3525 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 introduces and validates a normalized descriptor, , which expresses second-order nonlinear optical responses relative to a band gap-dependent physical limit, thereby enabling unbiased, universal screening and machine learning-driven discovery of nonlinear optical materials across diverse band gap energies.
This study characterizes high-quality single-crystalline BiPt as a conventional, time-reversal-symmetric s-wave superconductor with a transition temperature of 1.2 K that operates in the dirty limit and exhibits pronounced anisotropy due to its hexagonal structure, serving as a crucial topologically trivial reference for Bi-based non-trivial superconductors.
This study establishes micro-Raman spectroscopy of optical moiré phonons as a rapid, non-invasive method to precisely determine local twist angles in twisted WSe₂ bilayers with sub-micrometer resolution and better than ±0.3° accuracy, revealing significant spatial variations that impact electronic properties.
This study demonstrates that controlling substrate temperature during Joule-effect thermal evaporation enables the synthesis of uniform, ordered gallium nanoparticle arrays with optimized localized surface plasmon resonance properties by balancing nucleation and growth dynamics while mitigating coarsening mechanisms like Ostwald ripening.
This review synthesizes recent advances in Klein and anti-Klein tunneling across various low-dimensional materials and superlattices by establishing a general tight-binding framework based on pseudospin conservation, while highlighting the universality of these phenomena across diverse physical platforms ranging from electronic systems to artificial metamaterials.
Using first-principles calculations, this study establishes pressure as a robust criterion for mapping the family relationships among hafnia polymorphs, revealing previously unreported ancestral structures that connect the monoclinic ground state and ferroelectric phase to their parent high-symmetry states.
This paper proposes "Dicke materials," a class of magnetic systems exhibiting a superradiant phase transition, as a robust resource for quantum squeezing that remains stable against finite temperature, disorder, and local interactions, thereby offering a promising platform for quantum metrology and entanglement detection in solid-state systems.
This study demonstrates that electrochemical desodiation and thermal changes drive continuous, second-order phase transitions in P2-NaxNi1/3Mn2/3O2, where Na+-vacancy ordering is intrinsically coupled to orthorhombic-to-hexagonal structural distortions, fundamentally influencing sodium chemical diffusivity.
This paper provides a formal group-theoretical proof that structural chirality cannot serve as a primary ferroic order parameter because transitions to enantiomorphic space groups cannot be driven by zone-center instabilities from a common achiral parent.
This paper presents an AI-supported degradation study of carbon-based perovskite solar cells that utilizes a drift-diffusion guided autoencoder to extract physical parameters from scan-speed-dependent J-V curves, enabling in situ tracking of device aging and the creation of a digital twin to bridge the gap between classical interpretation and machine learning predictions.