4820 papers
Condensed matter physics and materials science form a dynamic partnership, exploring how the collective behavior of atoms gives rise to the unique properties of solids and liquids. This field bridges the gap between fundamental quantum mechanics and the practical engineering of everything from flexible electronics to superconductors, turning abstract theories into tangible innovations that shape our daily lives.
At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex discoveries accessible to everyone. Our team generates both plain-language overviews and detailed technical summaries for each paper, ensuring that researchers, students, and curious minds alike can grasp the latest breakthroughs without getting lost in dense jargon.
Below are the latest papers in condensed matter and materials science, organized by their most recent publication dates.
By integrating synchrotron X-ray pair distribution function analysis with machine learning-accelerated molecular dynamics simulations, this study elucidates how the interplay between linker geometry, aromatic rigidity, and non-covalent interactions governs the distinct local structural flexibility and dynamic behavior of two three-dimensional imine-linked covalent organic frameworks.
This paper demonstrates that a super-resolution generative adversarial network (SRGAN) significantly enhances the throughput of electron backscatter diffraction (EBSD) analysis for Li-ion battery electrode materials by computationally upscaling low-resolution data with higher accuracy in preserving microstructural features compared to classical interpolation methods.
This review synthesizes multi-scale evidence to demonstrate that avalanche-like plasticity in complex concentrated alloys arises from diverse, tunable mechanisms rather than a single universal phenomenon, revealing that seemingly smooth macroscopic flow masks intermittent, collective defect dynamics at finer scales.
This study utilizes unpolarized small-angle neutron scattering to characterize magnetic correlations in rare-earth-free Mn-Bi permanent magnets, revealing that their remanent state features long-wavelength transversal magnetization fluctuations with a radius of gyration of 220–240 nm originating from slightly shape-anisotropic structures.
This paper presents a theoretical framework based on Brown's micromagnetics equations for computing the uniaxial polarization of scattered neutron beams from bulk ferromagnets, which is validated against experimental data from a soft magnetic nanocrystalline alloy to establish a new approach for analyzing magnetic microstructures.
This paper presents an analytical treatment of the magnetization profile and magnetic small-angle neutron scattering cross section for a spherical nanoparticle with Néel surface anisotropy, deriving an approximate solution via perturbation theory and validating it against numerical Landau-Lifshitz simulations.
This study employs atomistic simulations based on the Landau-Lifshitz equation to investigate the magnetization structure and magnetic small-angle neutron scattering response of noninteracting spherical nanoparticles, specifically analyzing the impact of Néel surface anisotropy and particle-size distributions while comparing numerical results with analytical models to guide experimental interpretation.
This paper utilizes micromagnetic simulations to investigate how pore-type defects and dipolar interactions influence the magnetic small-angle neutron scattering signatures and pair-distance distribution functions of spherical nanoparticles, providing experimentalists with valuable guidelines for interpreting their data.
Motivated by recent experimental results, this numerical micromagnetic study demonstrates that interfacial Dzyaloshinskii-Moriya interactions in nanocrystalline terbium induce spin misalignment between adjacent grains, causing the asymmetric scattering patterns observed in polarized neutron experiments and highlighting the DMI's generic impact on the macroscopic magnetic properties of defect-rich polycrystalline materials.
This study demonstrates that non-contact atomic force microscopy, enhanced by machine learning-accelerated structure searches, can directly image and distinguish the precise 3D stereochemistry of individual carbohydrate anomers, revealing how glycosidic bond configurations govern on-surface chiral selection.