2794 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.
This study demonstrates that thermally-activated epitaxy at temperatures exceeding 1000°C enables the reproducible growth of high-quality NbO films with superior structural and transport properties, thereby establishing a prototypical understanding of NbO's electrical behavior and highlighting the utility of high-temperature synthesis for refractory metal compounds.
This paper introduces a model charge density approach to Ewald summations that accelerates convergence by canceling multipole moments of the crystalline charge distribution, a method validated on gallium arsenide and shown to clarify long-standing implementations in the CRYSTAL code.
This paper demonstrates that incorporating quantum-chemical bonding descriptors into machine learning models significantly improves the prediction of elastic, vibrational, and thermodynamic properties of approximately 13,000 solid-state materials while also enabling the discovery of intuitive physical expressions for these properties.
This paper establishes a unified framework connecting non-Hermitian Zeeman quantum geometry, local Dirac-node topology, and measurable transport signatures by demonstrating that the Zeeman quantum geometric tensor decomposes into normal and anomalous sectors, where the latter reveals a novel curvature-flux representation of local topology and distinct linear response scalings.
This study employs numerical simulations based on modified Fick's law and dynamic bond percolation to model solvent diffusion during the solvothermal exfoliation of transition metal dichalcogenides, revealing how diffusivity and reaction time influence nanoparticle size evolution, uniformity, and saturation through entropy analysis.
By employing interpretable machine learning models on first-principles molecular descriptors, this study reveals that human odor perception lacks a universal physicochemical determinant, instead relying on heterogeneous, odor-specific patterns of feature importance that reflect unique structure-odor relationships for each scent.
This study utilizes scanning transmission electron microscopy and electron energy loss spectroscopy to correlate the structural polymorphs and oxygen stoichiometry of epitaxial LaNiO thin films with their superconducting properties, establishing a framework for stabilizing superconductivity in bilayer nickelates through precise ozone-driven structural and oxidation tuning.
This study demonstrates that silicon $DX$ centers induce significant self-compensation in ultrawide-bandgap nitrides like AlN by stabilizing a negative charge state, which severely limits free electron concentrations and renders them largely independent of doping levels, although higher carrier densities may be achievable in AlGaN alloys or cubic boron nitride where the $DX$ level is closer to the conduction band.
This study demonstrates that electrostatic interactions between fluorinated phenyl ligands, rather than other factors, are the primary driver for selecting specific structural motifs in silver selenide-based metal-organic chalcogenides, establishing a design principle for controlling crystal structures through targeted ligand packing.
This paper introduces an enhanced non-contact steady-state temperature differential radiometry (SSTDR) platform that successfully measures the thermal conductivity and spectral emittance of molybdenum up to 3000 K with 7.9–11% uncertainty, effectively addressing critical data gaps for ultrahigh-temperature applications.