2794 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 reveals that ferroelectric polarization in two-dimensional higher-order topological insulators TlS and SnS serves as a tunable mechanism to engineer and reversibly switch orbital Hall conductivity, thereby establishing a new pathway for controllable orbitronics through the coupling of ferroelectricity and band topology.
This study systematically evaluates the impact of dispersion corrections on ab initio simulations of Group-I and Group-II molten fluorides, revealing that while semi-empirical models significantly improve density predictions and are crucial for high-charge-density cations like BeF, their influence on diffusion coefficients is minimal when density is held constant.
This study introduces an uncertainty-aware deep learning framework using Mixture Density Networks to predict phase fractions in refractory multi-principal element alloys, identifies a minimal feature set for robust predictions, and employs an active learning strategy to guide the discovery of novel alloys with unseen elements.
This study provides the first atomic-scale evidence of spin-lattice locking in the 2D d-wave altermagnet RbV2Se2O by utilizing spin-polarized quasiparticle interference mapping to reveal a unified picture of quadruple spin-texture locking involving spin-lattice, spin-momentum, spin-scattering, and a newly identified spin-stripe locking mechanism driven by a spin-density-wave moiré pattern.
This study demonstrates that a plasmonic AuPd/TiO2 photocatalyst enables the selective oxidative coupling of methane and nitrous oxide into valuable C2 and C3 hydrocarbons under ambient conditions by utilizing visible light to lower C-C coupling barriers and suppress overoxidation.
This study demonstrates that ultra-short-pulse laser beam scanning effectively enhances the electrical conductivity and oxygen sensing capabilities of Spatial Atomic Layer Deposition (SALD)-grown ZnO thin films on soda-lime glass, achieving a resistivity reduction of three orders of magnitude through optimized laser parameters while preserving structural integrity.
This study demonstrates that synchrotron-radiation X-ray topography under six-beam diffraction conditions, leveraging the super-Borrmann effect to penetrate thick ammonothermal GaN substrates, enables the quantitative determination of dislocation Burgers vectors and the observation of kinematical-to-dynamical diffraction transitions for nondestructive defect analysis.
This perspective paper reviews the application of Moreau-Yosida regularization in density-functional theory, highlighting its role in reformulating the Kohn-Sham approach, enabling density-potential inversion, and linking the theory to classical field theories, while outlining future development opportunities.
This study establishes phase-contrast microscopy as a nondestructive, high-speed, and high-resolution laboratory technique for three-dimensional imaging of threading dislocations in beta-, offering superior spatial resolution and depth profiling capabilities compared to synchrotron X-ray topography.
This paper systematically analyzes the distinct latent feature representations learned by universal machine-learning interatomic potentials (uMLIPs), revealing significant cross-model differences, dataset-dependent trends, persistent pre-training biases after fine-tuning, and a method for compressing atom-level features into global structure-level descriptors.