4007 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.
The study demonstrates that introducing Janus-induced mirror-symmetry breaking in MoSSe/MoS2 bilayers promotes atomic reconstruction into distorted aperiodic patterns, which significantly weakens interlayer coupling and amplifies the twist-angle dependence of thermal conductance by nearly an order of magnitude compared to conventional twisted bilayer MoS2.
This study employs density functional theory and machine-learning interatomic potentials to investigate the thermoelectric properties of LiCdSb, revealing a low lattice thermal conductivity and a figure of merit (ZT) exceeding 1 at temperatures above 600 K, which positions it as a promising candidate for high-temperature energy conversion.
This first-principles study demonstrates that while boron doping destabilizes the -FeO lattice, co-doping with yttrium restores structural stability and synergistically enhances the material's low-energy absorption and optical response, offering a viable strategy for tailoring hematite for advanced photoactive and optoelectronic applications.
This paper demonstrates that defining a micromechanical response function to local force monopoles in a mean-field glass model reveals exact relations between local stiffness and global susceptibilities, thereby bridging the model's non-equilibrium Replica-Symmetry-Breaking phase with finite-dimensional glasses and linking its soft mode statistics to the boson peak.
This paper presents a generalized, temperature-dependent dispersion framework that simultaneously models intrinsic and Zn-doped GaAs infrared optical properties, successfully describing phonon-plasmon coupling and Fano-type asymmetries through the simultaneous fitting of reflectance, transmittance, and ellipsometric data.
This paper introduces the concept of "response-selected hyperuniformity" in hydrodynamic active matter, demonstrating that while locally neutral clusters can screen active forces to produce hidden, long-range ordered flow, rare unscreened moments create a finite screening length that limits this quiet-flow regime.
This study reports the successful synthesis of chemically encapsulated, single-crystal TaSiAs nanowires with a square-net lattice, which exhibit enhanced room-temperature conductivity and non-saturating linear magnetoresistance due to topologically protected surface states, as confirmed by experimental transport measurements and first-principles calculations.
The paper introduces Catalyst Diffusion Transformer (CatDiT), a unified generative framework that efficiently designs novel and valid heterogeneous catalysts by simultaneously conditioning on adsorbate type, binding energy, and catalyst class, successfully demonstrating its capability to enrich candidate pools for targeted reactions like nitrogen reduction.
This paper introduces a graph neural network approach that successfully aligns heterogeneous DFT datasets by predicting cross-functional energy residuals, thereby upgrading legacy PBE calculations to high-precision r2SCAN accuracy and enabling the integration of multi-source data for robust materials AI models.
This paper presents a data-efficient neural-vision system that automates crystallographic alignment during CdTe molecular beam epitaxy growth by training on only 15 RHEED patterns and leveraging physics-aware postprocessing to replace manual inspection.