2898 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 demonstrates that substituting cerium with mischmetal in Al-(Ce, La, Nd) alloys yields consistent microstructural and mechanical properties, including excellent high-temperature hardness retention and creep resistance, while offering a sustainable alternative with reduced energy consumption and CO2 emissions.
This paper presents a computationally efficient single-particle method with core holes and semiempirical corrections that accurately predicts L- and M-edge X-ray absorption and electron energy-loss spectra for molecules and solids, offering performance comparable to or better than slower TDDFT calculations while highlighting the limitations of single-particle approximations in capturing multiplet effects.
This study reveals that extremely dilute magnetic disorder induces gapless superconductivity in 2H-NbSe2-xSx, a phenomenon driven by Se-S substitution-induced band structure modifications that alter in-gap scattering mechanisms and challenge the conventional understanding of disorder robustness in superconductors.
This study addresses the reproducibility challenges in perovskite solar cell manufacturing by developing an integrated fabrication platform and an interpretable machine learning framework to systematically map and quantify the complex, nonlinear interactions between ambient humidity, solvent partial pressure, and temperature on crystallization kinetics and device performance.
Using Density Functional Theory, this study reveals that interacting phosphorus and boron/aluminum -doped layers in silicon mimic intrinsic silicon at separations under 1 nm due to potential cancellation, while behaving as independent p-n junctions at larger distances with enhanced tunneling probabilities.
This paper presents an active-learning framework that combines density functional theory calculations, machine learning, and limited experimental data to identify effective metal capping layers, specifically Zr, Ta, and Sc, for inhibiting surface oxide formation and mitigating two-level system defects in niobium-based superconducting qubits.
This paper presents a novel, ligand-free femtosecond laser ablation method performed in ambient air to synthesize high-purity, tunable organic-inorganic and lead-free inorganic perovskite nanocrystals, offering a scalable alternative to conventional chemical synthesis for next-generation optoelectronic applications.
By combining advanced photoemission and absorption spectroscopy with cluster model calculations, this study quantifies the electronic parameters of NiTe to reveal that a negative charge-transfer energy and finite on-site Coulomb repulsion drive its classification as a moderately correlated type-II Dirac semimetal, distinguishing it from strongly correlated insulators like NiO.
This study resolves the absence of expected correlation-induced band narrowing in the topological metal CoTe by demonstrating that a negative charge-transfer energy and reduced on-site Coulomb interaction, validated through spectroscopic measurements and cluster model simulations, fundamentally shape its unique electronic structure and enable its topological behavior.
This study combines density functional theory and machine learning potentials to investigate hydrogen diffusion in TiCr Laves phases, revealing that migration barriers depend on bond-breaking requirements, diffusion exhibits non-monotonic concentration dependence, and experimental discrepancies are likely caused by defect-induced hydrogen trapping.