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.
This paper demonstrates that in materials graph neural networks, the ability to transfer to underrepresented chemical regions shifts from relying on static element priors to leveraging target-containing structures, where adding even a few such structures significantly reduces formation-energy errors and diminishes the impact of input representation choices.
This study identifies exchange striction—the sensitivity of exchange coupling to atomic displacements—as the key material parameter determining the ultrafast demagnetization speed of antiferromagnetic insulators, enabling the prediction and screening of candidate materials for high-speed memory applications.
This study utilizes excitation-energy-dependent photoluminescence excitation spectroscopy to demonstrate that resonant excitation drives dipolar interlayer excitons in MoSe/WSe heterobilayers into a nonlinear, high-density regime characterized by emission saturation and a population-dependent blueshift, providing direct evidence of net repulsive exciton-exciton interactions dominated by dipole-dipole forces.
This paper introduces "geometronics," a concept demonstrating how substrate topography can locally reorient anisotropic van der Waals crystals to transform uniform external perturbations into programmable, switchable magnetic and electronic landscapes without altering the material's composition.
This paper introduces the quasi-normal-mode spectrum as a complementary framework for characterizing finite stealthy hyperuniform materials, revealing systematic signatures of structural correlations in resonant states that are not captured by the traditional structure factor.
This paper demonstrates that Shubnikov-de Haas oscillations, analyzed with a method that accounts for superconducting shunting, can successfully characterize the normal-state electronic parameters of buried semiconductor quantum wells beneath various superconducting films, revealing that while interface subbands form, the intrinsic properties of the underlying well remain largely unchanged and its quantum lifetime is preserved or enhanced.
The paper introduces SHAARP, an open-source Python package that streamlines the analytical and numerical modeling of optical second harmonic generation in anisotropic crystals by implementing five key generalizations to accurately handle complex material symmetries, orientations, and optical properties.
This paper introduces the open-source SHAARP.ml package, which provides exact closed-form analytical solutions for optical second harmonic generation in anisotropic multilayers by fully accounting for multireflections of both fundamental and nonlinear waves, thereby eliminating the approximations inherent in existing methods and enabling accurate material characterization.
This study characterizes the resistivity, Hall coefficient, and mobility of a doped GaAs sheet as an n-type semiconductor using the Van der Pauw method and Hall Effect measurements across varying temperatures and magnetic fields, revealing that charge accumulation is quadratic in the magnetic field and that electron mobility decreases with increasing temperature.
This paper introduces a model-free framework utilizing escort-weighted Shannon entropy and various divergence matrices to sensitively detect phase transitions and statistical changes in scattering and imaging data without requiring explicit physical models or order parameters.