4135 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 resolves the controversy surrounding the nature of the rhombohedral (R) phase in epitaxial hafnia by utilizing synchrotron-based grazing incidence diffraction to conclusively demonstrate that it is a distinct ferroelectric phase separate from the orthorhombic (OIII) phase.
This study combines polarization-angle resolved Raman spectroscopy with density functional theory calculations to comprehensively identify, assign, and characterize the Raman-active phonon modes in orthorhombic LaInO, successfully extracting relative Raman tensor elements and confirming experimental frequencies through first-principles simulations.
This paper presents a validated 3D multiphysics finite element simulation in ANSYS LS-DYNA that integrates mechanical, thermal, and electromagnetic solvers with a micromechanical constitutive model to accurately predict the thermomechanical behavior and hysteresis of shape memory alloy hybrid composite actuators.
This paper proposes a non-integer-dimensional model for anisotropic solids using q-deformed derivatives inspired by Tsallis statistics to derive thermodynamic properties that accurately match experimental data while linking the deformation parameter to microscopic disorder and memory effects.
The paper presents AutoMAT, an autonomous hierarchical framework that integrates large language models, automated CALPHAD simulations, and AI-guided optimization to efficiently discover and experimentally validate high-performance alloys, successfully identifying a lightweight titanium alloy and a high-entropy alloy with superior properties while compressing the discovery timeline from years to weeks.
This paper demonstrates that the alpha-relaxation dynamics of various glass-formers across different temperature regimes can be universally described by a two-parameter model derived from the two-state, two-scale (TS2) theory, which connects to the Hall-Wolynes elastic relaxation framework.
This study employs a combined first-principles and Wannier-based model to reveal that the direct-to-indirect bandgap transition in layered MoS is driven not only by interlayer - coupling but also critically by previously overlooked - and - orbital interactions between neighboring sulfur atoms.
Using atomistic simulations, this study reveals that edge dislocation cores in BaTiO can either nucleate or pin ferroelectric switching depending on the applied field direction, with the strongest coupling occurring when the field is parallel to the Burgers vector.
This study investigates the previously unexplored role of magnon-magnon interactions in the coherent optical excitation of two-magnon modes in a cubic antiferromagnet, revealing their nontrivial influence on time-domain dynamics and providing a unified theoretical framework that connects spontaneous Raman scattering with Impulsive Stimulated Raman Scattering spectra.
This paper presents a low-cost, open-source IoT platform utilizing an Arduino and LED array to enable physics students to conduct autonomous, closed-loop experiments that train and compare machine learning algorithms, thereby bridging the gap between theoretical ML concepts and practical experimental skills.