3437 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 investigates the high-pressure behavior of lead-free halide perovskite RbTeBr, revealing a sequence of structural phase transitions from cubic to orthorhombic and monoclinic phases, coupled with pressure-induced band-gap narrowing and non-monotonic photoluminescence changes driven by competing radiative and nonradiative relaxation mechanisms.
By applying an in-plane magnetic field to a magnetic topological sandwich structure, researchers successfully realized a parity anomalous semimetal phase characterized by a single unpaired gapless Dirac cone and a distinctive two-stage conductivity evolution that stabilizes half-integer quantized Hall conductivity against localization.
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.
OXtal is a large-scale, 100M-parameter all-atom diffusion model that leverages a novel lattice-free training scheme and data augmentation to efficiently predict experimentally realizable 3D organic crystal structures from 2D chemical graphs, achieving state-of-the-art accuracy in both molecular conformation and packing while significantly outperforming traditional quantum-chemical methods in speed and scalability.
This paper presents a Wigner Transport Equation-based framework to model non-diffusive thermal transport driven by both phonon populations and coherences, predicting significant size-dependent thermal conductivity deviations in low-conductivity materials like CsPbBr and LaZrO at experimentally accessible length scales.
This study employs the QS method to reveal that the CaSnN semiconductor possesses a direct 2.59 eV band gap suitable for sustainable blue LEDs, while also characterizing its anisotropic optical transitions, excitonic properties, and strain-induced crystal field splitting.
This paper demonstrates that off-label use of low-cost infrared microbolometric cameras can effectively profile terahertz beams with performance comparable to specialized, expensive THz detectors, offering a highly affordable alternative for high-fidelity beam diagnostics.
This paper presents an exascale multi-task graph foundation model built on HydraGNN and trained on over 544 million atomistic structures across 16 datasets, which achieves billion-scale materials screening in seconds and enables efficient fine-tuning for diverse downstream tasks by leveraging high-performance computing resources like Frontier.