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 heavily doping cubic silicon carbide with nitrogen, researchers broke the long-standing rule that ferroelectricity and metallicity are mutually exclusive, creating a ferroelectric metal that exhibits atomic-scale polarization reversal and high-performance nonvolatile memory capabilities.
This paper introduces a numerically exact method called the finite-temperature Green's function cluster expansion, which combines generalized cluster expansions with the thermofield double formalism to compute momentum- and frequency-resolved polaron spectral functions at finite temperatures without requiring time evolution or analytic continuation.
This study utilizes complementary Sagnac magneto-optics and harmonic Hall measurements to demonstrate that interface engineering in Co/Gd-based synthetic ferrimagnets enables precise quantification and optimization of spin-orbit torques, revealing the active role of rare-earth layers in torque generation and facilitating low-power current-induced magnetic switching.
This paper introduces a graphite-gated architecture for bilayer graphene that completely isolates the active device from natural edges, enabling the fabrication of fully electrostatically defined Hall-bars with disordered boundaries and field-effect transistors exhibiting record-high quantum mobility of 2.5 × 10⁶ cm²/Vs.
This study demonstrates that by systematically combining different template vesicles, catalytic polymers, and amphiphiles, researchers created synthetic minimal cell variants exhibiting distinct, composition-dependent growth responses that map a multi-dimensional fitness landscape, thereby establishing a physicochemical pathway toward evolvable, self-reproducing artificial cells.
This study demonstrates room-temperature polarity control of the anomalous Nernst effect in high-magnetic-anisotropy MnAlGe thin films through Al/Ge compositional tuning, enabling enhanced thermoelectric output in practical devices by modulating intrinsic Berry curvature via sublattice-selective carrier doping.
By combining operando Raman spectroscopy with DFT calculations, this study reveals that applied electric fields induce mode-selective electron-phonon coupling in 2D halide perovskites involving hybrid organic-inorganic vibrations, where fluorination enhances carrier-mediated lattice responses and structural order dictates opposing phonon lifetime evolutions in thin films versus single crystals.
This paper demonstrates that incorporating parity labels into machine learning model features is a critical design choice that guarantees physically exact predictions for symmetry-forbidden properties in centrosymmetric crystals, whereas models lacking this feature produce physically impossible errors in nearly all cases without sacrificing accuracy.
This study demonstrates that He ion irradiation enables controllable magnetic phase engineering in layered CrSBr, where polarization-resolved Raman spectroscopy serves as a sensitive, non-destructive tool to identify irradiation-dose- and thickness-dependent transitions from antiferromagnetic to ferromagnetic and defect-related magnetic states.
The paper introduces DPA4C, a co-designed equivariant machine-learning potential that achieves near-first-principles accuracy across diverse chemical systems while operating at the speed and scalability of empirical potentials, enabling multimillion-atom molecular dynamics simulations on single GPUs.