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 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 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.
This review examines how recent advances in materials science, device characterization, and nanofabrication are overcoming critical challenges in Josephson junction reproducibility, dissipation, and scalability to enable the transition from laboratory components to industrial-scale superconducting quantum processors.
This paper introduces a self-heating electrochemical cell that functions as a non-volatile, programmable linear resistor with nine decades of resistance range and high precision, overcoming the non-linearity and error limitations of existing memory technologies to enable efficient in-sensor analog signal processing and in-memory computing.
This paper presents a robust U-Net-based deep learning framework combined with a geometric analysis pipeline to quantitatively characterize the evolution of magnetic labyrinthine stripe patterns in Bi:YIG films, revealing distinct structural transition modes linked to field polarity during magnetic annealing.
This study demonstrates that compressive strain stabilizes an altermagnetic phase in epitaxial RuO films by enhancing the density of states near the Fermi level, with symmetry analysis revealing ideal altermagnetism in (100) films versus an uncompensated ferrimagnetic state in (110) films.
This paper introduces C2DTD, a compact, interpretable, and physics-informed topological descriptor that effectively captures multi-scale structural features of 2D carbon networks to enable robust, data-efficient machine learning predictions and deep physical insights into their energy landscapes.
PolyJarvis is an LLM-driven autonomous agent that integrates with the RadonPy platform to execute end-to-end all-atom molecular dynamics simulations for polymers, successfully predicting key properties like density and bulk modulus with high accuracy while demonstrating that LLM agents can match expert-level performance in polymer simulation workflows.