4006 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 a non-invasive, all-optical gigahertz ultrasonic technique that reveals unexpectedly long-range interfacial confinement effects, such as acoustic stiffening and solid-like behavior, in liquids like glycerol, 8CB, and ionic liquids when confined within nanometer-scale gaps.
This paper demonstrates the fabrication of high-quality, single-crystalline Gallium phosphide-on-insulator substrates via ion slicing and bonding techniques, preserving the material's superior linear and nonlinear optical properties for advanced nanophotonic and quantum applications.
This study demonstrates that low-power continuous-wave laser illumination can optically switch and deterministically control the magnetic order in few-layer CrSBr via magneto-excitons, offering a promising pathway for applications in magneto-optoelectronic devices.
This paper introduces GRADAR, an orientation-map-driven algorithm that solves the inverse problem of identifying and ranking specific grains in a polycrystal capable of achieving a targeted two-beam diffraction condition for Electron Channeling Contrast Imaging (ECCI), thereby enabling automated, high-precision dislocation imaging without prior grain selection.
This paper establishes a correspondence between generalized Bell inequalities and frustrated spin systems, demonstrating that quantum violations of these inequalities are equivalent to the existence of classical coplanar ground states with lower energy than Ising ground states, a framework used to generate new inequalities including some that are not violated by the singlet state.
This paper presents a framework that integrates physically consistent, microstructure-dependent neural network material models with density-based topology optimization to enable the efficient design of multiscale heterogeneous hyperelastic structures under finite deformations.
SynCoTrain is a semi-supervised machine learning framework that utilizes a co-training strategy with dual graph neural networks (SchNet and ALIGNN) and Positive-Unlabeled learning to overcome data scarcity and accurately predict the synthesizability of oxide crystals, thereby advancing high-throughput materials discovery.
This study integrates experimental measurements with quantum-chemical and computational modeling to demonstrate that bond heterogeneity and strong anharmonicity drive the exceptionally low lattice thermal conductivity in Ag8TS6 (T=Si, Ge, Sn) argyrodites, while confirming that their thermal and ionic conductivities can be tuned independently.
This paper introduces Transport Novelty Distance (TNovD), a novel distributional metric based on Optimal Transport and contrastive learning that jointly evaluates the quality and novelty of generated materials to overcome the limitations of existing evaluation approaches in materials discovery.
This study demonstrates that topologically constrained disorder in Shakti artificial spin ice enables robust, sequence-dependent memory effects absent in conventional square spin ice, revealing how correlated disorder and cyclic driving can generate deterministic and stochastic memory in frustrated artificial materials.