528 papers
This paper presents a shift-invariant deep learning framework using Spatial Transformer Networks that effectively corrects for spectral shifts in X-ray Photoelectron Spectroscopy data, achieving high accuracy in identifying chemical functional groups and advancing automated material analysis.
This paper presents an automated computational workflow that integrates adaptive annealing to efficiently screen large polymer libraries, thereby generating consistent data suitable for machine learning models to predict key properties like density and glass transition temperature.
This paper addresses the challenges of modeling Y6 non-fullerene acceptors by tuning a range-separated hybrid functional to account for solvatochromic effects and oscillator strength borrowing in solid-state dimers, while cautioning that standard range-separated hybrids may be less accurate than global hybrids and proposing that reducing the range-separation length can improve accuracy without complex tuning.
This study demonstrates that atomic layer deposition (ALD) of tantalum nitride (TaN) films yields thermally stable, insulating tunnel barriers with a band gap of 1.5–1.8 eV and uniform composition, making them a superior alternative to aluminum oxide for fabricating high-quality Josephson junctions in superconducting quantum circuits.
This paper extends first-principles spin-lattice relaxation theory to include three-phonon processes, demonstrating that while these contributions are negligible for the studied Chromium nitride complex under experimental conditions—thereby validating the weak coupling assumption—the framework reveals that slightly stronger coupling could make three-phonon effects significant at room temperature.
This study establishes a validated computational framework using rSCAN+ density functional theory to predict the equilibrium morphologies of rock salt, zinc blende, and wurtzite manganese sulfide nanocrystals as a function of sulfur chemical potential, a prediction that is experimentally confirmed by the synthesis of cubic rock salt nanocrystals and oxidative solution calorimetry measurements.
This study experimentally validates the transferability of a machine-learned interatomic potential across different thermodynamic states by demonstrating its ability to accurately reproduce high-pressure inelastic neutron spectroscopy data of crystalline 2,5-diiodothiophene, thereby establishing high-pressure INS as a rigorous benchmark for predictive computational chemistry.
This paper demonstrates the room-temperature coherent propagation of high-velocity antiferromagnetic spin waves over approximately 10 micrometers in canted -FeO, achieving group velocities up to 22.5 km/s through the Dzyaloshinskii-Moriya interaction and validating these findings with an analytical model of quasi-linear dispersion.
This study reports the fabrication of a wafer-scale amorphous silicon carbide thin film with a record-breaking ultimate tensile strength exceeding 10 GPa and room-temperature quality factors above 10^8, establishing a new benchmark for high-performance nanomechanical sensors and dynamic applications.
This study employs a fully self-consistent, nonperturbative approach to demonstrate that electron-spin coupling significantly renormalizes Heisenberg exchange coupling parameters, thereby enabling the construction of quantitatively reliable spin models that accurately predict magnetic phase-transition temperatures in diverse materials.
This paper introduces the concept of pair-induced uniaxial anisotropy in disordered magnetic systems, demonstrating through density functional theory calculations on GaMnN that accounting for nearest-neighbor interactions significantly improves the accuracy of atomistic spin simulations compared to traditional single-ion anisotropy models.
This study demonstrates that epitaxial RuO2 thin films exhibit a robust, anisotropic spin Hall effect with a negative spin Hall angle across various fabrication methods and orientations, conclusively showing the absence of altermagnetic spin-splitting contributions despite the material's classification as a prototypical altermagnet.
This study establishes a computational framework linking the random interlayer stacking of mesoscopic 1-TaS flakes to their complex electronic properties, demonstrating that Hubbard repulsion drives a coexistence of metallic, band, and Mott insulating states that cannot be explained by ordinary band theory.
This study reveals that while atomically thin monolayer and bilayer MnTe lose their bulk altermagnetic properties due to symmetry constraints, they instead exhibit distinct emergent magnetic phases characterized by robust layered antiferromagnetism in bilayers and unprecedented spin-glass-like behavior in monolayers.
This study demonstrates that twisted magnonic crystals can generate magnonic frequency combs through enhanced three-magnon interactions driven by non-collinear magnetic configurations, revealing an optimal range of twist angles and excitation frequencies for producing high-quality combs with potential applications in information processing and metrology.
This study demonstrates a method to determine the electric field gradient principal axes frame in single-crystal potassium chlorate via Rabi frequency goniometry and characterizes spin relaxation mechanisms at cryogenic temperatures (17–200 K) using a cryogen-free system to enhance the accessibility of NQR spectroscopy.
This study reveals that solute interstitial segregation induces barrier-free nucleation of distinct disconnection states via grain boundary phase transitions, fundamentally altering grain boundary kinetics in alloys by promoting amorphization, pure sliding, and precipitate nucleation.
This paper introduces a deep learning framework using a lightweight convolutional neural network to automate the rapid and objective identification of weak-beam imaging conditions in Dark-Field X-ray Microscopy, thereby enabling scalable, non-destructive, and statistically significant 3D characterization of dislocations in bulk crystalline materials.
This study demonstrates that chemical patterning in amorphous interfacial complexions of Cu-rich nanocrystalline alloys, driven by dopant segregation to amorphous-crystalline transition regions, directly modulates local structural short-range order, thereby enabling new avenues for microstructural engineering.
This paper presents the experimental demonstration of a fully interconnected all-magnonic neuron on an ultra-low damping garnet that utilizes tunable nonlinear activation to achieve thresholded firing, self-resetting, and a controllable fading memory capable of integrating up to 50 pulses and cascading across three neurons.