2794 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 proposes a unified framework for superconducting qubit decoherence that mathematically separates device geometry from microstructural topology into independently testable factors, introducing a falsifiable experimental protocol to enable predictive materials engineering.
This paper presents the first implementation and comprehensive dose validation of the .decimal GRID block applicator within the RayStation Treatment Planning System, establishing a robust protocol and novel standardization approach for Spatially Fractionated Radiation Therapy (GRID therapy) that achieved a 98% agreement between planned and measured doses.
This paper demonstrates that orthogonally twisting bilayer CrPS induces a robust -wave altermagnetic state with significant spin splitting and efficient spin-to-charge conversion, establishing twistronics as a versatile strategy for engineering advanced spintronic materials.
This paper summarizes the comprehensive development of Ferrosolar's upgraded metallurgical grade (UMG) silicon technology, demonstrating that through optimized purification, doping, and defect engineering, UMG-Si can produce high-efficiency, reliable, and environmentally superior solar cells and modules that are competitive with conventional polysilicon.
This study demonstrates that while Al substitution in SrFeAlO hexaferrites weakens magnetic exchange interactions and lowers the Curie temperature, it simultaneously stabilizes single-domain behavior to achieve exceptionally large coercive fields of up to 1.2 T.
This paper demonstrates that combining reflection and transmission synchrotron radiation x-ray topography enables the comprehensive determination of Burgers vectors for individual threading dislocations, including edge, mixed, and screw types, in acidic ammonothermal-grown GaN substrates.
This paper introduces the Element-Weighted Kolmogorov-Arnold Network (KAN) as a state-of-the-art, interpretable machine learning framework that accurately predicts crystal energy landscape properties while uncovering intrinsic chemical trends aligned with periodic and quantum mechanical principles without explicit physical constraints.
This paper demonstrates through atomistic simulations and linear stability analysis that irrational twin boundaries in martensitic materials initiate motion at significantly lower shear stresses than rational boundaries via a nonlocal instability mechanism involving orthogonal microtwin formation, a phenomenon that local measures fail to capture.
This study demonstrates a circular-economy approach by converting discarded thermoelectric waste into high-performance hydrogen evolution reaction (HER) catalysts, where a melting-cast route yielding a BiSbTe3/ZnTe heterostructure outperforms ball-milled counterparts through enhanced charge transfer and catalytic activity.
This paper introduces a parameter-free, two-channel Allen-Dynes framework that unifies phonon and spin-fluctuation mechanisms to achieve highly accurate blind predictions of superconducting critical temperatures across five orders of magnitude, while simultaneously establishing a quantum-metric no-go result that limits the universality of geometric superfluid weight as a predictor.