Papers

We investigate the influence of strain on the electrical properties of graphene nanoribbons that have potential applications in making sensors and other optoelectronic devices. In particular, we chose pristine armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH), boron doped armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH-B) and armchair graphene nanoribbons with 7 zigzag edges that have one carbon atom vacancy (7aGNRsH-V). Based on first-principles calculations, results show that pristine unstrained 7aGNRsH is electrically nonconductive but turns to be electrically conductive in a wide range of energy spectrum, e.g., from IR to visible to UV, due to the application of strain engineering. In metallic unstrained and strained 7aGNRsH-B and 7aGNRsH-V, non-vanishing electrical conductivity in the IR, visible and UV energy spectrum regimes are observed. We also investigate the influence of strain on the Berry curvature of 7aGNRsH, 7aGNRsH-B and 7aGNRsH-V nanoribbons. The results show that fermions are spread through out the Brillion zone in the reciprocal space for semiconducting unstrained 7aGNRsH but localized near the $\Gamma$-point for strained 7aGNRsH that have out-of-plane deformations due to strain engineering. For metallics 7aGNRsH-B and 7aGNRsH-V, Berry curvature plots show that fermions are localized far away from the $\Gamma$-point. In two atom boron doped p-type armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH-2B), large peaks in electrical conductivity at IR energy spectrum regimes can be observed. These peaks of electrical conductivities in 7aGNRSH-2B may be detectable in experimentally synthesized structure in Reference, JACS 137, 8872 (2016).

Despite the strength of Molecular Dynamics simulations in providing insights into the microscopic details of phenomenon in many fields in materials science, physics and biology, the biggest barrier is its limited timescale which is several orders of magnitude lower than the timescale of the real-world processes and phenomena being modeled. Free energy calculations are designed as a remedy to this problem that in theory can overcome this barrier. This is particularly relevant for biomineralisation processes such as tooth mineral formation and dissolution, while reflecting a broader challenge in accurately modelling rare events and long-timescale phenomena across complex molecular systems. However, due to the novelty of the field, a number of questions remain outstanding pertaining to the best practice of applying this method. The non-equilibrium work approach based on the Jarzynski equation is one of the most promising free energy calculation methods. However, the biggest challenge for the broader use of this method is the question of how many simulations are required for an accurate free energy estimation. Comparing the free energy results with very long reversible pulling simulations of atom clusters from the surface, each taking 75 days on the 48-core Intel Xeon Platinum 8268 CPUs, in this study we showed that this question is irrelevant and higher quality and better equilibrated initial structures is the proper approach than simply based on the number of simulations. We designed a new adaptive free energy calculation methodology which combines high quality, high computational cost free energy values with lower quality, lower cost values to build up the entire free energy profile. In the best case scenario this method lowers manifold the computational cost required for the non-equilibrium work free energy calculations compared to both the regular method and the reversible simulation.

Controlling electron spins in double quantum dots allows individual electrons to be trapped and manipulated for next-generation solid-state qubit devices. In this paper, the study analyzes spin relaxation due to deformation potentials of acoustic phonon in single and double quantum dots under in-plane and out-of-plane magnetic fields, showing that in single quantum dots the relaxation rate is highly sensitive to low in-plane magnetic fields ($<1T$) but converges near a spin-hot-spot region. In a single quantum dot, the spin-hot spot arises from well-understood level crossings between singlet and triplet states. In double quantum dots, a new and unusual spin-hot spot appears as the dots are pulled apart from the origin, with spin-relaxation rates three orders of magnitude lower than conventional single quantum dots. In displaced quantum dots dominated by magnetic confinement, two distinct spin-hot spots appear at different in-plane magnetic field strengths, where spin-relaxation time varies from millisecond to picosecond. When quantum dots are separated by about 60 nm, calculations predict oscillations in spin-hot spots as the in-plane magnetic field changes. These unusual spin-hot spot oscillations occur at low magnetic fields ($<1T$), resulting in spin-relaxation rates about four orders of magnitude lower than those of conventional high-field spin-hot spots ($\approx 4.5T$). The extremely low spin-relaxation rate at the spin-hot spot enables the preparation of qubit superposition states for quantum computing and information processing.

The realization of fully reconfigurable, voltage-controlled, and programmable on-chip magnonic devices is essential to fully harness the potential of spin waves for signal processing, logic and neuromorphic computing. Yet, existing demonstrations of electrical tuning of magnonic responses are either volatile, current-driven and thus energy-inefficient, or rely on local strain modification limiting their scalability for wafer-scale integration. Here, we address this challenge using a BiFeO3/La0.67Sr0.33MnO3 multiferroic thin film heterostructure. We show that ferroelectric domain engineering in BiFeO3 enables deterministic tuning of the magnon dispersion of La0.67Sr0.33MnO3, producing frequency shifts up to $\sim 150 MHz$ and allowing reconfigurable waveguiding. Micro-focused Brillouin light scattering directly images these effects, revealing electrically defined magnonic waveguides and spatially programmable dispersion. Compared to conventional approaches, this method provides non-volatile and reversible control. Furthermore, using an inverse-design simulation code, we demonstrate the capability of our platform to perform advanced magnonic functions such as frequency demultiplexing. Our results open a new avenue for using magnetoelectric heterostructures for magnonic logic, with further applicability to reservoir and neuromorphic computing and AI driven magnonic devices.

The superconducting (SC) pairing symmetry and its link to time-reversal symmetry breaking (TRSB) in the vanadium-based kagome superconductors remain unresolved, with ambiguities stemming from sublattice interference and charge-density-wave (CDW) entanglement with superconductivity. Using two representative SC pairings, i.e., the conventional on-site $s$-wave and the TRSB $d_{x^2-y^2}+id_{xy}$-wave, as a model study, we theoretically show that while single magnetic impurity yield qualitatively identical spectral behavior of local density of states (LDOS) for these two symmetries, two magnetic impurities give rise to distinct LDOS patterns. For the conventional on-site $s$-wave pairing, time-reversal symmetry (TRS) enforces equivalent forward and backward scattering between two impurities across all impurity configurations, leading to near disappearance of a Yu-Shiba-Rusinov (YSR) state pair along the line connecting the two impurities. However, for the TRSB $d_{x^2-y^2}+id_{xy}$-wave pairing, this scattering equivalence holds only for inversion-symmetric impurity configurations, with a pair of YSR disappearance restricted to this case. These distinct spectral features are resolvable in scanning tunneling microscopy (STM) experiments, providing a direct avenue to discriminate TRSB and non-TRSB SC pairing symmetries in kagome superconductors and an alternative method to probe SC nonreciprocity that circumvents the ambiguities of conventional critical current-based techniques.

Using a combined experimental and first-principles theoretical approach, we demonstrate interface engineering of the anomalous Hall effect in Ni-based epitaxial thin-film heterostructures. Ferromagnetic Ni thin films are grown on (001)-oriented single-crystal LaAlO$_3$, SrTiO$_3$, and MgO substrates, which impose different biaxial tensile strains of 0.3%, 0.6%, and 0.8%, respectively. Our room-temperature Hall transport measurements reveal a pronounced substrate-dependent modulation of the anomalous Hall conductivity. Interestingly, our calculations show that strain alone cannot account for the experimentally observed trends. Instead, we identify interfacial inversion-symmetry breaking, which induces Rashba spin-orbit interaction, as the key mechanism governing the anomalous Hall conductivity across different interfaces. Building on this understanding, we further demonstrate both theoretically and experimentally that the anomalous Hall conductivity can be continuously tuned by an external electric field. These findings establish the critical role of substrate-induced interfacial effects in controlling the anomalous Hall effect in engineered heterostructures and provide a viable pathway toward electrically tunable room-temperature spintronic devices.

We investigate the nonlinear response of the chiral magnetic current to an external magnetic field in a holographic setup. Using the D3/D7 brane system, where the chiral magnetic effect (CME) can be realized by considering rotating probe D7-branes, corresponding to introducing an axial chemical potential, we analyze the current-magnetic field relation beyond the linear regime. Focusing on the vicinity of the phase boundary between the insulating phase and the CME phase, we find that the chiral magnetic current exhibits a multi-valued dependence on the magnetic field, indicating a highly nonlinear response characteristic of this model. We further study the dynamical stability of the insulating phase near the transition point, and show that the presence of both an axial chemical potential and an external magnetic field cooperatively stabilize the system. Our results clarify the interplay between the axial chemical potential and the magnetic field in determining the phase structure and stability of the system, and reveal new nonlinear aspects of chiral transport in holographic gauge theories.

Neural networks trained with gradient-based methods exhibit a strong simplicity bias: they learn simpler statistical features of their data before moving to more complex features. Previous analyses of this phenomenon have largely focused on settings with (quasi-)isotropic inputs. In this work, we study the simplicity bias from a Fourier perspective, which allows us to include two key features of natural images in the analysis: approximate translation-invariance and power-law spectra. We first show experimentally that simple neural networks trained on image classification tasks first rely on amplitude information – related to pair-wise correlations between pixels – before exploiting phase information, which encodes edges and higher-order correlations. In view of this, we introduce a synthetic data model for translation-invariant inputs that allows precise control over amplitudes and phases while remaining tractable. We rigorously establish that for isotropic and high-dimensional inputs, classification based on phase information alone is a genuinely hard task: online stochastic gradient descent (SGD) cannot distinguish the structured inputs from noise within $n \ll N^3$ steps, but needs at least $n \gg N^3 \log^2{N}$ steps. In contrast, we show both experimentally and theoretically that power-law spectra can dramatically accelerate the speed of learning phase information, even if the spectra do not help with classification. Simulations with two-layer networks trained on textures and with deep convolutional networks on ImageNet and CIFAR100 confirm this non-trivial interaction between amplitudes and phases, providing mechanistic insights into how deep neural networks can learn natural image distributions efficiently.

We prove that every probabilistic cellular automaton with strictly positive transition probabilities that admits a stationary Bernoulli measure is exponentially ergodic. Moreover, the mixing time of any finite region in such a system is logarithmic in the diameter of the region. A similar result holds in continuous time for positive-rate, finite-range interacting particle systems. The proofs use entropy, and rely on a representation of the system as a perturbation of another system with noise. The ergodic behaviour results from a competition between the accumulation of randomness due to noise and the diffusion of randomness due to local information exchange. We show that, in two and higher dimensions, the positive-rate probabilistic cellular automata that admit stationary Bernoulli measures are algorithmically indistinguishable from those that do not.

We determine the temperature and momentum dependence of the static spin exchange–correlation kernel of the unpolarized three-dimensional uniform electron gas using variational diagrammatic Monte Carlo. The calculations cover metallic densities with Wigner–Seitz radii $r_s=1\!-\!4$ and temperatures $T/T_F=1/16\!-\!8$, where $T_F$ is the Fermi temperature. At low temperature, the calculated kernel approaches established ground-state parametrizations. Heating suppresses its Fermi-scale minimum and reduces its magnitude, while the relative momentum dependence evolves nonmonotonically through the warm-dense crossover. At the two highest temperatures studied, the spin kernel is nearly constant over $q \leq 2k_F$, where $k_F$ is the Fermi wave number. The charge kernel at $r_s=1$ retains pronounced momentum dependence under the same conditions, making a local approximation to the XC kernel more accurate in the spin channel over this interval. The directly sampled uniform response determines the XC free-energy curvature with respect to spin polarization, independently of a fitted polarization dependence. This curvature sets the interaction contribution to the free-energy cost of weak polarization and differs by several percent from the Groth–Dornheim–Bonitz parametrization at elevated temperatures. These results provide momentum-resolved reference data for finite-temperature spin-response theories and direct constraints on the spin dependence of thermal free-energy functionals.

Machine learning approaches informed by physics have offered new insights into the discovery of constitutive models from data, helping overcome some limitations of traditional constitutive modelling while reducing the cost of otherwise computationally intensive simulations. Yet, many existing methods either enforce only part of the relevant physical and thermodynamic structure, or achieve thermodynamic consistency within specific constitutive classes, leaving open questions about their applicability across a broad range of material behaviours and their ability to generalise to unseen loading paths when limited data are available. This work establishes a thermodynamics-constrained learning framework for inelastic constitutive models from macroscopic stress-strain data. The framework is based on non-equilibrium thermodynamics and parameterises both the free energy and the generalised transport operator governing the evolution of the material state, while enforcing material objectivity, energy balance, and non-negative dissipation as hard, scalable constraints. Analytical benchmarks involving simple stress-strain loading paths demonstrate that the method learns thermodynamically consistent and robust constitutive models for a range of inelastic materials of increasing complexity. At inference, the resulting models generalise to more demanding, unobserved paths and identify interpretable internal variables that capture path-dependent behaviours. The framework is then applied to granular media, prototypical heterogeneous and history-dependent materials. Trained on numerically simulated experiments based on the discrete element method, the method identifies admissible constitutive equations and predicts the response under cyclic loading, including the emergence of hysteresis absent from the training data, relying solely on macroscopic stress-strain histories.

We develop a complete relativistic theory to describe the dynamics of electronic angular momentum including both spin (S) and orbital (L) contributions in magnetic systems. We start with the relativistic Dirac-Kohn-Sham Hamiltonian under the influence of an electromagnetic field and apply a unitary transformation to formulate the extended Pauli Hamiltonian. Using the transformed semirelativistic Hamiltonian, we derive the angular momentum dynamics for the orbital and spin angular momenta. Thereby, we formulate the coupled dynamics of orbital and spin moments consistent with the relativistic Dirac framework. Considering especially the conservation of the total angular momentum, J = S +L, we show first that J is conserved in the absence of a spin-polarized Kohn-Sham exchange field, but is no longer conserved under the application of an electromagnetic field, e.g., laser pulse, THz field, etc. Second, considering magnetic systems with atomic spin and orbital momenta, we derive the coupled equations of motion of angular momenta dynamics whilst making the atomistic Heisenberg approximation for the exchange interaction. Our results suggest that, under these assumptions, the total angular momentum remains conserved, even with electromagnetic field, but atomic spin and orbital angular momenta individually are not conserved.

We theoretically investigate emergent impedance induced by domain-wall dynamics in antiferromagnets. Emergent impedance, arising from a combined action of spin-transfer torque and spinmotive force, was previously predicted and observed in spiral magnets. Here we develop a formalism for the electrical response of an antiferromagnetic domain wall under ac currents, and obtain analytical expressions for the resulting emergent impedance. We find that two dynamical modes play separate roles in the emergent impedance: Translational motion of the domain-wall center generates a contribution proportional to its velocity, analogous to that arising from the corresponding motion of a spiral magnet. Another contribution, unique to antiferromagnetic domain walls, originates from the time-dependent canting of the sublattice magnetizations localized within the moving domain wall, whose magnitude is inversely proportional to the antiferromagnetic exchange coupling constant. The competition between these two distinct contributions determines the sign and magnitude of the imaginary part of the emergent impedance at sub-resonant frequencies. Our results provide a fundamental insight into electron transport in antiferromagnets, and open avenues for novel antiferromagnet-based spintronics devices.

Strongly correlated transition-metal perovskite oxides pose a fundamental challenge for electronic-structure theory and for large-scale, data-driven materials discovery. While DFT+DMFT provides a quantitatively accurate description of such systems, its high-throughput application is hindered by the need to determine material-specific Coulomb interaction parameters ($U$). First-principles approaches such as the cRPA predict a highly nonlinear and non-transferable evolution of the interaction strength across chemically similar ABO$_3$ perovskites. Here we show that this paradigm does not extend to the large-energy-window eDMFT, which employs highly localized orbitals and treats electronic correlations and screening self-consistently within the same many-body framework. As a result, spectral properties are governed primarily by the dynamical self-energy rather than by static interaction-induced energy shifts. Recent constrained-eDMFT calculations demonstrated that, for broad classes of $3d$ transition-metal oxides, the self-consistently screened Coulomb interactions naturally fall within relatively narrow ranges for correlated metals and insulators. Motivated by these findings, we implement a high-throughput eDMFT framework employing physically derived interaction values of $U=6$ eV for metals and $U=10$ eV for insulators together with $exact$ double counting. We test this framework using systematic high-throughput eDMFT calculations for ABO$_3$ compounds (A = Ca, Sr, La; B = V–Ni) and benchmark the resulting spectral functions against photoemission experiments, where we find overall excellent agreement. Our results establish that charge self-consistent eDMFT enables robust, parameter-tuning-free high-throughput many-body calculations for correlated oxides, opening a practical pathway toward predictive electronic-structure databases for strongly correlated materials.

Modeling the dynamics of colloidal rods remains a central challenge in soft-matter physics due to the anisotropic and long-ranged nature of their interactions. Hydrodynamic interactions in rods suspensions are often assumed to be screened or too week to play any role in semi-dilute regimes, yet we find here these assumptions to break down at shear rates and concentrations that are often attained in experiments. Using particle-based simulations and scaling analysis, we uncover a cascade of tumbling events driven by hydrodynamic coupling among neighboring rods. This collective dynamics disrupts flow alignment and leads to a pronounced increase in viscosity and normal stress differences, in qualitative agreement with recent experiments. The discovery of this hydrodynamically-promoted cascade effect calls for a revision of existing constitutive models for colloidal rods and highlights hydrodynamic coupling as a key mechanism governing collective dynamics in highly anisotropic suspensions.

Stratospheric aerosol injection (SAI) using solid particles has been proposed as an alternative to sulfate aerosols for solar radiation modification, but practical deployment faces challenges in efficiently deagglomerating and dispersing powders as submicron particles. Here we experimentally demonstrate pneumatic dispersal of particles in optically optimal size ranges for SAI. Using spherical amorphous silica particles, we find that applying a hydrophobic surface treatment substantially improves dispersibility, with 50-85% of treated particle mass achieving submicron sizes compared to 10% for untreated particles. We compare the dispersal of treated particles of different sizes and find that 300 nm particles provide superior deagglomeration than 500 nm particles for the same air consumption. Theoretical modeling of the adhesion forces between particles, combined with surface roughness parameters extracted from atomic force microscopy, successfully predicted the relative dispersibility across different particle types. The pneumatic dispersal system achieved optimal performance at air-to-powder mass ratios of about 10:1. Using the measured dispersed particle sizes, we provide a scaling analysis suggesting that a feasibly sized fleet of dispersal aircraft could provide an aerosol layer sufficient for meaningful climate intervention. These results demonstrate that hydrophobic surface treatment and pneumatic dispersal can overcome the agglomeration challenge for SAI with solid particles.

Diffusion-based information-theoretic approaches provide new theoretical and practical tools to study complex networks. So far, they have not been generalized to temporal networks. In this work, we show that common entropic measures based on modeling diffusion on graphs, such as the entropy rate and the spectral entropy, do not generalize straightforwardly to temporal networks due to the process's non-stationarity and temporal asymmetries. Instead, we propose the conditional entropy of heat diffusion as an entropic measure for continuous-time temporal networks and study its properties. We show that this quantity is monotone in time, yielding an information-theoretic analog of the second law of thermodynamics for inhomogeneous diffusion on temporal networks. We provide an upper bound and suggest a lower bound on its evolution and explain how discrepancies from it arise due to asymmetric temporal paths. We then introduce a local version of conditional entropy, designed to probe diffusion over finite temporal windows, and show that it provides an informative signal for change-point detection in continuous-time temporal networks. We evaluate the proposed methodology on synthetic benchmarks, including comparative experiments with existing nonparametric baselines in the snapshot setting, and then apply it to a real-world temporal contact network. Finally, we show how to use detected change points to guide community detection on targeted sub-intervals, improving the quality and interpretability of the clustering results.

Many biological processes, from cell division to viral lysis, are triggered when an internal stochastic variable reaches a threshold. Here we introduce Branching under First-Passage Resetting, a general framework in which replication events arise endogenously from first-passage dynamics rather than from externally imposed lifetime clocks. We show that the resulting population dynamics obey an exact renewal equation linking single-trajectory first-passage statistics to the population growth rate. This mapping shows that, for fixed offspring number and fixed mean replication time, stochastic timing fluctuations necessarily enhance growth relative to a deterministic clock. When offspring yield depends on the first-passage time, however, fluctuations have non-trivial effects and expose a fundamental yield-delay trade-off: waiting longer can increase the number of descendants, but delays all future lineages. Our framework allows us to address this optimization problem analytically, and upon application to bacteriophage lysis, gives an optimal lysis time and growth rate consistent with empirical data.

Reduced density-matrix functional theory (RDMFT) provides a variational route to electronic correlations beyond conventional density-functional approximations, but explicit evaluations of density-matrix functionals still scale exponentially with the number of active one-particle states. We formulate and assess a real-space-decomposed density-functional plus reduced-density-matrix-functional (DF+RDMF) scheme in which the Coulomb interaction is partitioned locally in real space and the RDMF correction is evaluated only for the strongly correlated part of the interaction. The resulting local density-matrix functionals are further compressed using the adaptive cluster approximation (ACA), which performs a unitary rotation of the bath subspace before truncation and therefore preserves the local interaction while reducing the number of explicitly correlated bath states. As a molecular test case, we consider the bending potential of carbon suboxide, C$_3$O$_2$. While semilocal PBE favors a linear molecule, the DF+RDMF/ACA correction stabilizes a bent configuration in qualitative agreement with the quasilinear behavior inferred from spectroscopy. The approach provides a systematic embedding hierarchy for combining density functionals with explicitly correlated density-matrix corrections in extended or spatially inhomogeneous systems.

Descriptions of complex physical or biological systems often include stochastic contributions, and these are commonly simulated using Wiener processes. In many cases however, non-Gaussian fluctuations may originate from non-Wiener processes which remain less explored. The Euler-Maruyama method of discretising stochastic differential equations to non-Wiener processes is generalised. Non-Gaussian noise generated from a subset of Lévy processes can be used simply and often with more physical justification, for both additive and multiplicative noise. An example of this is provided that gives superior physical results compared to using geometric Brownian motion. Finally the results of the additive noise are shown to be equivalent to a derived master equation via the Kramers-Moyal expansion.

The single-turn coil method is a destructive pulsed magnet for generating over 100 T with a few $\mu$-second pulse duration, and it inevitably causes the coil to explode. The temporal and spatial distributions of the electric current and magnetic field are highly inhomogeneous, arising from the skin effect, rapid temperature rise, and coil deformation. To grasp the dynamic phenomena in the single-turn coil, we conducted a finite element analysis using multiphysics simulation. We employed finite element method calculations using a fully 3D model of the single-turn coil with broken cylindrical symmetry. The calculated result revealed highly nonlinear diffusion of electric current, temperature, and magnetic fields, which are the sources of the inhomogeneous magnetic fields inside the single-turn coil in time and space.

We consider the electron transport through a single molecule magnet transistor in the presence of a local transverse magnetic field and ac-driven gate voltage. We calculate the conductance as a function of the electron energy and transverse magnetic field by using the Floquet and Landauer formalism. We show that the time periodic potential causes zero transmission resonances that oscillate as a function of the transverse magnetic field due to the Berry phase interference associated with two quantum tunneling paths. We find that these Berry phase oscillations can be detected in the conductance as a function of the transverse magnetic field for an incoming electron with a specific energy.

Phonon-mediated superconductivity is conventionally thought to be capped at a transition temperature $T_{\mathrm{c}}$ no larger than roughly one-tenth of the phonon frequency $\Omega$, a bound rooted in the breakdown of Migdal-Eliashberg theory at intermediate coupling and in the heaviness of bipolarons formed in standard models with phonons that couple to the electron density. In this review I describe a route to phonon-mediated high-$T_{\mathrm{c}}$ superconductivity that bypasses this bound. The key ingredient is a class of electron-phonon couplings in which lattice distortions modulate the electron hopping and therefore its kinetic energy rather than its potential energy, known as the Peierls model (also known as Su-Schrieffer-Heeger model). In these models phonon exchange generates an interaction that binds two electrons into a small but unusually light bipolaron. Using sign-problem-free quantum Monte Carlo simulations of a bond-Peierls model on the square and cubic lattices, my collaborators and I have shown that a dilute liquid of such bipolarons forms an $s$-wave superconductor with a $T_{\mathrm{c}}/\Omega$ that significantly exceeds the conventional bound, that this conclusion is robust against screened Coulomb repulsion, and that $T_{\mathrm{c}}/\Omega$ – despite being reduced – remains above bound in presence of strong long-range Coulomb repulsion. A semi-classical instanton analysis explains why, at strong coupling, bipolarons in models with phonon-modulated hopping are lighter than their density-coupled (Holstein) counterparts. I close with a discussion of materials in which this physics may be operative, in particular the iron-based pnictide superconductors, and of design principles that follow from it.

Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an alternative to physics-based simulation, offering a faster and cheaper way to filter materials based on their stability and other target properties, reducing the number of candidates that reach the costly synthesis stage. Recently, Large Language Models (LLMs) have been applied to this role, but these models are parameter-heavy and computationally expensive both during training and at inference time, making them unsuitable for high-throughput tasks. This inefficiency stems from both the large over-parameterization of language models and the difficulty of framing material generation as a sequence learning problem. In this paper, we present PRISMat, a cost-effective, permutation-invariant model, which addresses these limitations. We show that PRISMat, despite taking less time for inference, is able to outperform LLMs in generating crystal slabs conditioned on critical materials' surface properties. In targeted material discovery, we achieve mean absolute errors of 0.188 eV/A$^2$ and 2.79 eV for cleavage energy and work function tasks, respectively, reducing the error of the next best model by 4$\times$.

We review recent machine-learning (ML) approaches for point defects in non-metallic materials, with an emphasis on defect formation energies. Existing studies largely fall into two categories: direct ML models that predict defect energetics from local structural representations, and machine-learning potentials (MLPs) that approximate the defect-containing potential energy surface. We summarize key achievements as well as persistent bottlenecks, emphasizing that dataset quality often dominates practical model performance. We further identify charged-defect formation energies as a central frontier, where Fermi-level alignment, finite-size corrections, and long-range electrostatics must be handled carefully and consistently to enable meaningful comparisons and transferable predictions across different materials.

Incorporation of $\mathrm{Al_{y}Ga_{1-y}N}$ (AGN) semiconductors into high power electronics offers efficiency improvements in power transmission, generation, and use, if approaches to eliminate the defects arising from film-lattice mismatch can be established. Here, we report the optical floating zone crystal growth of $\mathrm{Ta_{1-x}Hf_{x}C_{0.5}}$ (x = 0.2), a new metallic substrate material family lattice matched to the ultra-wide-band-gap, Al-rich side (y = 0.91) of the AGN solid solution. Laue diffraction demonstrates large single crystal domains in the as-grown boule. Single crystal x-ray diffraction at T = 213 K in conjunction with first principles calculations shows that the material adopts a layered crystal structure with AA-type stacking of (Ta/Hf)-C-(Ta/Hf) trilayers described in the trigonal space group P-3m1 (#164), with a = 3.1168(4) Å, c = 4.9644(4) Å, and $\beta$ = 120.0{\deg}. X-ray photoelectron spectroscopy (XPS) measurements show the Hf:Ta ratio to be close to the nominal value of 0.8:0.2 in the grown crystal. Density Functional Theory calculations reveal that this structure is stabilized by the low energy of carbon-vacancy formation of a hypothetical $\mathrm{(Ta/Hf)_{1}C_{1}}$ anti-NiAs structure type, and imply flexibility in interface structure with an overlayer nitride film. A surface preparation/polishing procedure is developed that reduces root mean square (RMS) surface roughness from as-cut 130 nm to 7 nm as measured by atomic force microscopy. Scanning electron microscopy shows the presence of a native surface oxide, removed by polishing, along with carbon-rich pits. Time-domain thermoreflectance measurements show a room temperature thermal conductivity of $\kappa$ = 18.1(4) W m-1 K-1. These results provide key first steps for utilizing metallic, lattice matched, substrates for the growth of Al-rich AGN semiconductors.

In cuprate superconductors, electron-electron repulsion results in characteristic spectroscopic features known as `waterfalls', where the sharp quasiparticle dispersion transitions into broad Hubbard bands. However, in multi-orbital systems, the additional Hund coupling results in behavior that defies the conventional Mott–Hubbard paradigm, creating qualitatively distinct `superdispersive' features in the spectral function. Here, we use tunneling spectroscopy to reveal this signature of Hund physics in Sr$_2$RuO$_4$. By combining density functional theory, dynamical mean-field theory, and continuum local density of states calculations, we show that the experimental features are in excellent agreement with theoretical predictions and intimately linked to the non-monotonous energy dependence of the real part of the self-energy in a Hund metal. Our results provide direct experimental evidence for Hund-induced spectroscopic features and open a new route to probing correlation effects in quantum materials.

We present a methodology to analyze metastable properties of a simple prototypical stochastic bistable system by identifying slow inter-well and fast saddle escape processes using the formalism of the Koopman operator. Instead of studying noise-induced transitions by following the trajectories of the system, we track them by studying the time evolution and the decay rate of the subdominant mode of the Koopman operator, thus in a geometry-agnostic framework. The obtained escape time statistics together with the decay rate are in good agreement with the predictions - both the exponential and subexponential ones - of large deviation theory in the weak-noise limit, both in equilibrium and nonequilibrium conditions. The subdominant Koopman mode also allows for an accurate reconstruction of the competing basins of attraction. Furthermore, going deeper in the Koopman spectrum, we are able to recognise modes that are associated with intra-well variability as well as with the escape of trajectories from the saddle towards the attractor, both in the equilibrium and nonequilibrium case. Our methodology, being grounded in purely data-driven techniques, could provide a comprehensive, multi-scale framework for studying high-dimensional metastable systems.

The determination of crystallographic texture through elastic wave propagation offers a cost-effective, nondestructive means of obtaining through-thickness information with minimal sample preparation. Existing ultrasonic approaches rely on either bulk-wave or guided-wave velocity measurements for texture inversion. These strategies impose geometric constraints: bulk-wave methods become impractical for thin specimens, whereas guided-wave techniques are limited to relatively small thicknesses. Furthermore, many formulations assume orthotropic symmetry of the aggregate, thereby restricting their applicability to materials with higher anisotropy. In this work, a full-field wave fitting strategy is developed in which the transmitted ultrasonic field is simulated and directly compared to experimental measurements. Because the approach does not rely on bulk-wave or plate-wave approximations, it remains applicable across a broad range of specimen thicknesses. Furthermore, no macroscopic symmetry assumptions are imposed on the aggregate, enabling the characterization of generally anisotropic materials. The effective elastic response is computed using a Hashin-Shtrikman homogenization framework, which provides tighter bounds than classical Voigt-Reuss-Hill averages and constrains the admissible search space during optimization, thereby improving convergence. The nonlinear inverse problem is solved using a GPU-accelerated optimization scheme. The methodology is validated on materials with hexagonal and cubic crystal symmetry over a range of specimen thicknesses. The inferred texture coefficients show consistent agreement with independent diffraction measurements. Additionally, textures with weak elastic anisotropy are successfully recovered, demonstrating the robustness and versatility of the proposed method. Complete measurement and inversion are achieved within approximately 10 minutes.

We identify a correlation-driven mechanism for the temperature-induced spin reorientation in the quasi-one-dimensional van der Waals antiferromagnet CrPS4. Magnetic pair distribution function (mPDF) analysis resolves the local spin direction and shows that ferromagnetic intrachain correlations persist far above TN. Combining these correlations with a DFT-derived spin Hamiltonian reveals competing single-ion and exchange-anisotropy channels, with single-ion anisotropy remaining local while exchange anisotropy is renormalized as intersite correlations decay. This differential renormalization rotates the effective easy axis and captures the ordered-state canting. Above TN, the continued rotation beyond the model prediction delineates the limits of the dominant-chain approximation. These results establish mPDF-derived correlations as direct inputs to microscopic Hamiltonians and show how low-dimensional correlations can control magnetic anisotropy.