528 papers
This paper analytically investigates the spin wave dispersion and dielectric response in multiferroic materials with cycloidal spin structures, revealing how magnetoelectric coupling and the equilibrium wave vector influence anisotropy contributions and potentially induce instability.
This study demonstrates the epitaxial growth of high-quality, ultrathin bismuth-substituted yttrium iron garnet (BiYIG) films with tunable magnetic anisotropy and low damping via strain engineering and growth parameter optimization, highlighting their potential for advanced spin-orbitronic and magnonic devices.
This study utilizes atomistic and multiscale simulations to demonstrate that misfit and threading dislocations significantly reduce the surface energies of PbTe-PbSe interfaces, with heteroepitaxial processes yielding up to 50% lower energy compared to coherent interfaces.
This paper reviews and extends the generalized Keffer form of the Dzyaloshinskii constant by combining three known contributions and suggesting a fourth, analyzing their macroscopic consequences for spin, momentum, and polarization evolution while also proposing an analogous form for the exchange integral in symmetric Heisenberg Hamiltonians.
This paper presents the development of a deep prior-based denoising system integrated into a micro-focused soft X-ray ARPES setup at SPring-8, which effectively removes noise and significantly reduces measurement time to approximately 40 seconds while maintaining high statistical accuracy and energy resolution.
This study demonstrates through 3D quantum wave packet calculations that bilayer graphdiyne membranes exhibit enhanced quantum transport and interlayer-separation-dependent resonances compared to monolayers, suggesting their superior potential for isotope separation applications.
This study employs a hierarchical approach combining density functional theory and custom-trained machine-learned interatomic potentials to perform high-throughput crystal structure prediction of zinc imidazolate, successfully identifying thousands of new energy minima and topologies while validating the method against experimental data and proposing promising candidates for future synthesis.
This study uses density functional theory to demonstrate that specific multi-component cubic halide perovskite alloys can simultaneously exhibit negative mixing enthalpy and rare upward band gap bowing due to s-s orbital repulsion, enabling the design of stable alloys with band gaps larger than any of their individual constituents.
This study establishes practical thin-film synthesis routes for -carbide type iron nitrides in Fe-W-N and Fe-Mo-N systems, revealing that Fe-Mo-N offers a broader stability window and exhibits tunable ferromagnetism dependent on stoichiometry, unlike the more compositionally restricted Fe-W-N.
This paper proposes a novel symmetry-driven generative framework that combines large language models for chemical semantics with a linear-complexity heuristic beam search to rigorously enforce algebraic consistency in Wyckoff patterns, thereby overcoming the combinatorial bottleneck in crystal structure prediction to achieve state-of-the-art performance in discovering new materials without relying on existing databases.
This study reveals that Moiré graphene domain-wall networks undergo hierarchical symmetry breaking driven by lattice relaxation, leading to the spontaneous emergence of distinct chiral network morphologies that fundamentally alter the localization and spectral characteristics of their topologically protected electronic states.
This review advocates for the adoption of microphysiological systems (MPS) as advanced, physiologically relevant models to overcome the limitations of traditional methods in assessing nanotoxicity, thereby enabling safer and more reliable risk-benefit evaluations for nano-enabled food innovations.
This study characterizes the 2017 Mukundpura CM2 carbonaceous chondrite from Rajasthan, India, using high-resolution microscopy and Raman spectroscopy to confirm the presence of 3–5 nm nanocrystalline diamonds alongside graphitic carbon and abundant iridium, offering insights into stellar evolution and the geological impact anomalies associated with mass extinctions.
This study demonstrates that Superhydrophobic Sand (SHS) mulch significantly reduces soil evaporation in arid regions by shifting the process from a temperature-controlled regime to a diffusion-limited one, with effectiveness governed by mulch thickness and soil thermal properties.
This study presents a high-throughput computational framework that successfully identifies seven bifunctional organic semiconductors, including an optimal candidate with 36.1% predicted power conversion efficiency and strong protein-binding affinity, by balancing photovoltaic performance against synthetic accessibility.
This paper introduces OrbEvo, an equivariant graph transformer model that efficiently predicts time-dependent electronic wavefunctions and related physical properties under external fields by learning to evolve orbital coefficients through autoregressive rollout, thereby overcoming the computational bottlenecks of conventional real-time time-dependent density functional theory.
First-principles calculations demonstrate that applying in-plane tensile strain significantly reduces the ionization energies of n-type dopants in AlN, particularly by stabilizing the Si donor and shifting S and Se levels closer to the conduction band, thereby offering an effective route to enhance electron concentrations for deep-ultraviolet light sources.
This study demonstrates that engineering the dielectric interface in van der Waals heterostructures by incorporating Clinochlore substrates significantly enhances the emission intensity and modifies the radiative dynamics of single-photon emitters in monolayer WSe through coupling with substrate-specific Fe-related states, despite a trade-off in single-photon purity.
This paper introduces a data-efficient Quantile Regression Tree-based Active Learning strategy that successfully identifies spin-crossover metal-organic frameworks from limited and noisy computational data, enabling the discovery of a new high-confidence candidate set (pSCO-105) while overcoming the challenges of large-scale geometry optimization.
This article proposes that dynamically self-organizing systems, ranging from solidifying metals to bird flight, consistently select pathways that maximize the entropy generation rate to redistribute internal energy, form patterns, and maintain system resilience.