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 reports the successful demonstration of high-performance top-gated SrSnO MOSFETs featuring ultra-wide bandgap properties, high mobility, and excellent switching characteristics, establishing the material as a promising platform for next-generation power electronics.
This study utilizes advanced spectroscopy to demonstrate that long 48-carbon polyynes exhibit highly delocalized ground states with weakened Peierls distortions and rapid excited-state self-localization, revealing that their electronic properties have plateaued near the carbyne limit and are significantly influenced by chain topology.
This paper presents a theoretical framework for elastic wave propagation in flexomagnetic solids with microstructure, revealing that the coupling between magnetism and strain gradients induces unique phenomena such as normal and abnormal dispersion, anomalous phase velocity relationships, wave attenuation, and exotic modes like zero group velocity and wave freezing, which are absent in classical linear elasticity.
This study employs a machine-learned DFT+ approach optimized via Bayesian inference to reproduce quasiparticle self-consistent $GW$ results, identifying CoTiSn and CoZrAl as the most promising half-metallic Heusler candidates for epitaxial growth on InAs while highlighting the critical dependence of spin polarization on the computational method used.
Using real-time time-dependent density-functional theory, this study reveals that terahertz pulses induce transient spin and orbital magnetization in SrTiO through a mechanism where light transfers angular momentum to electronic orbitals, which is subsequently converted to spin via spin-orbit coupling, thereby dynamically breaking inversion symmetry and explaining recent observations of light-induced multiferroicity.
This paper introduces a novel computational framework combining *ab initio* electronic-structure methods with machine learning to accurately predict the temperature-dependent optoelectronic properties of chemically disordered, anharmonic solid solutions, specifically demonstrating its efficacy on silver chalco-halide systems.
The paper introduces ActiveStructOpt, an active learning framework that integrates graph neural network surrogate models to efficiently and accurately determine atomic structures from diverse spectroscopic data, outperforming existing methods under equivalent computational budgets.
This paper demonstrates that engineering the composition of highly porous SnO2-graphene oxide nano-heterojunctions enables the tunable development of room-temperature selective chemical sensors for trace volatile organic compounds and high-performance visible-blind photodetectors.
The paper introduces Symmetr, a Python package designed to determine the symmetry-restricted forms of physical property tensors in crystals, with a specific focus on magnetic materials and support for both magnetic space groups and spin groups.
This paper analyzes the long-range van der Waals interactions between filamentary objects, revealing that multi-filament dispersion energies alternate between attractive and repulsive terms as the number of objects increases due to electronic screening and anti-screening effects, thereby indicating the necessity of non-perturbative calculations for accurate predictions.