4797 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 proposes a novel electromechanically coupled multiphase-field framework that integrates generalized kinetic relations directly into the evolution law, enabling the accurate simulation of nonlinear domain wall dynamics and distinct interfacial properties in ferroelectric materials.
This paper presents a transferable, physics-based multiscale full-band Monte Carlo framework that links atomistic tight-binding band structures and alloy-disorder scattering to accurately model avalanche gain and excess noise in complex quaternary alloy avalanche photodiodes, such as AlInAsSb, with minimal calibration.
This study theoretically demonstrates that non-phenomenological electron-phonon coupling, particularly from optical phonons, significantly broadens and closes Floquet topological gaps in laser-driven graphene, explaining their experimental absence while proposing mitigation strategies like pre-pumping coherent phonons or utilizing alternative Dirac systems to enable direct observation.
This paper theoretically demonstrates that a spatially varying temperature profile in ferromagnet/antiferromagnet bilayers induces chirality-selective magnon transport and generates a thermally triggered spin current in the antiferromagnet, offering new insights for designing magnonic heterostructures.
This study utilizes scanning near-field optical microscopy and low-temperature scanning tunneling microscopy to reveal atomic-scale lattice relaxation and topological flat bands in helical trilayer graphene, identifying a magic angle of approximately 1.9° and establishing a direct microscopic link between lattice structure and electronic properties.
Using x-ray photon correlation spectroscopy, this study demonstrates that a fractal nanoparticle gel can be trained to exhibit microstructural reversibility and memory of random shear driving, albeit more slowly and irregularly than with deterministic cyclic shear, by inducing internal stress redistribution and diminishing irreversible strain displacements.
This paper introduces the calibrated Mahalanobis (CALM) extrapolation grade, a computationally efficient, per-atom uncertainty metric integrated into GRACE foundation potentials that reliably detects structural and chemical extrapolation by correlating latent-space distances with force errors to guide targeted data collection.
This paper proposes a "two-parent" energetic framework explaining the widespread yet boundary-confined superconductivity in crystalline graphene as a result of first-order transitions between degenerate isospin-ordered normal states, where even a small pairing energy gain can stabilize a superconductor by selecting the normal state parent that maximizes the net free energy gain.
This paper presents a phase-field modeling strategy that incorporates CALPHAD-type thermodynamic descriptions of ordering in liquid solutions by defining internal variables and using Allen-Cahn relaxation equations, demonstrated through the formation of stoichiometric phases in Na-Sb and Na-Sn systems.
This study demonstrates that incorporating interaction and strengthening coefficients derived from discrete dislocation dynamics simulations into phenomenological crystal plasticity models significantly enhances their predictive quality and parametrization efficiency without incurring additional costs.