3396 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 theoretically investigates how magnetic dipolar interactions mediate coupling between magnetostatic and Lamb waves in ferromagnetic thin films, predicting the emergence of hybridization gaps in their dispersion relations.
This paper experimentally investigates elastic wave dispersion and cut-off frequencies in an octet truss lattice, attributing these phenomena to rib resonance and modeling the material's behavior using micromorphic continuum mechanics.
This study demonstrates that Ni-doping of CdMnS thin films prepared via chemical bath deposition enhances crystallinity, increases grain size, and reduces the optical band gap, making them promising photoconducting window layer materials for optoelectronic applications.
This paper introduces a translationally equivariant and higher-order finite-difference scheme to improve the accuracy and symmetry preservation of momentum-space derivatives used in Wannier interpolation for calculating quantum geometric and response properties.
Using high-vacuum electrospray deposition and scanning tunneling microscopy, this study demonstrates that the conformation of individual P3HT chains on Au(111) is controlled by the interplay between thermal energy and the regularity of the substrate's reconstructed surface potential.
Using machine-learned molecular dynamics simulations, this study provides a microscopic explanation of sulfur's -transition by demonstrating how temperature-induced formation of non-S rings triggers polymerization, ultimately mapping a pressure-temperature phase diagram that reveals a critical point where polymerization merges with the melting line.
This study demonstrates that nanoporous tantalum produced via liquid metal dealloying follows classical Gibson-Ashby scaling laws due to enhanced ligament connectivity, distinguishing its mechanical behavior from that of nanoporous gold and highlighting solvent chemistry as a key factor in tuning the properties of nanoporous metals.
Through extensive 3D dislocation dynamics simulations of FCC Cu, this study resolves inconsistencies in avalanche statistics by demonstrating that the power-law exponent is invariant to dislocation density and loading direction, while the truncation scale is strictly controlled by density, thereby providing robust scaling laws for predictive plasticity modeling.
This study demonstrates that applying explainable machine learning techniques to prune irrelevant and correlated features from a support vector regression model yields a simplified, five-feature predictor for material band gaps that maintains high accuracy while significantly improving generalization and interpretability for materials discovery.
This paper introduces a composition-only machine learning framework based on heterogeneous Rashomon set ensembles to identify design principles for quantum defect host materials, successfully screening 45,000 compounds to predict 122 high-confidence candidates—including TiO and layered chalcogenides—that are validated by density functional perturbation theory calculations.