4044 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 experimentally maps photoexcited small polaron properties in transition metal oxides onto phase diagrams of ground-state models, identifying the t-J-Holstein model as the most accurate descriptor and establishing a framework to guide the design and control of excited-state polarons through electron-phonon coupling, localization, and spin exchange.
This study demonstrates that silver iodide (AgI) thin films exhibit distinct, phase-dependent nonlinear optical anisotropies, with zincblende and wurtzite phases showing different second harmonic generation symmetries and effective susceptibilities, thereby establishing AgI as a versatile platform for investigating phase-tuned nonlinearity in wide-gap halides.
This paper presents a systematic theoretical analysis of spin-based terahertz emission from magnetic heterostructures using a superdiffusive spin-transport model to establish optimization guidelines regarding layer thickness, interface properties, and excitation protocols, ultimately revealing trade-offs between bandwidth and efficiency while proposing a double-pulse trilayer protocol for enhanced broadband emission.
Using large-scale molecular dynamics simulations, this study reveals that precursor grain structure governs the graphite-to-diamond transformation by decoupling nucleation from propagation, where grain boundaries facilitate local nucleation but arrest growth at mismatched interfaces, thereby stabilizing kinetically trapped mixed sp²-sp³ states and establishing structural heterogeneity as a critical control parameter alongside pressure and temperature.
This study utilizes spectroscopic ellipsometry to demonstrate that co-vapor deposited NPD and TPD organic semiconductor glasses form high-density, thermally stable materials whose properties are governed by substrate temperature and surface equilibration mechanisms, with phase transformations initiating at the free surface and propagating into the bulk.
This study demonstrates that co-deposited organic semiconductor glass mixtures consistently exhibit high kinetic stability (ultrastability) and predictable anisotropic molecular packing, regardless of large differences in component glass transition temperatures, offering key insights for designing more durable and efficient organic electronic devices.
The paper introduces AGAPI-XRD, a hybrid framework that integrates generative AI, database pattern matching, and automated Rietveld refinement to enable end-to-end, accessible crystal structure determination from raw powder X-ray diffraction data with high accuracy across diverse mineral benchmarks.
This study demonstrates that scalable (100)-oriented -GaO wafers with large grown-in offcuts, produced via Edge-defined Film-fed Growth, enable fast molecular beam epitaxy growth rates and high-quality epilayers that yield Schottky barrier diodes with superior breakdown fields and low unintentional doping, thereby overcoming the historical growth rate limitations of this crystal orientation.
This paper numerically investigates how randomly introduced vacancies in a one-dimensional artificial Dirac comb potential affect the ground-state energy and boson distribution of a weakly interacting Bose gas, revealing an exponential energy decrease with increasing vacancy count and the emergence of localization features around defects.
This paper introduces the Hypothesis Evolution Protocol (HEP), a framework that equips large language model agents with explicit, auditable operations for hypothesis generation, testing, and belief revision, thereby enabling transparent and verifiable AI-driven scientific discovery.