2898 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 reports the stabilization and characterization of five-fold and seven-fold magnetic skyrmion lattice disclinations in pentagon- and heptagon-shaped FeGe nanocrystals, combining experimental imaging with micromagnetic simulations and analytical models to explore these previously unexplored angular defects.
This study employs first-principles calculations to demonstrate that hydrogen-substituted graphdiyne (HsGDY) is a thermally robust, indirect semiconductor with a 0.89 eV band gap in its energetically favorable AA stacking configuration, exhibiting strong visible-to-UV optical absorption that positions it as a promising candidate for next-generation optoelectronic and energy-harvesting applications.
This study reports the synthesis of a nonstoichiometric, ilmenite-type CaIrO polymorph via topochemical ion exchange, characterizing its quantifiable stacking faults and identifying a low-temperature magnetic anomaly associated with Ir moments.
This paper introduces M-CODE, an open-source, ontology-based categorization system that classifies materials structures by dimensionality, complexity, and evolutionary provenance to support standardized data management and reproducible dataset generation in AI-driven materials science.
This study reveals that the unique bulk-like -wave spin splitting in CuF, distinguishing it from other transition-metal difluorides, is driven by antipolar fluorine ion displacements that introduce an additional magnetic octupole component, thereby offering a pathway to control nonrelativistic spin splitting through structural modifications.
This paper demonstrates that the standard generalized Cluster-Correlation Expansion (gCCE) method fails to provide even a qualitatively accurate description of spin-spin relaxation at low temperatures, and offers a mathematical deconstruction of the theory to identify the root causes of this breakdown for future resolution.
This paper presents a scalable, CMOS-compatible non-superconducting tunnel junction technology based on TiW alloy and AlOx barriers, which overcomes the integration limitations of previous methods and demonstrates robust performance in cryogenic applications down to 20 mK.
This study demonstrates that combining density functional theory with physically informed descriptors enables accurate and chemically transferable machine learning predictions of dopant formation energies in TiO monolayers, even when trained on small, well-curated datasets.
This study demonstrates that uniaxial compressive strain effectively suppresses both the spontaneous anomalous Hall effect and the tiny net magnetization in the altermagnet candidate h-FeS by tuning the c-axis ferromagnetic moment through domain population modification, highlighting strain as a viable control mechanism for spintronic applications.
This paper introduces a novel, computationally efficient hybrid simulation method that combines atomistic and continuum modeling using optimized "quasi-atoms" and online machine learning, demonstrating superior speed and accuracy compared to full-atomic simulations and existing hybrid approaches like AtC.