned with hexagonal boron nitride (hBN), we propose and study a model in which the moiré potential is defined by the pattern of layer-shear strain fields produced by lattice relaxation in these heterostructures. Although these strain fields decrease exponentially with the number of layers, their imprints on electrons residing away from the contact layer are non-negligible. In the absence of a displacement field, lattice relaxation effects amplify the electronic differences among the two different stackings with hBN. These differences, although attenuated at the single-electron level, survive in the so-called moiré-distant regime and are further enhanced with the inclusion of electron interactions. We find that lattice relaxation plays a crucial role in flattening and isolating a valley-polarized Hartree-Fock electron band with $|C|=1$ Chern number. Our results challenge the conventional wisdom on moiré effects in these heterostructures by illustrating the intertwined effects of long-range Coulomb interactions and lattice relaxation, and opens the door to explore different regimes of twist angles and displacement fields for the search for topological states.

Atomic-scale modeling has advanced rapidly through integration of machine learning, yet a key bottleneck remains. Even with an accurate potential energy surface and a clear target material, we still lack a practical atomistic dynamics framework that can simulate how materials form under realistic synthesis and processing conditions. Many processing transformations are governed by rare events in non-idealized evolving environments, while direct molecular dynamics is limited by femtosecond timesteps and short accessible trajectories. Existing acceleration methods often require prior mechanistic knowledge, including reaction coordinates, collective variables, event tables, or pathway guesses, which is rarely available in real experiments. Here we present an E(3)-equivariant deep reinforcement learning framework that enables goal-directed pathway discovery without hand-crafted reaction coordinates. The framework introduces a complementary operating mode for atomistic simulation in which realistic, non-idealized environments can be addressed directly while retaining kinetic plausibility through barrier-aware rewards. As a challenging benchmark, we target silicon dry oxidation, where rare-event pathways in amorphous SiO2 are effectively inaccessible to conventional atomistic methods. We treat an O2 molecule as an agent that performs continuous rigid-body translations and rotations in a Si/a-SiO2 environment. The agent is trained with an episode-level objective that rewards verified O2 dissociation while preferring low effective activation barriers. We demonstrate that the learned policy discovers kinetically favorable O2 diffusion and dissociation pathways in a disordered Si/a-SiO2 environment, progressively improving success rate while reducing effective activation barriers over training. We also discuss how the approach can be generalized to other processing and synthesis problems.

Reduced dynamical descriptions for spatially extended systems in contact with a nonequilibrium medium give essential information about extensions of fluctuation-dissipation relations and, at the same time, are relevant for many natural systems. Here we study a chain of harmonic oscillators coupled to fast run-and-tumble particles, discretizing earlier work on an elastic string. We derive the effective Langevin dynamics, including the explicit discrete counterparts for the nonequilibrium-induced streaming term, friction coefficient, and noise amplitude. At sufficiently high bath persistence, the linear friction becomes negative, destabilizing the chain dynamics. However, numerical simulations confirm that the anti-damping is eventually arrested and results in a nonlinear stationary condition, whose properties are the central topic of this work. We characterize the long-time phase-space trajectories and their Rayleigh-like structure, identify multistage relaxation of the mean-squared displacement and mean-squared velocity, and establish a non-monotonic dependence of stationary fluctuations on bath persistence. We further analyze the stationary distributions of the displacement and velocity, as well as the spatial correlations, which develop damped oscillations in the strongly persistent regime. The transfer of activity thus transforms a passive harmonic chain into a self-sustained fluctuating medium with many-body Rayleigh-like dynamics, pulsating displacements, and persistent velocities.

We present a case study for how AI coding systems can be used to generate novel scientific hypotheses. We combine a generic coding agent (Google's AntiGravity) with an LLM-driven tree search algorithm (Empirical Research Assistance / ERA) to autonomously generate high-efficiency three-dimensional photovoltaic (3DPV) structures that overcome losses limiting flat solar panels at mid-latitudes. These structures operate by presenting favorable angles to the sun throughout the day, and for illustrative purposes we focus on optimizing performance for a single solar day. Our workflow begins by using AntiGravity to reproduce calculations \cite{bernardi2012solar} showing that 3DPV can have energy densities much higher than stationary flat PV panels. We use these initial designs as the starting point for large scale tree search, where we seek improved solutions and score them for their diurnal yield. The initial tree search leads to nominally more efficient solutions, yet they are caused by algorithmic reward hacking, arising from non-physical design features such as structurally levitating disconnected tiers and exploitations of the discretizations in the optics solver. To counteract this, we develop a workflow where the coding agent iteratively patches the physics engine with constraints to eliminate reward hacking. With reward-hacking eliminated, ERA discovers a series of designs with various constraints and improved performance, including optimal designs with different fixed collector areas, optimizing zenith tracking and avoiding self shadowing. Combining coding agents with tree search (ERA) provides a powerful platform for scientific discovery, for problems whose solutions can be empirically evaluated with a score function.

Transparent conducting oxides (TCOs) are essential for the optoelectronics industry, but there is a critical gap in cost-effective methods to rapidly deposit low sheet resistance, high transmittance films without damaging delicate materials, including emerging soft semiconductors like metal-halide perovskites. In this work, atmospheric pressure chemical vapor deposition (AP-CVD) is used to synthesise H:In2O3 films with 7.20+/-0.01 Ohm/sq sheet resistance (0.50+/-0.06 mOhm.cm resistivity) and transmittance up to 89% in the near-infrared (NIR), surpassing commercial sputter-deposited indium tin oxide. The growth rate is 40x higher than atomic layer deposition (ALD), and the AP-CVD films are fully processed under atmospheric conditions at only 140 C. Comparison of secondary ion mass spectrometry and time-of-flight elastic recoil detection analysis with changes in carrier concentration indicate that H dopants are introduced from the water oxidant. There is an increase in mobility form 40+/-10 cm2/Vs to 160+/-30 cm2/Vs when changing from O2 to H2O as the oxidant, which is attributed to H dopants passivating oxygen vacancies that act as carrier scattering centers. This work establishes AP-CVD as a promising method for manufacturing high figure-of-merit TCOs in a rapid, scalable and cost-effective manner, using mild growth conditions compatible with thermally-sensitive materials.

Space plasmas are generally characterized by non-Maxwellian distributions with suprathermal populations, as routinely revealed by in situ observations. Such departures from standard Maxwellian distributions can be understood as signatures of quasiequilibrium states, in which the distribution of the medium can be expressed as a continuous superposition of Maxwellian distributions, namely through superstatistics. Here, we construct macroscopic relations linking fluxes to their associated driving forces in such plasmas, where superstatistical effects enter the picture through the transport coefficients. After comparing the resulting superstatistical distributions with observed electron distributions in the solar wind, we turn to the kinetic response of quasiequilibrium plasmas and derive the corresponding transport coefficients, including the electric and thermal conductivities, the mobility, and the diffusion coefficient. We further extend the analysis to viscous plasmas and compute the shear and bulk viscosity coefficients. Overall, quasiequilibrium effects are found to systematically enhance the transport coefficients relative to their Maxwellian values. We quantify this enhancement for the three main universality classes of superstatistics, which are the most commonly encountered in experimental and observational situations, and interpret it as a consequence of the increased population of energetic particles in the non-Maxwellian tails.

A reason is discussed (may be not the only one) for why we do not see any superposition of macroscopic states in the real world. Under the general assumption that quantum macrostates are statistical ensembles of microstates, it is shown that any superposition of macrostates is reduced in a very short time by the unitary dynamics of the ordinary Schrödinger equation, deducing the Born rule without having to postulate it. In more detail, the macroscopic and microscopic degrees of freedom are decoupled in the Schrödinger equation, yielding an effective stochastic equation for the macroscopic variables, with the ensemble average of the microscopic amplitudes that acts as a self-generated internal white noise. The stochastic equation is shown to be a reducing Itô equation if some general causality conditions are met, predicting a very quick collapse of any macroscopic superposition upon formation, with probabilities which satisfy the Born rule. In the context of the von Neumann measurement scheme, the relevance of the result is discussed as a simple dynamical solution of the measurement problem.

Dicke superradiance by an ensemble of quantum emitters produces a collective burst of radiation, but no entanglement in the mixed state of the emitters. We show that adding a local kinetic constraint between the emitters generates extensive mixed-state entanglement, while otherwise preserving all key features of Dicke superradiance. Specifically, for any local Boolean constraint, we analytically derive a lower bound for the emission rate which implies a peak intensity $\propto N^2$ and a peak time $\propto (\log N)/N$ with number of spins $N$. This effect enables the superradiantly accelerated preparation of entangled dark states. Hereby, Hilbert-space fragmentation of the Dicke ladder leads to an exponentially branching decay tree that generates a hierarchy of dark states. Importantly, these disconnected manifolds include exponentially many long-range entangled singlet dark states. The explored kinetic constraints and superradiant dynamics can be realized in neutral-atom arrays coupled to an optical cavity, and we suggest a simple and accessible witness to detect the predicted mixed-state entanglement in such experiments. Moreover, we show that entanglement generation is robust against atomic decay and collective dephasing, and should be observable under recently reported experimental conditions. Our results, thereby, offer a general framework and experimentally viable approach for the dissipative engineering of entangled dark states enhanced by superradiance.

One-dimensional alternating particle systems are widely used to study interconnections between the hydrodynamics of blast waves in a gas-like medium and the Newtonian dynamics of its corpuscular constituents. We study the model in which point particles with masses $m,\mu, m,\mu,\dots, (m\geq\mu)$ are distributed on the positive half-line $\mathbb{R}_{+}$. Their dynamics are initiated by giving a positive velocity to the leftmost particle; in its course, the particles undergo elastic collisions. For this model with $m/\mu=2$, it has previously been established that the dynamics that start from random initial positions are consistent with predictions based on Euler's hydrodynamic equation. In particular, they have the following properties: (i) the position of the rightmost particle (shock front) evolves as $t^\delta$ with $\delta<1$; (ii) recoiled particles behind the front enter the negative half-axis; (iii) particles with locations $x\leq0$ move ballistically and eventually take over the total energy of the system. In this paper, we present numerical and analytical results for the dynamics of this model with nonrandom (typically equidistant) initial positions and various values of $m/\mu$. For $m/\mu=2$ and equidistant initial positions, our results qualitatively agree with those just mentioned. At the same time, we found an infinite family of numbers $\{\mathcal{M}_k\}$ such that, for $m/\mu=\mathcal{M}_k$, the hydrodynamic behavior mentioned changes drastically to the following. At each moment, only a single triplet $m,\mu, m$ is in motion, whereas all other particles are at rest. As a result, the shock front moves ballistically with an average velocity equal to the initial one. Such a `staggering domino-like' picture is obtained as an exact solution, which yields, in particular, explicit formulas for $\mathcal{M}_k$ and the particle velocities and positions.

Thermal conductivity is a fundamental material property that plays a crucial role in understanding the dynamics and evolution of planetary interiors. Despite its importance, the thermal conductivity of seifertite and pyrite-type SiO$_2$ remains unknown. Here, we calculate the lattice thermal conductivities of seifertite and pyrite-type SiO$_2$ using the Green-Kubo method based on molecular dynamics (MD) simulations driven by two machine learning potentials (MLPs) constructed from the SCAN and PBEsol exchange-correlation functionals, with $\textit{ab initio}$-level accuracy. To demonstrate our methodology, we also compute thermal conductivities using the phonon quasiparticle approach for comparison. Overall, the Green-Kubo method predicts up to 119 % higher thermal conductivity with a temperature dependence close to $T^{-1}$, as it fully captures diffusion-like phonons at high temperatures that are missed by the phonon quasiparticle approach. The 19 % reduction in thermal conductivity across the phase transition from seifertite to the pyrite-type phase suggests the potential formation of a thermally insulating layer in the mantle of super-Earths.

We consider the bulk $\phi^3$ deformation of the free boundary conformal field theory in the $\epsilon$ expansion. We determine the leading corrections to the scaling dimensions of boundary fundamental operators and some boundary operator expansion coefficients. Our procedure combines the conformal multiplet recombination with the boundary crossing symmetry. The results cover both the single field case and the multi-field case with $S_{N+1}$ global symmetry, which are associated with the Yang-Lee model and the $(N+1)$-state Potts model respectively. These semi-infinite models describe branched polymers, percolation, and spanning forest at a surface. We generalize these results to some higher derivative theories. In addition, we study the $\phi^{2n+1}$ theories with $n>1$, but only obtain some boundary operator expansion coefficients.

GB local stress is central to the initiation and evolution of long-term creep damage in polycrystalline superalloys. Owing to the high-dimensional nonlinear relationships between the GB stress response and multiple crystallographic, microstructural, and micromechanical characteristics, it remains challenging to identify the key characteristics governing GB stress and to elucidate their mechanisms of influence. Dislocation-climb-affected crystal-plasticity finite-element simulations of minimal grain clusters are combined with an integrated causation-guided machine-learning framework, in which mechanics-informed descriptors are analyzed by causation entropy (CE) to identify governing mechanisms and then distilled into a reduced-order regression form for interpretable prediction of GB normal stress. Unlike conventional black-box surrogate models, the proposed framework combines physically meaningful descriptors, CE-based mechanism identification, and an explicit compact regression form, enabling the dominant physical variables and their effects on GB normal stress to be directly interpreted. Among 18 physically motivated characteristics, the GB inclination angle, the slip transmission, the climb-related Schmid-type indicator, and the elastic-modulus mismatch are found to be dominant, revealing the coupled roles of interfacial geometry, crystallographic compatibility, creep stress relaxation, and micromechanical contrast. The identified characteristics hierarchy and functional representation remain effective under multiaxial loading and can be extended to tricrystal systems through physically interpretable nonlocal augmentation when a purely local description becomes insufficient, demonstrating strong physical consistency and robust generalizability across physical conditions.

High-entropy spinel oxides provide an excellent platform for investigating entropy-stabilized correlated systems with strong configurational disorder. In this work, we systematically study the temperature evolution of the structural and magnetic properties of Cr-based high-entropy spinels with compositions $(Mn_{0.2}Co_{0.2}Ni_{0.2}Cu_{0.2}Zn_{0.2})Cr_2O_4$ and $(Mg_{0.2}Co_{0.2}Ni_{0.2}Cu_{0.2}Zn_{0.2})Cr_2O_4$. Our results reveal that both systems crystallize in cubic structure with space group \textit{$Fd\overline{3}m$} at room temperature. Each system undergoes antiferromagnetic ordering below the Néel temperatures $ T_N$ = 49 K and 35 K, respectively. Neutron diffraction measurements confirm the emergence of long-range magnetic order with spiral spin arrangement. Both systems exhibit a structural phase transition from cubic \textit{$Fd\overline{3}m$} to orthorhombic Fddd symmetry at approximately 55 K and 85 K, respectively. Notably, despite the significant chemical disorder at the A site, both systems undergo transitions analogous to those observed in low entropy spinel systems. This behavior suggests that high configurational entropy may promote global structural stabilization despite local chemical disorder, thereby preserving long-range orderings and the characteristic symmetry-breaking transitions of the pristine spinel systems.

The experimental determination of eutectic points is a long-established and widely used technique, but it is generally only practical for systems with relatively low melting points. Many modern, promising materials, however, are ultra-refractory, with melting points exceeding 3000 K. For these systems, conventional melting experiments become prohibitively expensive and technically challenging. Advanced AI modeling can serve as a powerful precursor to guide successful experimentation in such cases. This work proposes a novel criterion for determining the eutectic point concentration in ultra-refractory alloys. The approach is verified using the Ti-B-C system - the most thoroughly studied three-component refractory system to date. The core of the algorithm is a machine-learning interatomic potential, based on a neural network, which achieves accuracy comparable to ab initio methods. Crucially, the algorithm operates effectively in the liquid phase, eliminating the need for information about the solid alloy's crystalline structure to estimate eutectic points.

Perovskite quantum dots (pQDs) are promising materials for optoelectronic and photocatalytic applications due to their unique optical properties. To enhance charge carrier extraction or injection donor/acceptor molecules can be tethered to the pQD. These molecules must strongly bind to the ionic surfaces of pQDs without compromising colloidal stability. These we achieve by using multifunctional ligands containing a quaternary ammonium binding group for strong pQDs surface attachment, a long tail group for colloidal stability, and a functional group near the pQD surface. Such pQDs with ferrocene-functionalized ligands show fast photoexcited hole transfer with near-unity efficiency. Density functional theory calculations reveal how ferrocene's molecular structure reorganizes following hole transfer, affecting its charge separation efficiency. This approach can also be extended to in photoexcited electron and energy transfer processes with pQDs. Therefore, this strategy offers a blueprint for creating efficient QD-molecular hybrids for applications like photocatalysis.

Antibody-based therapeutics-including antibody-drug conjugates (ADCs), bispecific antibodies, and novel formats-are reshaping oncology, yet key determinants of efficacy, safety, and manufacturability frequently emerge after conjugation and formulation. We argue that computational biophysics provides an underexploited framework to address this gap by connecting molecular interactions to biological outcomes. We highlight how molecular dynamics, coarse-grained simulations, and free energy calculations reveal how conjugation site, linker chemistry, and drug-antibody ratio reshape conformational landscapes. We emphasize structural coupling between antibody, linker, and payload, with implications for antigen binding, internalization, and developability. We propose that integrating physics-based modeling into development pipelines-alongside experimental validation-can reduce empirical iteration and de-risk translation. As force fields, and hybrid physics-machine-learning methods improve, this field is poised to become a central driver of next-generation ADC design.

Correlative nanoscale surface characterization benefits from simultaneously measuring electronic and structural properties in the same environment, a capability that is essential for modern-day materials science and semiconductor failure analysis. In-situ AFM-SEM measurements facilitated by self-sensing cantilevers offer great potential here; however, they are limited due to their inherent capacitive crosstalk. Here, we demonstrate for the first time the in-situ implementation of single-pass heterodyne Kelvin probe force microscopy inside a scanning electron microscope, using piezo-resistive cantilevers. We overcome the capacitive crosstalk prevalent in piezo-resistive cantilevers by demodulating excitation and detection to simultaneously map surface topography and contact potential difference for correlation with compositional analysis. We systematically compare different operational modes of this heterodyne technique, elucidating their spatial resolution, signal sensitivity, and signal-to-noise ratio. The integrated approach yields exceptional signal quality and reveals how electron beam scan parameters can directly influence surface potential contrast. We demonstrate this correlative analysis workflow on two-dimensional heterostructures and semiconductor circuits. This work establishes a robust and versatile correlative imaging mode for in-situ Kelvin force and topography imaging inside a scanning electron microscope for next-generation semiconductor device analysis and materials science.

In two-dimensional materials where interacting Fermi pockets occur in valleys related by time-reversal symmetry, a spontaneous valley imbalance results in a novel state known as an orbital magnet. Due to the breaking of time-reversal symmetry, this state can be probed in transport experiments by the violation of Onsager relations, most often done through the anomalous Hall effect (AHE). Here we propose that odd-in-field, in-plane linear magnetoconductivity (LMC) is an alternative probe of valley polarization which can occur even when the AHE vanishes. In multilayer structures, the effect originates from in-plane orbital moments and Berry curvatures enabled by interlayer tunneling and dominates over the spin response. After a classification of many recently studied multilayers, we focus on two valley polarized examples: twisted bilayer graphene, where LMC is finite but the AHE vanishes unless additional symmetry breaking from the substrate is present, and rhombohedral graphene multilayers, where LMC and AHE both track valley polarization because they have the same symmetry. Using self-consistent Hartree-Fock and semiclassical transport calculations, we present detailed predictions of LMR for these two examples and analyze the implications for recent experiments.

The main ideas and some of the most important results of the spherically symmetric self-consistent approach and a number of related theoretical algorithms are presented. These methods make it possible to study low-dimensional Heisenberg-type spin models, including frustrated ones, with careful consideration of the theoretic (Mermin-Wagner and Marshall) theorems, as well as the site spin constraint. Thus, the difficulties that may arise in the traditional analysis of low-dimensional magnetic systems are avoided. The approach can also be applied to the spin-pseudospin model, and is also embedded in more complex constructions when considering spin models with free carriers, such as the basic and three-band Hubbard models, t-J and s-d models, and the Kondo lattice.

The extension of ab-initio methods like density functional theory (DFT) to quantum dot (QD) geometries has enabled researchers to explore relationships between QD surface termination and electronic structure. However, fully utilizing the data from DFT requires novel classification methods for QD orbitals. Here, we identify relationships between QD geometry and electronic structure by transforming real-space QD orbitals into momentum-space using Bloch orbital expansion (BOE), yielding a fuzzy QD band structure. Comparing with bulk band structures, we show that truncated, unpassivated facets in III-V and II-VI QDs produce mid-gap orbitals derived from bulk surface orbitals; an identification challenging in real space. QDs with reconstructed facets, however, feature delocalized orbitals formed by superposition of bulk Bloch orbitals. Moreover, we prepare for the first time atomistic core/shell QD models and by the BOE expansion we can clearly identify the core/shell band alignment not possible in real space. These findings emphasize BOE as a vital tool for connecting computational and experimental insights in nanocrystal research.

The creation and exploration of new materials under extreme pressure-temperature conditions has become increasingly reliant on laser-heated diamond anvil cell (LHDAC) techniques, which provide direct access to previously unexplored regions of multinary phase diagrams. Whereas numerous high-pressure phases have been identified in situ, systematic recovery and post-synthesis physical property characterization of these materials remain significant challenges. In this work, we present the development of an integrated LHDAC synthesis and demonstrate a practical LHDAC-based synthesis workflow that enables stabilization and recovery of metastable intermetallic phases for subsequent structural and transport studies. Using this approach, we successfully achieved LHDAC synthesis of high-pressure MnSb2 and YbZn2 phases under moderate pressures. Synchrotron X-ray diffraction and spatial mapping confirm dominant formation of the targeted phases, whereas laboratory-based refinement quantifies phase fractions despite intrinsic microstrain and minor secondary phases. High-pressure transport measurements on recovered samples reveal tunable by pressure electronic instabilities in both systems. In MnSb2, pressure suppresses two high-temperature magnetic ordering anomalies, observed in transport, by 5 GPa and for higher pressures induces a new low-temperature feature that increases with further pressure increase. In hexagonal high-pressure YbZn2, an electronic reconstruction emerges at ~11 GPa, characterized by semiconducting-like behavior from ~ 30 K to 300 K and a broad low-temperature coherence crossover near 30 K. Our results establish LHDAC synthesis not only as a structural discovery tool, but also as an experimental platform for investigating correlated quantum states stabilized far from equilibrium thermodynamic conditions.