4239 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 introduces an efficient, first-principles framework that computes photoelectron states as Kohn-Sham scattering solutions to enable accurate, transparent, and widely compatible simulations of angle-resolved photoemission spectroscopy (ARPES), as demonstrated by excellent agreement with experimental data for graphene and WSe.
This study proposes a new computational random graph algorithm that unifies low-temperature bosonic and high-temperature crystalline paradigms to analytically model solid amorphous inorganic structures, successfully explaining the boson peak phenomenon and validating its results against experimental neutronography data without requiring melting simulations.
This paper introduces SCALAR, a benchmark designed to evaluate how materials foundation models handle geometric scale generalization and structural reasoning across diverse nanoparticle structures, revealing that while explicit physics-grounded reasoning can reduce hallucinations and errors, it often compromises output consistency and validity.
This study employs density functional theory to elucidate how hydrogen uptake in brownmillerite perovskites induces localized electronic and magnetic changes, establishing design rules based on B-site d-electron counts and lattice flexibility while highlighting the need for careful computational treatment and machine-learning benchmarks to guide the development of hydrogen-responsive iono-electronic devices.
This study employs a correlative scanning transmission electron microscopy workflow to simultaneously map lattice structure, crystallographic orientation, and chemical composition at 10-nanometer resolution, thereby quantifying the nanoscale evolution of phase fractions, lattice parameters, and microstructural textures in deformed medium-Mn TRIP steel.
This study demonstrates the successful synthesis of a stable monolayer ice phase on a hydrophobic Au(111) surface using a low-energy-electron-assisted growth method, challenging the conventional view that such ordered structures cannot form on inert substrates.
This study reports the discovery of anomalous long-distance spin transport (up to 480 μm) in the frustrated hyperkagome magnet Gd3Ga5O12, driven by significant spin fluctuations and a correlated "director" state rather than conventional magnons, thereby highlighting the potential of frustrated magnets as superior channel materials for spintronics.
This study utilizes cryogenic atom probe tomography to achieve near-atomic resolution imaging of liquid-solid interfaces, revealing the formation mechanisms and complex distribution of iodine-containing species on nanoporous gold surfaces to advance the understanding of nanoscale chemical functionalization.
By combining scanning tunneling microscopy, first-principles calculations, and global symmetry analysis on bismuth crystals grown on superconducting V3Si, this study provides direct evidence of higher-order topological bulk-boundary correspondence through the observation of spin-helical hinge state loops and proximity-induced superconductivity, establishing bismuth as a promising platform for realizing topological superconductivity and Majorana quasiparticles.
The paper introduces GPR_calculator, a Python and C++ package that accelerates massive Nudged Elastic Band simulations by employing an on-the-fly Gaussian Process Regression surrogate model to predict energies and forces, thereby reducing computational costs by 3 to 10 times compared to pure ab initio calculations.