4173 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 strong uniaxial anisotropy in iron films enables an anomalous inverse orbital Hall effect, where orbital dynamics mediated by high orbital Hall conductivity facilitate efficient orbital-to-charge conversion in out-of-plane configurations, offering new avenues for controlling spin and orbital currents in orbitronics devices.
This paper presents a unified theoretical framework demonstrating that the temperature-dependent electronic spectra and thermodynamic properties of magic-angle twisted bilayer graphene arise from the cooperative interplay between electron correlations and external symmetry-breaking effects induced by strain and lattice relaxation.
This study presents an efficient computational framework combining embedded cluster expansions and Monte Carlo simulations to predict equilibrium vacancy concentrations in multi-principal element alloys, revealing how alloy composition and short-range order influence vacancy behavior to guide the design of complex concentrated alloys.
This paper reviews the unique thermoelectric advantages of topological semimetals, establishes optimal design principles to maximize their figure of merit (), and utilizes these principles to identify twelve promising new materials for high-efficiency thermoelectric applications.
This paper experimentally demonstrates that orbital-to-charge conversion via the inverse orbital Hall and Rashba effects dominates over spin-related mechanisms in metallic and semiconductor heterostructures, revealing significant signal enhancements in oxidized copper and distinct orbital diffusion behaviors in titanium and germanium to advance the field of orbitronics.
This paper benchmarks universal machine learning interatomic potentials (uMLIPs) against a domain-specific model for supported Cu/AlO nanoparticles, finding that while uMLIPs like MACE-OMAT and MatterSim-v1.0.0-1M can effectively identify stable structures and reproduce molecular dynamics trends without fine-tuning, their significantly higher computational cost remains a limiting factor for large-scale simulations.
This study demonstrates that discretized Halbach spheres constructed from permanent magnets arranged with icosahedral symmetry offer a practical and scalable solution for generating highly homogeneous magnetic fields, achieving usable volumes up to 260 times larger than traditional cylindrical arrays while maintaining deviations below 1%.
This study experimentally investigates spin and orbital torque phenomena in various normal metal/ferromagnet bilayers using spin-torque ferromagnetic resonance, successfully extracting torque components and providing compelling evidence for orbital torque driven by the orbital Hall effect, including the demonstration of an out-of-plane torque attributed to interfacial mechanisms.
By combining first-principles calculations with machine-learned potentials for large-scale molecular dynamics, this study reveals how cooperative octahedral rotations and dominant four-phonon scattering drive negative thermal expansion and a non-monotonic thermal conductivity anomaly in CaSnF, establishing a unified mechanism linking structural phase transitions to anharmonic lattice dynamics and macroscopic transport properties.
This paper demonstrates the application of variational quantum deflation to study Frenkel excitons on NISQ hardware, introducing a novel deep learning-based error mitigation framework that outperforms conventional post-selection techniques.