3252 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 provides a concise yet comprehensive overview of the fundamental principles and methodologies of X-ray photoelectron spectroscopy (XPS), aiming to bridge the gap between easy data acquisition and reliable interpretation by clarifying essential concepts such as photoemission processes, chemical shifts, charge referencing, peak fitting, and quantification strategies for newcomers to the field.
This study demonstrates that K-ion intercalation in electrodeposited Prussian blue analogs drives reversible nanoscale resistive switching, establishing a low-cost, scalable, and ultrafast memristive platform suitable for neuromorphic and memory applications.
This study demonstrates that increasing the thickness of amorphous grain boundary complexions in nanocrystalline Cu-Zr suppresses plastic strain localization and shear banding, thereby promoting homogeneous deformation and enhancing damage tolerance.
This paper introduces ESU-MOF, a dataset and positive-unlabeled learning framework that fine-tunes large language models to predict the scalability potential of Metal-Organic Framework syntheses with 91.4% accuracy, thereby accelerating industrial deployment by addressing fragmented scale-up knowledge.
This paper presents a global digital image correlation framework that solves the correlation problem directly on the lattice mesh with automatic element deletion and data-driven damage detection, enabling robust, high-resolution tracking of crack initiation and propagation in architected materials where traditional continuum-based optical methods fail.
This study demonstrates that giant spontaneous Kerr rotations observed in bulk MnTe at telecommunication wavelengths arise from carrier self-doping rather than ideal altermagnetic order, as evidenced by the absence of such signals in stoichiometric thin films.
This study utilizes scanning tunneling spectroscopy and angle-resolved photoemission spectroscopy on antimony telluride homologous superlattices with two to four antimonene layers to experimentally confirm the presence of an M-point saddle point and van Hove singularity, revealing that Sb and Te orbital hybridization is the key mechanism driving this feature toward the Fermi level.
The paper introduces DielecMIND, an AI framework that combines large-language-model hypothesis generation with physics-validated first-principles calculations to successfully discover and validate five new extreme-kappa dielectric materials, thereby expanding this rare materials class by 35% and establishing a new paradigm for overcoming data scarcity in functional materials discovery.
This paper reviews data-driven and machine learning approaches, particularly descriptor-based models and interatomic potentials trained on DFT data, that accelerate point defect simulations in solid-state materials by enabling rapid, quantum-mechanically accurate predictions of properties like formation energies and vibrational free energies for high-throughput screening and experimental integration.
This paper introduces MagMatLLM, a constraint-guided generative framework that successfully identifies twelve previously unknown, dynamically stable magnetic insulators by integrating language-model-based crystal generation with evolutionary selection and first-principles validation to navigate the challenging, data-scarce regime of competing physical constraints.