4071 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 investigates electron scattering from chiral spin textures like skyrmions across all coupling strengths, revealing new features such as Ramsauer-Townsend minima and resonances that significantly influence topological and spin Hall conductivities, particularly in the strong-coupling regime relevant to real materials.
This study develops a ReaxFF reactive force field for GaN to demonstrate that externally applied electric fields non-linearly and anisotropically modulate defect migration barriers through charge-lattice coupling, offering critical insights for device reliability in high-field environments.
This paper reports the experimental discovery of unexpected flat bands near the chemical potential in the bulk noncentrosymmetric type-II Weyl semimetal TaRhTe via angle-resolved photoemission spectroscopy, revealing a unique platform where nontrivial topology coexists with flat bands despite the absence of such features in theoretical predictions.
This study demonstrates that free-standing elemental silicon, germanium, and tin slabs exhibit unique stable non-diamond structures, including clathrate layers and specific adatomic patterns, within the 3–6 monolayer range due to surface energy stabilization, while revealing distinct structural preferences and instabilities at lower coverages.
This study systematically evaluates ensemble-based uncertainty quantification methods for neural network interatomic potentials and reveals that predictive precision often fails to reliably indicate accuracy in out-of-distribution regimes, frequently plateauing or decreasing as errors grow, thereby highlighting fundamental limitations in using uncertainty estimates as proxies for accuracy in large-scale materials simulations.
This review examines the critical role, challenges, and limitations of exchange-correlation functionals within density functional theory for accurately predicting the properties of two-dimensional materials, while evaluating advanced computational approaches and emerging strategies like machine learning to overcome current deficiencies in modeling quantum confinement and van der Waals interactions.
This paper reports the development of an X-ray detected ferromagnetic resonance (XFMR) spectrometer featuring an out-of-vacuum photodetector that facilitates easy detector replacement and enables versatile measurements, including XEOL spectroscopy and XFMR signal detection, thereby expanding possibilities for advanced applications like XFMR microscopy.
This study employs first-principles calculations to demonstrate that Janus MoSSe/WSSe heterobilayers function as highly efficient (17.1% STH) selective photocatalysts for overall water splitting by leveraging the synergy between intrinsic interfacial dipoles and metal chemical potential differences to suppress carrier recombination.
This paper demonstrates the optimization of BiSe/buffer/[Pt/CoB/Ru] heterostructure growth via molecular beam epitaxy and sputtering, revealing that a critical buffer layer thickness is essential to suppress BiSe terracing and achieve uniform perpendicular magnetic anisotropy with well-defined domains suitable for spintronics applications.
This paper presents a self-driving microscopy framework that integrates dual-novelty deep kernel learning for adaptive data acquisition and a dual variational autoencoder for representation learning to autonomously map structure-property relationships in halide perovskite films, revealing distinct hysteresis behaviors linked to specific nanoscale structural motifs.