4134 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 post-growth annealing with SrI2 significantly enhances the crystallinity and optoelectronic quality of phase-pure SrZn2P2 thin films by promoting grain growth and improving photoluminescence uniformity, offering a viable pathway for advancing Zintl phosphide semiconductors in optoelectronic applications.
This study demonstrates that epitaxial VO2 thin films exhibit a thermally switchable, type-II hyperbolic optical response in their metallic rutile phase due to intrinsic crystalline anisotropy, establishing them as a promising platform for tunable and reconfigurable photonic devices.
This study employs lattice Boltzmann simulations within a lubrication-theory framework to quantify how film thickness, surface energy, and wettability govern thin film dewetting kinetics and morphology, ultimately providing predictive design guidelines for coating stability and surface engineering.
This paper presents a multirate characterization of relaxation mechanisms for two non-equivalent nuclear spins in a liquid, combining conventional measurements with novel techniques using maximally entangled pseudo-pure Bell states to experimentally and theoretically validate microscopic theories, identify unconventional relaxation contributions, and establish a universal ratio for intra-pair magnetic dipolar interactions.
This paper reports the fabrication of vertical NiOx/(011) -GaO heterojunction diodes with a breakdown voltage exceeding 10 kV and a power figure of merit over 2.3 GW/cm, achieving a record-breaking parallel plane breakdown field of >5.3 MV/cm in thick (011) -GaO epitaxial layers.
This paper introduces AutoREC, an open-source Python platform that leverages reinforcement learning to automate the generation of equivalent circuit models from electrochemical impedance spectroscopy data, achieving high accuracy on synthetic datasets and strong generalization across diverse experimental systems to enable scalable, autonomous electrochemical analysis.
This study demonstrates that electrostatic gating of the 3D Dirac semimetal Cd3As2 enables programmable control over the chirality of terahertz emission by selectively modulating the Berry curvature-driven linearly polarized component, thereby allowing the polarization state to be tuned across the Poincaré sphere.
This study utilizes a novel deep learning framework combining MACE machine learning potentials and equivariant graph neural networks to simulate ultrafast, coherent sliding ferroelectric switching in bilayer hexagonal boron nitride, revealing a viable 5-picosecond mechanism that reproduces experimental hysteresis loops.
This study demonstrates that FePt/FeRh bilayers significantly enhance heat-assisted magnetic recording efficiency by leveraging the FeRh phase transition to reduce FePt coercivity through interfacial exchange coupling and improved domain wall mobility, rather than by softening intrinsic anisotropy.
VibroML is an open-source Python toolkit that leverages machine-learned potentials and genetic algorithms to automate the remediation of dynamical instabilities, validate finite-temperature stability, and systematically explore compositional spaces, thereby transforming high-throughput materials screening from mere stability verification into a comprehensive workflow for generating physically viable crystalline structures.