3229 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 review comprehensively examines fluorescence nanothermometry by detailing its fundamental mechanisms, material platforms, and recent advances while critically evaluating current challenges and outlining future strategies to enable robust, real-time temperature measurements at the nanoscale.
By combining neutron and X-ray diffraction with large-scale molecular dynamics simulations, this study demonstrates that while explicit long-range interactions improve liquid structure predictions, they remain insufficient for accurately modeling the medium-range order of silica glass, as both short-range and long-range machine learning potentials fail to capture the necessary network flexibility and ring statistics during the liquid-to-glass transition.
This paper presents a physics-based model validated by experimental data on short-channel IGZO thin-film transistors to characterize electron velocity-field behavior, accounting for trapping effects, scattering mechanisms, and thermal phenomena to achieve carrier velocities exceeding 4×10⁶ cm/s in the band, thereby providing a crucial framework for designing next-generation nanoscale oxide electronics.
Using in-operando positron annihilation spectroscopy on copper, this study demonstrates that electrically driven solids exhibit a reversible, non-equilibrium vacancy population driven by current-induced Frenkel-pair production rather than Joule heating alone, providing a defect-mediated mechanism for flash state phenomena.
This study reports the observation of a room-temperature third-order nonlinear anomalous Hall effect in the ferromagnetic metal Fe3GaTe2, which persists up to its Curie temperature (~350 K) and is attributed to the Berry curvature quadrupole, offering new avenues for nonlinear electronic devices.
This study reports the experimental observation of higher odd-order (third-, fifth-, and seventh-order) nonlinear Hall effects in magnetic topological insulator Mn(Bi1-xSbx)2Te4 thin flakes, attributing the phenomenon to Berry curvature multipoles and demonstrating its dependence on the Néel temperature and charge neutral point.
This paper presents a systematic analytic inverse-design framework for temporal metamaterials that leverages space-time duality to directly synthesize closed-form refractive-index modulations for tailored wave responses, such as mathematical operators and specific filters, without requiring iterative optimization.
This paper introduces a symmetry-based framework called "order-(dis)order family trees" to evaluate the true novelty of predicted crystal structures by identifying their relationships to known disordered parent phases, revealing that many seemingly novel ordered compounds are actually derived from existing disordered materials and highlighting the importance of this distinction for improving data-driven materials discovery.
The paper introduces FINDSPINGROUP, an online computational platform that unifies spin space group and magnetic space group frameworks to automate symmetry analysis, classify magnetic phases, and facilitate the high-throughput discovery of unconventional magnets for next-generation spintronics.
GEWUM is a unified, open-source platform that automates materials discovery by integrating Selective Random Structure Search with universal Machine Learning Interatomic Potentials to enable efficient, scalable structure generation, selection, and validation on HPC clusters.