4962 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 incorporating high entropy alloys into bicontinuous nanoporous structures overcomes inherent macroscopic brittleness through strain hardening mechanisms like dislocation starvation and sluggish motion, resulting in materials with specific strengths 5 to 10 times higher than single-element counterparts and enhanced thermal resilience.
The paper introduces Diffusion-based Crystal Omni (DAO), a pretrain-finetune framework utilizing Siamese foundation models that significantly outperforms conventional methods in predicting crystal structures, achieving high accuracy on real-world superconductors while operating over 2,000 times faster than DFT-based approaches.
This study introduces a Fourier Neural Operator (FNO) based surrogate model that achieves resolution-invariant, rapid, and accurate prediction of multi-grain microstructural evolution, overcoming the computational limitations of traditional phase-field simulations and the generalization issues of existing machine learning approaches.
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.