4213 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 presents the first theoretical and experimental realization of two-dimensional Floquet non-Abelian band topology in photonic scattering networks, demonstrating unique multi-gap topological phenomena such as anomalous phases, Euler transfers, and non-Abelian braiding of band nodes.
This paper presents a versatile, low-density polyethylene-based transfer method that enables the deterministic, large-area stamping of high-quality 2D materials and heterostructures onto both flat and arbitrarily patterned substrates, facilitating the scalable fabrication of tunable optoelectronic devices.
By combining laser-driven shock compression experiments with machine-learned molecular dynamics simulations, this study reveals that titanium begins melting at 86 GPa, exhibits significant grain refinement during solid-liquid coexistence between 110–126 GPa, and retains highly textured crystalline remnants up to 180 GPa, highlighting challenges in precisely determining melt completion pressures with current experimental platforms.
This study demonstrates that controllably introducing disorder via iron cluster deposition in monolayer Fe(Te,Se) drives a superconductor-insulator transition, revealing a disorder-induced quantum phase transition characterized by the evolution from superconducting to insulating U-shaped gaps attributed to localization-enhanced Cooper pair correlations.
This study demonstrates the industrially scalable synthesis and characterization of high-density UB-UBC composites, revealing their superior uranium loading and promising oxidation resistance as advanced accident-tolerant nuclear fuel candidates.
This paper presents a systematic study of few-layer -MoTe that correlates superconducting properties with material parameters and band structure, revealing that highly hole-doped bilayer samples exhibit conventional phonon-mediated -wave pairing.
This paper introduces and experimentally validates the concept of "domain-direct band gaps" through first-principles calculations on twisted diamond, revealing a material where extended 2D manifolds of the conduction-band minimum and valence-band maximum create a direct gap with unique anisotropic carrier dynamics and enhanced optical absorption.
This Perspective argues that metal-organic frameworks (MOFs) offer a uniquely tunable chemical platform for engineering the specific lattice symmetries required to realize and control altermagnetism, thereby advancing the development of spintronic materials beyond traditional inorganic crystals.
This study presents an inverse-design framework combining genetic algorithms with frequency-domain micromagnetics to successfully discover unconventional two-dimensional magnonic crystal structures featuring large band gaps, thereby addressing the challenges of optimizing complex lattice geometries for targeted spin-wave properties.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.