2898 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 paper presents a fully variational locally scaled self-interaction corrected energy functional that utilizes complex optimal orbitals and a kinetic energy density-based scaling factor to dynamically adjust the correction across different electron density regimes, thereby improving predictions for atomic, molecular, and solid-state systems.
This paper compares projection-based and slip-gradient methods for estimating geometrically necessary dislocation (GND) densities in crystal plasticity finite element models, revealing that while both align with analytical trends, the projection method significantly underestimates GNDs in polycrystals unless it is improved by restricting calculations to only active dislocation systems.
This paper introduces a polymer-free fabrication method to successfully isolate and image air-sensitive 2D transition metal di-iodides at the monolayer limit, revealing their unique structural characteristics, including low-energy stacking barriers and stable iodine vacancies, while demonstrating a versatile platform for creating clean suspended van der Waals heterostructures.
The paper introduces LEGO-xtal, a symmetry-informed AI generative framework that rapidly produces diverse crystal structures matching a target local environment by combining AI-generated initial structures with machine learning-based optimization, successfully expanding a small set of carbon allotropes into over 1,700 viable candidates.
This paper presents the significant scientific and technical advancements in the Abinit software package over the past five years, highlighting new capabilities in ground-state and excited-state methodologies, GPU-accelerated high-performance computing, second-principles modeling, and automated workflows designed to support high-throughput predictive modeling of solids and nanomaterials.
This study demonstrates that integrating a thermally conductive AlN insulator with a high-permittivity BaTiO3 field-plate in vertical -GaO Schottky barrier diodes effectively mitigates thermal hotspots and enhances electric field management, thereby significantly improving heat dissipation and breakdown performance for high-power applications.
This study presents a scalable, bioinspired brick-and-mortar microstructure design for lead-free Bi0.5Na0.5TiO3 piezoceramics that significantly enhances mechanical strength, fracture toughness, and fatigue resistance without compromising piezoelectric performance, thereby enabling durable energy transducers.
This paper introduces a novel time-resolved measurement method to demonstrate that the colossal and slight enhancements in the Seebeck effect observed in superionic semiconductors (Cu2Se and Ag2S) during structural phase transitions are not intrinsic phenomena.
This paper introduces InSpecLearn4SDL, an interpretable machine learning pipeline that utilizes a genetic algorithm and SHAP-guided feature selection to predict the electrical conductivity of doped conjugated polymers from rapid optical spectra, thereby reducing experimental effort in self-driving labs by approximately 33% while recovering key physical descriptors.
This study demonstrates that adsorbing enantiopure heterohelicene molecules onto a non-magnetic Cu(100) surface induces a localized spin-polarized state in the topmost copper layer through strong chemisorption-driven hybridization and Coulomb correlation, revealing a mechanism for generating magnetism at organic-inorganic interfaces without intrinsic magnetic components.