3501 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 employs tailored low-dose electron microscopy to reveal the atomic structures of grain boundaries, edge dislocations, and associated strain fields in templated FA0.9Cs0.1PbI3-xClx perovskite films, providing critical insights into how specific defects influence charge transport and device performance.
This study demonstrates the successful epitaxial growth of high-quality, earth-abundant MgSnN2 on 4H-SiC substrates via magnetron sputtering, revealing its tunable bandgap, high visible-light absorption, and green-emission capabilities as a promising, cost-effective alternative for sustainable photovoltaics and optoelectronics.
This study demonstrates that visible-light-induced photostriction in a PMN-PZT/FeGa/IrMn multiferroic heterostructure enables significant, room-temperature modulation of exchange bias and magnetization switching, offering a promising pathway for low-power, multistate, and wireless opto-magnetic memory applications.
This study employs first-principles calculations to demonstrate how specific optical phonon modes in Yttrium iron garnet (Y3Fe5O12) tune magnetic exchange interactions by altering the Fe-O-Fe bond geometry and superexchange pathways.
This study demonstrates through first-principles calculations that Zn substitution in EuMnSb induces a transition from a C-type antiferromagnetic semiconductor to an intrinsic magnetic Weyl semimetal by stabilizing ferromagnetism and breaking both time-reversal and inversion symmetries, thereby generating topologically protected Weyl nodes and Fermi-arc surface states.
This paper introduces the Element-Weighted Kolmogorov-Arnold Network (KAN) as a state-of-the-art, interpretable machine learning framework that accurately predicts crystal energy landscape properties while uncovering intrinsic chemical trends aligned with periodic and quantum mechanical principles without explicit physical constraints.
This paper demonstrates through atomistic simulations and linear stability analysis that irrational twin boundaries in martensitic materials initiate motion at significantly lower shear stresses than rational boundaries via a nonlocal instability mechanism involving orthogonal microtwin formation, a phenomenon that local measures fail to capture.
This paper demonstrates that the high dimensionality of spectral data, analyzed through the Feldman-Hajek theorem and concentration of measure, allows machine learning models to achieve perfect separation based on trivial artifacts like noise or normalization rather than meaningful chemical features, thereby explaining why such models often succeed without chemical validity and misleadingly highlight irrelevant spectral regions.
This study demonstrates a circular-economy approach by converting discarded thermoelectric waste into high-performance hydrogen evolution reaction (HER) catalysts, where a melting-cast route yielding a BiSbTe3/ZnTe heterostructure outperforms ball-milled counterparts through enhanced charge transfer and catalytic activity.
This paper introduces a parameter-free, two-channel Allen-Dynes framework that unifies phonon and spin-fluctuation mechanisms to achieve highly accurate blind predictions of superconducting critical temperatures across five orders of magnitude, while simultaneously establishing a quantum-metric no-go result that limits the universality of geometric superfluid weight as a predictor.