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.
TriForces is a model-agnostic, three-stream framework that combines self-supervised learning with separated composition and structure representations to significantly enhance the transferability and data efficiency of atomistic graph neural networks for machine learning interatomic potentials.
This paper utilizes first-principles calculations to demonstrate that the coupled spin-optical signals in hexagonal boron nitride originate from interacting donor-acceptor pairs rather than isolated defects, revealing how their separation and charge states govern key quantum properties and offering a unified framework for designing room-temperature quantum emitters.
Using nano-ARPES, researchers demonstrate that while the momentum positioning of valence band maxima in twisted bilayer WSe2 remains fixed, the twist angle can be used to tune the energetic separation between hole bands at the K and Γ points by over 100 meV, offering a pathway to control band gaps and spin-dependent electron-phonon coupling in 2D devices.
Through an extensive study of over 7,000 ground-state structures in a 2D binary packing model, the paper reveals that while non-periodic structures dominate, approximately 35% of them exhibit "structural selectivity," a property that serves as a signature of underlying order extending well beyond the diversity limits of periodic crystals.
This paper presents an enhanced generalized phase diagram for graphene CVD growth on copper that incorporates thermal-expansion-induced strain and chemical desorption effects to predict and guide the rational synthesis of high-quality bilayer graphene by linking macroscopic growth parameters to microscopic layer-selection mechanisms.
This study utilizes a machine learning force field to reveal that SbS exhibits anisotropic crystallization driven by its quasi-1D ribbon structure, with interface-controlled growth kinetics characterized by a significantly lower activation energy than diffusion, offering key insights for optimizing its performance in data storage and photonics applications.
This study demonstrates that transferable 3D Convolutional Neural Networks, specifically the DenseNet-201 architecture, significantly outperform traditional descriptor-based models in predicting the elastic constants of nanoporous metals, achieving high accuracy () and enabling the identification of Pareto optimal designs through transfer learning and large-scale stochastic evaluation.
This paper presents a finite-displacement algorithm that integrates Hubbard corrections into electron-phonon calculations for strongly correlated materials, demonstrating that these corrections significantly alter phonon stability and electron-phonon coupling in LaNiO and RuO by modifying Fermi surface topology, thereby resolving discrepancies between theoretical predictions and experimental observations.
This paper presents a chip-scale planar cavity platform utilizing an optimized attached split ring resonator integrated with various yttrium iron garnet geometries to achieve strong magnon-photon coupling, demonstrating that geometric design rather than magnetic volume is the key parameter for tailoring interaction strength in hybrid quantum devices.
This paper establishes a universal, energy-conservation-based upper bound on the magnetoelectric coupling of bi-isotropic nanoparticles that applies equally to both reciprocal chiral and non-reciprocal Tellegen particles, regardless of their material properties, size, or illumination conditions.