4172 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 hafnia-zirconia superlattices with 87.5% ZrO content achieve record-breaking remnant polarization and endurance while promoting sustainability through the substitution of hafnium with abundant zirconium.
This paper presents a unified statistical theory of heat conduction in nonuniform media by deriving a causal spatiotemporal kernel via the Zwanzig projection-operator formalism, which microscopically encodes memory, nonlocality, and heterogeneity to seamlessly bridge classical diffusion, hydrodynamic, and quasi-ballistic transport regimes while recovering conventional coefficients as coarse-grained limits.
This study establishes a precise stoichiometry-structure-magnetism correlation in , revealing that a well-ordered Fe superlattice at maximizes antiferromagnetic coupling () while deviations from this optimal ordering induce a complex sequence of magnetic phase transitions from paramagnetism to spin-glass and back.
This study utilizes X-ray photoelectron spectroscopy to non-destructively evaluate 17 metal capping layers for their ability to prevent niobium oxidation and surface contamination during fabrication processes, ultimately identifying resilient candidates that improve the performance of superconducting resonators.
This study presents a comprehensive, data-driven framework that integrates machine learning, a novel SISSO-derived tolerance factor, and sustainability metrics to screen and rank stable, experimentally feasible chalcogenide perovskites for next-generation solar energy applications.
This review examines the application of generative AI to the inverse design of inorganic compounds, analyzing how data-representation-model pipelines address unique challenges like symmetry and electronic structure across diverse systems while outlining future directions for benchmarking and synthesizability.
This paper introduces A-Lab GPSS, an agentic AI-driven robotic platform capable of autonomously synthesizing and characterizing air-sensitive lithium halide spinel conductors under strict air-free conditions, successfully optimizing discovery strategies through abductive and inductive reasoning to significantly increase the yield of high-purity, conductive materials.
This paper presents an end-to-end autonomous LLM agent capable of executing a "mini research loop" on computational physics literature, demonstrating its ability to autonomously identify substantive flaws in 42% of 111 papers and independently generate a publishable critique that revises the conclusions of a specific Nature Communications study.
This paper demonstrates that trilayer MoSe2 devices with a multi-gate architecture can be electrically reconfigured to transition from single-dot to double-dot transport regimes by tuning the backgate voltage, enabling the study of non-equivalent quantum dots with tunable coupling.
This study utilizes square-pulsed source thermometry combined with microstructural analysis to reveal that the thermal conductivity of HFCVD diamond films on SiC substrates increases significantly from ~60 to ~200 W m⁻¹ K⁻¹ from the nucleation interface to the surface, directly correlating with grain coarsening and offering critical insights for optimizing thermal management in high-power electronics.