4179 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 demonstrates that strong Ru–Mn interfacial exchange coupling in (LSMO/SRO) superlattices grown on SrTiO governs distinct two-step magnetization switching and tunable microwave damping, highlighting these oxide heterostructures as promising platforms for room-temperature spintronic applications.
This paper extends the cross derivative of Gibbs free energy to quantum systems, demonstrating its effectiveness in identifying Gaussian-type quantum phase transitions in the spin-1 XXZ chain by revealing a logarithmically diverging valley structure that accurately yields critical points and exponents consistent with established literature.
This study combines first-principles calculations, symmetry analysis, and dual spin-resolved spectroscopies (spin-ARPES and spin-ARRES) to experimentally map and distinguish relativistic and nonrelativistic spin splittings in the altermagnet CoNbSe across both occupied and unoccupied states, thereby establishing a prototype for exploring spin-group phenomena in layered antiferromagnets.
This study predicts that phosphorus carbide nanotubes (NTs) are a stable, chemically realistic quasi-one-dimensional material that uniquely hosts coexisting Dirac fermions and robust flat bands at the Fermi level, while exhibiting strain-induced structural and quantum phase transitions, localized edge states, and tunable magnetism for potential applications in quantum hardware and spintronics.
This study demonstrates that coating niobium superconducting resonators with alkyl-phosphonate self-assembled monolayers effectively suppresses native oxide regrowth, thereby maintaining high quality factors and temporal stability over six days of air exposure while significantly reducing two-level system losses.
This paper introduces a systematic series of integral equation-based approximations (Hedin I, II, III, etc.) that converge to the exact Hedin equations, offering a numerically tractable method to improve upon the GW approximation and capture increasingly complex Feynman diagrams, as demonstrated through zero-dimensional field theory.
This study demonstrates that friction can arise entirely from contactless magnetic interactions in a sliding dipole array, exhibiting a non-monotonic dependence on load due to collective magnetic reorientations and dynamical frustration that peak at intermediate distances.
Using density functional theory with rigorous convergence and consistency checks, this study refutes the long-held assignment of specific photoluminescence lines in CuO to copper and oxygen vacancies, demonstrating instead that oxygen interstitials, copper interstitials, and a specific split copper vacancy are the only native defects responsible for robust in-gap states.
This paper presents a Gaussian Process Regression-based Knowledge Distillation framework that leverages experimental literature data and molecular descriptors to simultaneously and accurately predict multiple physical and mechanical properties of diverse epoxy polymers, thereby overcoming limitations of existing machine learning models and accelerating the design of novel materials.
This paper presents a machine intelligence framework that streamlines the full chain of 2D ReSe₂ dendrite synthesis by utilizing active learning for rapid process optimization, data augmentation for precise morphology control, and a dual-driven model for comprehensive mechanism deciphering.