4772 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 tailoring the thickness of Fe/-Sn heterostructures leverages long-range magnetic proximity effects to suppress topological surface states at one interface while opening an exchange gap at the other, resulting in a record-high room-temperature inverse Edelstein length of -3.1 nm at a 25 nm thickness.
This paper demonstrates a versatile and reproducible CO-laser thermal preparation method that successfully produces atomically flat, chemically controlled surfaces across a diverse range of oxide substrates and crystallographic orientations, including challenging polar surfaces, thereby establishing a robust platform for high-quality epitaxial growth and interface engineering.
This paper introduces a fully automated workflow for Wannier disentanglement that systematically determines energy ceilings based on target projection counts and projectability analysis, demonstrating that freezing all Bloch states below this threshold generally yields lower band errors than projectability-based selection while seamlessly integrating with WIEN2k and CRYSTAL codes for high-throughput applications.
This study demonstrates that incorporating yttrium into amorphous Ge-Te-Se phase-change materials enhances AC conductivity by increasing localized tail states, with dielectric relaxation and charge transport mechanisms identified as correlated barrier hopping, suggesting strong potential for future phase-change memory applications.
This study reveals that the weak anomalous Hall and Nernst effects in hexagonal MnTe arise from symmetry-enforced Berry curvature cancellation near the valence band maximum, and demonstrates that applying volume-conserving biaxial strain can lift this cancellation to significantly enhance these transport responses while preserving the material's zero net magnetization.
This paper demonstrates that photonic crystals can mimic altermagnetism by hosting spin space group symmetries to create spin-split bands and enable circularly polarized light isolation without geometrodynamic spin-orbit interaction, offering a versatile platform for photospintronic applications.
This study combines molecular dynamics simulations and machine learning to reveal that high-pressure densification enhances the ductility and toughness of amorphous silica by altering local bonding topologies, thereby establishing a predictive link between initial atomic configurations and fracture propensity.
Using scanning tunneling microscopy and spectroscopy, this study reveals a sizable, homogeneous superconducting gap on the pristine surface of PtBi containing spatially modulated in-gap states identified as surface-extended Andreev bound states consistent with a chiral pairing scenario, thereby establishing a direct real-space link between the material's unconventional surface superconductivity and exotic quasiparticle excitations.
This paper presents a highly efficient boundary integral method that generalizes electron repulsion integral calculations to heterogeneous dielectric environments by combining Nyström discretization, adaptive quadrature, and fast multipole acceleration to accurately model screening effects in complex 2D material devices.
This study stress-tests six foundation machine-learned interatomic potentials on disordered carbon systems, revealing that low training errors do not guarantee out-of-domain reliability and identifying MACE-MH-1 as the most robust model for generating experimentally realistic nanoporous carbon structures.