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 transition-metal-oxygen covalency, rather than the mere absence of oxygen-hole character, is the critical factor governing ligand-hole stability and reversible high-valent redox in lithium-rich cathodes, offering a pathway to stabilize high-energy-density materials without triggering irreversible oxygen release.
This study employs a high-throughput computational workflow to screen over 20,000 materials and identify 11 promising intercalation electrodes for selective ion recovery and one for desalination, offering a thermodynamically validated list of candidates to advance energy-efficient water treatment technologies.
This paper introduces AI-Donkey, a hybrid agentic framework that combines expert-curated knowledge with deterministic execution to overcome the unreliability of current large language models in generating high-fidelity, consistent many-body perturbation theory workflows for computational materials science.
This paper develops a kinetic theory for ultra-subsonic fermion systems, demonstrating that restricted phonon scattering leads to unique low-temperature transport behaviors—including linear-in-T resistivity and perfect conductivity—which offers a new explanation for the "strange metal" phenomena observed in flat band magic angle twisted bilayer graphene.
This paper calculates the acoustic phonon contribution to the electrical resistivity of twisted bilayer graphene using the full Bistritzer-MacDonald band structure, revealing that complex band geometry drives intricate doping and temperature dependencies that explain experimental resistance peaks and provide a quantitative theory to address debates regarding the existence of a strange metal state.
This paper theoretically investigates how the anisotropic band geometry of Bernal bilayer and rhombohedral trilayer graphene, including features like Van Hove singularities and Lifshitz transitions, significantly influences phonon-limited electrical resistivity by inducing nonlinear temperature dependencies and complicating standard transport crossovers.
The paper introduces P-DivGNN, a physics-informed graph neural network framework that efficiently reconstructs local micro-scale stress fields under finite strain hyperelasticity by embedding physical equilibrium constraints and periodic boundary conditions into a graph-based message-passing architecture, offering significant computational speed-ups over traditional finite element simulations for applications like fracture and fatigue analysis.
This paper proposes a practical framework for establishing a shared, standardized database for Angle-Resolved Photoemission Spectroscopy (ARPES) data to overcome current publication-centric limitations, thereby enabling immediate improvements in reproducibility and laying the essential groundwork for future AI-driven discovery in condensed matter physics.
This paper introduces PXRD-Bench, a unified benchmark for evaluating crystal structure determination from powder X-ray diffraction under challenging conditions like unknown compositions and mixtures, alongside Bragglite, a new model that significantly outperforms existing baselines while revealing current limitations in generalization and multi-phase recovery.
This study validates a high-throughput screening workflow for Heusler thermoelectrics that combines machine-learned density of states with first-principles transport calculations to efficiently identify 80 promising computational candidates, including 61 unreported materials, while demonstrating that although neural network predictions often miss band gaps, they remain effective for prioritizing compounds for detailed analysis.