3992 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 presents a theoretical framework for elastic wave propagation in linear flexomagnetic solids with microstructure, revealing that the interplay between flexomagnetism and strain gradients induces unique phenomena such as normal and abnormal dispersion, transverse waves exceeding longitudinal phase velocities, wave attenuation, and exotic behaviors like zero or negative group velocity modes and wave freezing.
This theoretical study combines Landau--Lifshitz--Gilbert and Brownian dynamics to reveal that increasing AC field amplitude drives a crossover in magnetic nanoparticle orientation from perpendicular to parallel alignment at approximately half the anisotropy field, thereby dictating the transition between Brownian and Néel heating mechanisms in magnetic fluid hyperthermia depending on excitation frequency.
This paper develops a third-order homogenized instability theory based on a Lippmann-Schwinger formulation to predict how random fluctuations in film stiffness affect the critical load and wavelength of wrinkling in film-substrate systems, revealing that increased stiffness contrast lowers the critical load and shifts instability toward shorter wavelengths.
By combining first-principles lattice dynamics with Raman spectroscopy and diffraction data, this study resolves the structural controversy of bulk hexagonal diamond by demonstrating that its interlayer bonds are longer than its intralayer bonds, thereby identifying a specific positive bond asymmetry that contradicts previous conflicting refinements.
This paper introduces a general finite-difference framework for computing resonant Raman tensors using any electronic-structure method, demonstrating that hybrid functionals and meta-GGAs outperform semilocal DFT in predicting intensities for graphene and monolayer MoS by more accurately capturing electron-phonon coupling and reducing dielectric overscreening.
This paper addresses the challenge of determining the stress-free lattice spacing () in neutron-based strain measurements by reviewing and establishing a hierarchy of non-destructive, equilibrium-based estimation methods, which are validated through diverse real-world examples ranging from additively manufactured alloys to ancient artifacts where destructive sampling is impossible.
This study establishes that TbFeO exhibits pronounced interplay among lattice dynamics, spin correlations, and emergent local magnetic-lattice anomalies, characterized by negative thermal expansion, spin-phonon coupling, and a low-temperature mode indicative of complex magnetism without structural phase transitions.
This paper presents a fully *ab initio* theoretical study demonstrating that spin-fluctuation-mediated interactions drive -wave superconductivity in infinite-layer nickelates, a mechanism that successfully explains experimental observations and predicts specific properties for immediate verification.
This study utilizes spin- and angle-resolved photoelectron spectroscopy to characterize the Rashba-split valence bands and demonstrate spin-momentum locking in both the topologically trivial precursor phase and the quantum spin Hall insulator bismuthene at the graphene/SiC interface.
This paper presents an uncertainty-guided active learning framework using deep ensembles to efficiently predict stream-finishing wear fields directly from workpiece geometry, achieving high accuracy with only 13% of the required discrete element method simulations by selectively targeting the most uncertain orientations.