503 papers
This paper proposes a mechanism for nonvolatile electrical manipulation of spin and layer degrees of freedom in altermagnetic bilayers, such as CuF2, by utilizing sliding ferroelectricity to reversibly switch d-wave altermagnetic spin splitting, thereby enabling multifunctional spin-layertronic devices with potential multi-state logic applications.
This paper presents a comprehensive atlas of approximately 1.95 million cubic symmetric metamaterials derived from all 36 cubic space groups, revealing extreme mechanical properties like high bulk-to-shear ratios and negative Poisson's ratios, while introducing a 3D convolutional neural network surrogate model to accelerate the discovery and design of such architected materials.
This study demonstrates that Path Integral Molecular Dynamics simulations reveal nuclear quantum effects significantly accelerate the thermal decomposition of the TATB crystal and lower its activation energy by approximately 8% compared to classical methods, while highlighting that the Quantum Thermal Bath approximation substantially overestimates these quantum acceleration effects.
This study demonstrates that nonlinear long-range electron-phonon interactions significantly influence the finite-temperature carrier mobility of the anharmonic halide perovskite CsPbI, altering its temperature scaling and contributing approximately 10% to the mobility at room temperature, thereby highlighting the necessity of including such effects in theoretical models of these materials.
This paper presents a computationally efficient framework for calculating the zero-temperature superfluid weight and magnetic penetration depth from first principles by separating conventional and quantum geometric contributions, thereby enabling large-scale screening of superconductors and validating the method against experimental data for conventional materials.
This paper presents a computationally efficient microscopic screening theory for excitons in two-dimensional materials that bridges the gap between effective models and first-principles methods by employing an atomistic description with quantum-screened interactions to accurately estimate binding energies and address discrepancies in existing literature.
This paper utilizes first-principles calculations to demonstrate that the sliding barrier of small islands on a graphene-covered substrate, rather than electrostatic potential, serves as the most rigorous metric for predicting the viability of remote epitaxy, suggesting the phenomenon is governed by island migration kinetics.
High-precision heat-capacity measurements of submonolayer He on graphite reveal a second-order transition between two striped domain-wall phases below 1 K, where the melting of the fixed-spacing phase into the variable-spacing phase supports the existence of a quantum nematic state characterized by one-dimensional phonons.
By combining *ab initio* calculations with generalized Schwinger-boson mean-field theory, this study demonstrates that while exchange coupling to decorating sites stabilizes a gapped quantum-paramagnetic phase in the square-kagome antiferromagnet NaCuBiO(PO)Cl, symmetry-allowed Dzyaloshinskii-Moriya interactions suppress the spinon gap and drive the system toward a magnetic instability.
The paper introduces Neural Field Thermal Tomography (NeFTY), a differentiable physics framework that parameterizes 3D material diffusivity as a continuous neural field optimized via a rigorous numerical solver to achieve high-resolution, quantitative reconstruction of subsurface defects from transient surface temperature measurements, overcoming the limitations of traditional 1D approximations and soft-constrained PINNs.
This paper introduces a bidirectional learning framework that links local atomic environments to electronic band dispersion in semiconductor heterostructures using atomically resolved spectral functions, enabling both the prediction of electronic bands from atomic structures and the inference of atomic descriptors from spectroscopic data.
This paper proposes a machine learning approach that learns data-driven, structure-preserving (symplectic and time-reversible) maps equivalent to the mechanical action of a system, enabling accurate long-time-step molecular dynamics simulations that eliminate the energy conservation and equipartition artifacts typical of non-structure-preserving ML predictors.
This paper introduces the Tensor Atomic Cluster Expansion (TACE), a unified atomistic machine learning framework that employs irreducible Cartesian tensors to efficiently model both scalar and tensorial observables without complex angular-momentum coupling, demonstrating robust accuracy and scalability across diverse chemical systems and tasks.
This study employs density functional theory calculations to demonstrate that oxygen vacancies in the Fe(MoO) catalyst cause the experimentally observed Raman intensity reduction through oxygen-dominated vibrational modes, while rapid oxygen diffusion from the bulk to the surface preserves local symmetry, thereby explaining the absence of peak shifts or broadening.
This study calculates the vibrational linewidths of chemisorbed hydrogen on molybdenum and tungsten surfaces using first-order time-dependent perturbation theory, revealing strong coverage dependence and mode-selective electron-phonon coupling that aligns with experiments for Fano-shaped peaks but underestimates Lorentzian peaks, suggesting that adsorbate-adsorbate interactions become significant energy dissipation channels at high coverages.
This paper demonstrates a high-efficiency spin-Hall oscillator based on a perpendicular magnetic anisotropy gallium-substituted yttrium-iron-garnet (Ga:YIG) that achieves current-controlled spin-wave emission with an extended bandwidth, offering a promising platform for neuromorphic computing applications.
This paper rigorously derives the exact properties of the ensemble ground state for many-electron systems with fractional electron numbers and spin projections, resolving ground-state ambiguities in low-spin cases via entropy maximization, characterizing high-spin dependencies, and establishing generalized ionization potential theorems and new derivative discontinuities to advance density functional theory approximations.
This paper establishes a necessary and sufficient condition for the finite Prony representability of linear viscoelastic models by analyzing the alignment of arithmetic pole lattices in the Mellin transform of the complex modulus, thereby providing a complete analytical taxonomy that classifies classical models as finitely representable and fractional or log-normal models as requiring infinite Prony ladders.
The paper introduces NMRPeak, a unified cross-modal learning system trained on a large-scale experimental and simulated dataset that bridges the simulation-to-experiment gap to achieve high-accuracy molecular retrieval and de novo structure generation for automated NMR-based structure elucidation.
MolCrystalFlow is a novel flow-based generative model that predicts molecular crystal structures by disentangling intramolecular complexity from intermolecular packing through rigid body embeddings and Riemannian manifold representations, thereby outperforming existing methods and enabling data-driven discovery of periodic molecular crystals.