4173 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 Electrospinning-Data.org, a FAIR-aligned, structured knowledge resource that aggregates diverse and failure-inclusive electrospinning experimental data into a machine-readable format to overcome reporting inconsistencies and enable data-driven research, predictive modeling, and inverse design in nanofiber fabrication.
This study demonstrates that short-range atomic ordering in GeSn semiconductor alloys can be precisely quantified using a machine learning-enabled EXAFS analysis and subsequently tuned via annealing to significantly modify the material's bandgap, establishing local atomic order as a critical new degree of freedom for band engineering alongside composition and strain.
This paper re-derives and extends the 1964 Borisov model linking grain boundary free energy to diffusion coefficients by clarifying its underlying assumptions, correcting inconsistencies, and generalizing the framework to include impurity diffusion and various atomic mechanisms.
The paper introduces Mat3ra-2D, an open-source framework that enables the rapid, reproducible design of realistic two-dimensional materials and interfaces with defects and disorder to address the limitations of AI/ML models trained solely on ideal bulk crystals.
This study reports the successful realization of a single-phase two-dimensional van der Waals multiferroic, CuIn0.2V0.8P2S6, which exhibits intrinsic room-temperature ferroelectricity and ferromagnetic ordering below 14.6 K, demonstrating a promising route for next-generation multifunctional devices.
This study combines circularly polarized Raman, Raman optical activity (ROA), and terahertz circular dichroism (TCD) spectroscopy with density functional theory calculations to identify and characterize distinct low-frequency chiral phonon modes in several amino acid crystals, revealing their sensitivity to molecular chirality and local geometry.
This paper presents a dynamical diffraction formalism based on the Takagi-Taupin equations that enables Dark-Field X-ray Microscopy to overcome the frequency resolution limits of traditional Bragg-peak tracking, allowing for quantitative, depth-resolved imaging of coherent phonon decay and interactions in bulk materials through time-dependent intensity oscillation sidebands.
This paper identifies and experimentally validates a third mechanism for binding Dirac fields—phase discontinuities or branch cuts in complex mass fields—which support unique guided "Dirac branch-cut modes" characterized by energy-independent transverse confinement and robust propagation along freeform paths in acoustic metamaterials.
This study quantifies magnetic interactions in FeGeTe/graphite/FeGeTe van der Waals heterostructures using cross-sectional Lorentz transmission electron microscopy and electron holography, revealing a dipolar coupling length scale of 34 nm, surface-induced moment canting up to 100 nm, and narrow domain walls that can be modeled without Dzyaloshinskii-Moriya interaction.
This paper presents an efficient, open-source framework that utilizes an optimized forward solution method to generate high-fidelity synthetic 3D surface topographies and roughness data for milling operations, achieving a 42.2x computational speedup while maintaining accuracy to support data-driven manufacturing research.