2732 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 study utilizes transient grating spectroscopy to characterize a 3 m epitaxial NiTi film, revealing a 450% change in thermal diffusivity and a crossover in shear moduli during its R-phase transformation, which highlights the material's potential for thermal switch applications due to its anomalous heat capacity and lack of hysteresis.
This study demonstrates that the absolute stability limit for microsegregation-free solidification can be achieved in concentrated hypoeutectic Al-Ag alloys at growth rates relevant to additive manufacturing, a finding validated through Dynamic Transmission Electron Microscopy experiments and quantitative agreement with phase-field simulations and linear stability analysis.
This study employs density functional theory to demonstrate that dynamically stable 2D homo bilayer NbOX2 (X=Cl, Br, I) materials exhibit tunable band gaps, high anisotropic carrier mobility, and strong visible-to-UV light absorption, making them promising candidates for efficient photocatalytic water splitting.
This study combines mesoscale dislocation mechanics modeling and experiments to demonstrate that geometrically necessary dislocation (GND) hardening competes with thermal softening to produce finite-width adiabatic shear bands and dislocation patterning in polycrystalline aggregates, capturing size-dependent strengthening and large-deformation evolution without catastrophic softening.
This paper proposes a structured framework for governing AI-assisted scientific software development within strict quality assurance standards like NQA-1, using the TMAP8 fusion energy code to demonstrate how transparent, traceable, and auditable verification and validation processes can ensure human accountability and software reliability.
This paper theoretically predicts that ferroelectric monolayer -phase MoS acts as a higher-order topological insulator with quantized corner states and demonstrates that its orbital Hall conductivity can be reversibly modulated by the direction of ferroelectric polarization, offering a promising platform for electric-field-controlled orbitronics.
This study reveals that the thermal transport properties of the honeycomb antiferromagnet MnPS, particularly the sign reversals in thermal Hall conductivity and multiple valleys in longitudinal thermal conductivity below 2 K, are caused by the redistribution of Berry curvature in magnon bands, highlighting the effectiveness of thermal Hall measurements for detecting topological features in magnetic insulators.
This paper proposes a computationally efficient indicator, defined as the ratio of thermal conductivity calculated via the full linearized Peierls-Boltzmann equation to that from the relaxation time approximation, to identify and accelerate the discovery of materials exhibiting phonon hydrodynamics, while also highlighting the necessity of rigorous Brillouin zone sampling convergence for accurate predictions.
This paper presents the Multi-instrument Autonomous Discovery (MAD) framework, which integrates heterogeneous data from X-ray diffraction and electrical resistance measurements via a co-regionalization kernel to simultaneously map crystal structures and optimize resistance in Mn-Sb-Te phase-change memory materials, achieving a seven-fold speed-up in discovering novel compositions within 25 closed-loop iterations.
This study employs a high-throughput combinatorial approach combined with unsupervised machine learning to map the structural and magnetic properties of Fe-Ge-Te thin film libraries, revealing that the hexagonal crystal structure is a critical prerequisite for ferromagnetism and enabling the efficient discovery of novel room-temperature magnetic materials.