Noncollinear antiferromagnets exhibit anomalous Hall and magneto-optical Kerr effects driven by Berry curvature despite negligible net magnetization, promising ultrafast spintronic applications. While both effects are theoretically expected to reveal the intrinsic Berry curvature that serves as a spintronic memory bit, their quantitative interpretation is complicated by additional temperature-dependent contributions superimposed on the magnetic order parameter: extrinsic skew scattering in dc Hall transport, and optical-resonance effects in visible-wavelength Kerr measurements. Here we perform polar Kerr measurements at the infrared telecommunication wavelength (1550 nm) on epitaxial, stoichiometric ${\mathrm{Mn}}_3\mathrm{NiN}$ single-crystal films, revealing a spontaneous Kerr signal that remains stable within a few percent over a 200 K range below the Néel temperature. This temperature-invariant intrinsic Kerr response contrasts with the strongly temperature-dependent anomalous Hall effect in the same sample dominated by extrinsic skew scattering. Our findings establish infrared Kerr effect as a robust, local probe of Berry curvature in noncollinear antiferromagnets, enabling quantitative characterization and advancing antiferromagnetic spintronic technologies.

Topologically protected magnetic textures are a promising route to low-energy control of magnetism, but they are most often studied away from ambient conditions, typically at low temperatures and high magnetic fields. Here we use in situ Lorentz transmission electron microscopy with control of temperature and magnetic field to investigate the skyrmion metastability in ${\left({\mathrm{Fe}}_{0.5}{\mathrm{Co}}_{0.5}\right)}_5{\mathrm{GeTe}}_2$ (FCGT). By field-cooling FCGT in magnetic fields of different magnitude to different base temperatures and then removing the applied field, we create meta(stable) zero-field magnetic states. We use this method to build a phase diagram of the zero-field metastable spin structures in FCGT, which will be critical for selecting the desired topologically protected spin state for future studies to manipulate magnetism with stimuli such as electric current, electric field, mechanical strain, and more.

Photonic materials with a prescribed response over multiple spectral bands are essential to applications such as energy harvesting and thermal management. Engineering the response over such a wide range of wavelengths is difficult because these materials have hierarchical structures, which make it challenging to predict and optimize the spectral response. To address this challenge, we develop and compare several optical models aimed at capturing multiscale effects, and we apply these models to understanding the response of microgeodes—dielectric microshells encapsulating nanowires. Among the models, we find that an intensity-based transfer matrix method, newly developed in this work, provides the best agreement with experimentally observed spectra. In particular, it substantially improves the prediction of reflectance at short wavelengths, where the other models significantly underestimate the experimental values, resulting in an overall improvement across the full spectrum. We show that this model has superior predictive performance because it accounts for the interdependent scattering contributions from nanowires and microshells, while circumventing the need to specify reflectance conditions at the boundaries of the microgeode film. This approach could be used to explore the large design space of possible structures in microgeodes and other hierarchically structured, broad-spectrum materials.

Machine-learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in large-scale molecular dynamics requires a balance among accuracy, efficiency, stability, transferability, and uncertainty quantification. Here, we benchmark two chemically scalable MLIP frameworks, neuroevolution potential (NEP) and graph atomic cluster expansion (GRACE), for 16 elemental metals and their multicomponent alloys. GRACE-FS shows higher training efficiency and generally better average accuracy, chemical transferability, and finite-temperature robustness, whereas UNEP-v1 provides substantially higher inference speed and remains competitive in selected stress and large-error metrics. We further show that chemical transferability is closely linked to high-temperature MD stability in highly multicomponent environments and that ensemble-based uncertainty provides a more reliable error indicator than D-optimality for the heterogeneous systems considered here. Finally, $3\times {10}^6$-atom shock simulations demonstrate that UNEP-v1, combined with ensemble uncertainty, enables uncertainty-aware simulations under extreme dynamic conditions, yielding robust global spall-strength predictions while revealing model sensitivity in local damage pathways. These results provide practical guidelines for selecting and deploying MLIPs in large-scale simulations of multicomponent alloys.

Recently, ${\mathrm{NiTe}}_2$ has emerged as a representative metallic transition-metal dichalcogenide (TMD), owing to its tunable electronic transport, high current-carrying capability, and experimental accessibility. However, its phonon transport behavior remains unclear. Here, we employ first-principles calculations to investigate the lattice thermal conductivity ($k_{\mathrm{p}}$) of both 1T and 1H phases for ${\mathrm{NiTe}}_2$. Our results show that in 1T phase, four-phonon (4ph) processes reduce $k_{\mathrm{p}}$ by up to 58.2%, lowering $k_{\mathrm{p}}$ from 3.79/4.69 to 1.70/1.96 W ${\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$ along the armchair/zigzag directions. Electron-phonon interaction (EPI) leads to an additional maximum 29.5% suppression, reducing $k_{\mathrm{p}}$ to 0.58 W ${\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$ at a carrier concentration of 1 × ${10}^{14} {\mathrm{cm}}^{-2}$ in $n$-type 1T-${\mathrm{NiTe}}_2$. In contrast, the 1H phase exhibits a more moderate decrease, with $k_{\mathrm{p}}$ reduced from 3.61 to 2.44 W ${\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$ by 4ph scattering. The most distinct EPI effect in 1H-${\mathrm{NiTe}}_2$ occurs in the intrinsic state, further reducing $k_{\mathrm{p}}$ to 1.57 W ${\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$. The more pronounced suppression in $k_{\mathrm{p}}$ in 1T-${\mathrm{NiTe}}_2$ originates from the larger phonon band gap and higher electronic density of states compared with 1H-${\mathrm{NiTe}}_2$. Our results suggest viable routes to tune phonon transport in TMDs via phase control and carrier engineering.

Understanding the structural origins of thermal expansion in metallic glasses (MGs) is essential for engineering next-generation materials with high thermal stability and performance. Accordingly, we used molecular dynamics simulations to investigate how local atomic motifs control the thermal expansion of MGs. Motif-resolved analysis using Voronoi tessellation reveals that mixed polyhedra are the main contributors to macroscopic thermal expansion, whereas icosahedral units act as stabilizers that resist volume changes. This interpretation is supported by motif-resolved potential-energy analysis, which shows lower, more temperature-stable energies for icosahedral polyhedra, in contrast to the strongly anharmonic response of mixed polyhedra. To connect motif-level thermal expansion to atomic-scale behavior, we analyzed bonds using the radial probability function $\left[G\right(r\left)\right]$. A skewed-normal fit to the first peak shows a monotonic increase in mean interatomic distance with temperature, resolving the apparent contraction reported in earlier studies relying solely on the position of the first peak of $G\left(r\right)$. Overall, this work establishes a coherent structural picture for thermal expansion in MGs. The consistent behavior across the investigated alloys demonstrates that icosahedral units are systematically associated with lower local expansion, while mixed polyhedra dominate the macroscopic thermal response. This motif-dependent behavior is robust across different compositions, interatomic potentials, and cooling rates, providing a basis for designing MGs with improved thermal stability.

The monolayer FeSe film grown on ${\mathrm{SrTiO}}_3$ has attracted tremendous interest due to its remarkable interface-enhanced high-temperature superconductivity and emergent quantum phenomena. However, achieving controlled growth of monolayer FeSe on the ${\mathrm{SrTiO}}_3$ substrate remains challenging. Here, utilizing an in situ molecular beam epitaxy-scanning tunneling microscopy and spectroscopy combined system, we develop a method to successfully grow monolayer FeSe films on both conductive and insulating ${\mathrm{SrTiO}}_3$ substrates through a two-step deposition approach that involves the deposition of Fe clusters and subsequent selenization. By applying postannealing, we further tune the crystallinity and superconducting properties of the as-grown film, yielding an in situ scanning tunneling spectroscopy gap of ∼15.7 meV and an ex situ macroscopic transition temperature of ∼37.2 K. This work provides a valuable route for the controlled growth of interface-enhanced high-temperature superconductors and could be branched out into growing other similar compound films.

We report a nematic emulsion in which silicone oil droplets dispersed in a nematic liquid crystal induce conic anchoring and spontaneously stabilize elastic hexadecapolar distortions. Exploiting the symmetry and angular profile of these droplets, we develop a controlled fusion strategy to assemble three-phase compound droplets by merging hexadecapolar oil droplets with dipolar glycerol droplets, thereby producing architected multilobed structures. Upon application of an electric field, these compound droplets undergo systematic morphological evolution accompanied by defect migration and topological transitions. Landau-de Gennes simulations quantitatively reproduce director fields and defect networks, revealing an underlying connection between interfacial anchoring, elastic symmetry, and emergent morphology. These findings open avenues for electrically reconfigurable nematic emulsions as platforms for programmable control of droplet geometry and topology.

We investigate the formation, diffusion, and motion of Au-Ge nanodroplets on atomically flat Ge(111) terraces under a thermal gradient. The observations are carried out in real time, by low-energy electron microscopy. Au deposition leads to nucleation of mobile nanodroplets that exhibit biased diffusion toward the hot side of the substrate. Quantitative analysis of the droplet trajectories reveals that the diffusion coefficient scales with the droplet's radius as $D_{\mathrm{drop}}\propto R^{-2}$, consistent with a rate-limiting mechanism dominated by atom attachment/detachment at the droplet-substrate interface, as a possible interpretation. The droplet velocity shows no statistically significant dependence on droplet radius within the investigated range. In contrast, the velocity increases with increasing temperature. The effective activation energy for the droplet motion is 0.5 $\pm 0.3\mathrm{eV}$. These results provide insights into the atomic-scale mechanisms governing nanodroplet dynamics on semiconductor surfaces.

The interplay of topological surface states and magnetism gives rise to the unconventional transport behaviors such as anomalous Hall effect (AHE) and anomalous Nernst effect (ANE). The antiperovskites like ${\mathrm{Mn}}_3\mathrm{SnC}$, which are nodal-line semimetal and exhibits concurrent antiferromagnetic (AFM)-ferromagnetic (FM) ordering offer a fertile ground for anomalous transport. Here we report that the anomalous transport (AHE and ANE) in the compound is mainly dominated by the intrinsic Berry curvature effect. We establish a unified relationship between the AHE and ANE using Mott's relation. The scattering of electrons with both antiferromagnetic and ferromagnetic magnons is manifested in the anomalous Nernst signal. The ratio of anomalous Nernst conductivity to anomalous Hall conductivity $(|{\alpha }_{xy}^A/{\sigma }_{xy}^A|)$ is a sizable fraction of $k_B/e$, which points toward the stronger contribution of the Berry curvature to the ANE.

Photoemissive materials are central to a wide range of technologies, including ultrafast imaging, low-light detection, and accelerator-based light sources, where they determine the fundamental performance limits of these instruments. Despite their importance, establishing quantitative links between material synthesis, electronic structure, intrinsic photoemission properties, and device performance remains a major challenge. Here, we present a comprehensive study that bridges these aspects for Na–K–Sb photocathodes, combining controlled thin-film growth, in situ spectroscopic analysis, photoemission characterization, and investigation of the operational performance. We developed a reproducible synthesis method yielding highly homogeneous films with quantum efficiencies up to $7.5\%$ at $520\mathrm{nm}$ and a stoichiometry close to the predicted optimum. Electronic structure and photoemission properties were analyzed in a dedicated laboratory environment. Furthermore, we demonstrate the successful operation of these photocathodes in a superconducting radio-frequency photoinjector, where the photocathodes are exposed to extreme conditions, forming a particularly stringent test environment. The operational performance was analyzed over several weeks, including detailed investigation of the degradation and recovery mechanisms associated with gas exposure and electron back-bombardment. The photoemissive threshold energy was identified as a bridging parameter connecting electronic structure, photoemissive key parameters, and the degradation state. The approach provides general design principles for the development and integration of photoemissive materials across a broad range of applications. Together, these results establish a quantitative framework linking synthesis, electronic structure, and operational performance in photoemissive materials.

Uranium nitrides (UN, ${\mathrm{U}}_2{\mathrm{N}}_3$, and ${\mathrm{UN}}_2$) are candidate materials for advanced nuclear fuels and corrosion-resistant coatings, yet achieving a unified atomic-scale model that accurately captures their behavior across stoichiometries and under extreme conditions remains challenging. In this work, we present a unified machine learning interatomic potential (MLIP) for the entire U–N system, developed within the neuroevolution potential framework. The potential is trained on a comprehensive density functional theory (DFT) dataset constructed through an iterative active learning procedure that effectively samples configuration space using farthest point sampling and random network distillation. The resulting MLIP accurately reproduces structural, elastic, vibrational, and defect properties of all major uranium nitride phases, showing close agreement with DFT and experimental reference data. It reliably predicts finite-temperature thermodynamics, defect migration barriers, nitrogen diffusion kinetics, and the stability of nonstoichiometric compounds. This transferable and efficient potential enables large-scale molecular dynamics simulations of key processes in uranium nitrides, providing a robust and efficient computational tool for microstructural evolution and phase stability in next-generation nitride-based nuclear systems.

We report on the synthesis and characterization of $\mathrm{M}{\mathrm{n}}_3\mathrm{Ga}$ thin films with controlled phase and orientation using DC magnetron sputtering. High-quality Mn-deficient hexagonal $\mathrm{M}{\mathrm{n}}_3\mathrm{Ga}$ films with C-plane and M-plane orientations were achieved on various single-crystal substrates, and their structural properties were systematically investigated by x-ray diffraction. Magnetization, Hall effect, and neutron diffraction measurements confirm the formation of antiferromagnetic order below $T_{\mathrm{N}}=460\text{–}470$ K and reveal its characteristic magnetic behavior. A sizable anomalous Hall effect is observed coexistent with weak net magnetization, consistent with the 120° noncollinear spin structure. The ability to tune both the crystal phase (hexagonal vs tetragonal) and orientation through composition and thermal treatment provides a platform for exploring the intrinsic magnetic and topological properties of $\mathrm{M}{\mathrm{n}}_3\mathrm{Ga}$, with promising implications for antiferromagnetic spintronic applications.

Magnetic refrigeration presents an energy-efficient and environmentally benign alternative to traditional vapor-compression cooling technologies. It relies on the magnetocaloric effect, in which the temperature of a magnetic material changes in response to variations in the applied magnetic field. Optimal magnetocaloric materials are characterized by a significant change in magnetic entropy under moderate magnetic fields. In this study, we systematically investigated the interatomic exchange interactions, magnetic anisotropy energy and magnetocaloric properties of $\mathrm{Mn}X$ ($X=\mathrm{N}$, P, As, Sb, Bi) using a combination of density functional theory and Monte Carlo simulations. Additionally, the magneto-optical Kerr and Faraday spectra were computed using the all-electron, fully relativistic, full-potential linearized muffin-tin orbital method. The large magneto-optical response observed in MnBi originates from the interplay between the strong exchange splitting of Mn $3d$ states and the substantial spin-orbit interaction associated with the heavy Bi atom. To extract the site-projected spin and orbital moments, spin-orbit coupling and orbital polarization correction were accounted for in the present calculation, which shows good agreement between the moments obtained from the x-ray magnetic circular dichroism sum rule analysis, spin-polarized calculation, and experimental studies. The magnetic transition temperatures predicted through Monte Carlo simulations were in good agreement with the corresponding experimental values. Our results provide a unified microscopic understanding of magnetocaloric performance and magneto-optical activity in Mn-based pnictides and establish a reliable computational framework for designing next-generation magnetic refrigeration materials.

Amorphous materials have long been used in various technological applications, including semiconductor-based electronics. However, modeling amorphous materials at an atomic scale remains challenging. The melt-quench method is commonly used to generate models of amorphous structures, but the necessary lengthy molecular-dynamics runs can make calculations prohibitively expensive, especially for ab initio molecular dynamics. Furthermore, the relevance of melt-quench to athermal techniques like low-temperature deposition is unclear. We present an alternative method for constructing amorphous structures that starts with randomly placing molecules in a large box, followed by relaxation of the cell and atomic positions. We apply this approach to a variety of amorphous systems, using ab initio density-functional theory for the energetics, and obtain good agreement with experimental densities and radial distribution functions with less computational cost than the melt-quench method.

Understanding and predicting mechanical properties such as elasticity and ductility are critical for the design of structural materials. Here, we develop a generalizable machine-learning framework to assess elastic properties and brittle-ductile tendencies across a chemically diverse space of inorganic compounds, including both binary and ternary systems. By integrating stacked ensemble learning and symbolic regression, we achieve strong predictive performance (e.g., $R^2=0.95$ for bulk modulus, $R^2=0.88$ for shear modulus, and ductile/brittle classification accuracy of 88%)—notably high for a general-purpose model spanning diverse chemistries. We also derive simple closed-form expressions using elemental and structural descriptors, enabling quick property estimation in practical settings. Finally, we apply the models to out-of-training-set compounds, including quaternary systems, and demonstrate their effectiveness in real applications. Relying only on several key basic descriptors, the physically interpretable models developed here are particularly valuable for quick screening workflows—especially for experimentalists without access to ab initio simulations.

It is well documented that the contact angle of droplets catalyzing the vapor-liquid-solid growth of III-V binary nanowires (NWs) plays the central role in their crystal phase, which can be either cubic zincblende or hexagonal wurtzite. Here, we develop a model for the contact angle selection in III-V ternary NWs. We demonstrate that the stable contact angle of ternary $A_x{B}_{1-x}C$ NWs based on group-III intermix increases with their composition $x$ for larger incorporation rates of atoms A relative to B and decreases with $x$ otherwise. This trend is reversed for $A_x{B}_{1-x}C$ NWs based on group-V intermix. The effect is due to the total balance of the effective V/III ratio for atomic fluxes entering the droplet and used for NW growth. We present some calculations of the stable contact angles vs composition for ${\mathrm{Al}}_x{\mathrm{Ga}}_{1-x}\mathrm{As}$, ${\mathrm{In}}_x{\mathrm{Ga}}_{1-x}\mathrm{As}$, and ${\mathrm{GaP}}_x{\mathrm{As}}_{1-x}$ NWs and compare them to the recently obtained crystal phase diagrams. The developed approach unravels the role of NW radius, diffusion lengths of group-III adatoms, incorporation rates of group-V atoms in the contact angle selection, and the related crystal phase change and should be useful for understanding and controlling the crystal phase of III-V ternary NWs by the growth parameter tuning.

Thermal hysteresis is a critical design parameter for shape memory materials, particularly for shape memory ceramics, where high hysteresis may be associated with catastrophic cracking upon transformation. A direct framework for predicting hysteresis from the atomistic level does not exist, so proxies such as the interfacial compatibility condition, ${\lambda }_2$, are used for the computational design of shape memory materials. In this paper, we introduce a framework that directly links interfacial compatibility to the energy barriers encountered during the phase transformation. We derive these barriers using adjusted Nudged Elastic Band calculations for two prototypical shape memory systems: zirconia-ceria and NiTi. Although our method does not explicitly simulate the martensite-austenite interface, we demonstrate that the structural pathway followed during the transformation samples the same atomistic structures as that interface. Furthermore, we show that the different correlations observed between the two material classes are fundamentally linked to differences in their shear moduli. These results help explain why our results reveal a strong correlation between interfacial compatibility and the transformation energy barriers: the atomic distortions required to effect the transformation are present whether at interfaces or not, and are accommodated elastically. This approach establishes a computationally efficient, quantum-accurate, method for determining energy barriers directly linked to thermal hysteresis, facilitating the design of resilient shape memory materials.