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 reviews the unique thermoelectric advantages of topological semimetals, establishes optimal design principles to maximize their figure of merit ($zT$), and utilizes these principles to identify twelve promising new materials for high-efficiency thermoelectric applications.
This paper experimentally demonstrates that orbital-to-charge conversion via the inverse orbital Hall and Rashba effects dominates over spin-related mechanisms in metallic and semiconductor heterostructures, revealing significant signal enhancements in oxidized copper and distinct orbital diffusion behaviors in titanium and germanium to advance the field of orbitronics.
This study employs a hierarchical high-throughput screening workflow combining machine learning and first-principles calculations to identify over 200 alkaline-stable NASICON and garnet-type lithium-ion conductors, revealing key cation substitution strategies and design trade-offs for advancing solid-state humid Li-air batteries.
This paper benchmarks universal machine learning interatomic potentials (uMLIPs) against a domain-specific model for supported Cu/AlO nanoparticles, finding that while uMLIPs like MACE-OMAT and MatterSim-v1.0.0-1M can effectively identify stable structures and reproduce molecular dynamics trends without fine-tuning, their significantly higher computational cost remains a limiting factor for large-scale simulations.
This study demonstrates that discretized Halbach spheres constructed from permanent magnets arranged with icosahedral symmetry offer a practical and scalable solution for generating highly homogeneous magnetic fields, achieving usable volumes up to 260 times larger than traditional cylindrical arrays while maintaining deviations below 1%.
This review identifies the primary obstacles hindering the experimental realization of machine learning-predicted thermoelectric materials—specifically poor model generalizability due to small data and sampling biases, inadequate structural representation, and thermodynamic stability challenges—and advocates for advanced validation strategies and active learning loops to bridge the gap between computational predictions and experimental discovery.
Atom probe tomography reveals that 2D Ti0.87O2 deviates from its assumed stoichiometry through oxygen vacancies and retained alkali metals, a compositional complexity that drives structural reconstruction and significantly influences the material's electronic and functional properties for next-generation nanoelectronics.
This study demonstrates the on-surface synthesis of atomically precise 2D lateral heterostructures combining graphene nanoribbons and graphdiyne networks via sp-sp2 hybridization, revealing a bromine-mediated formation mechanism and showing that the resulting junction enables voltage-tunable spatial current separation for next-generation all-carbon nanoelectronics.
The paper introduces **optimade-maker**, a lightweight toolkit that automates the conversion of diverse raw atomistic datasets into interoperable, OPTIMADE-compliant REST APIs, thereby lowering technical barriers and enabling scalable, FAIR data integration across community-contributed and curated materials databases.
This paper presents a low-cost, low-power, and versatile electronic nose featuring a 1024-pixel capacitive CMOS array functionalized with diverse materials via inkjet printing to selectively detect volatile organic compounds with high humidity tolerance.