2858 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 introduces Kernel-Averaged Mechanical Mismatch (KAMM), a neighborhood-informed feature that, when combined with elastic modulus and hardness, significantly improves the robustness and accuracy of phase clustering in nanomechanical property maps of multiphase materials under realistic conditions of low contrast and diffuse interfaces.
This paper presents a scalable, monolithic fabrication strategy that integrates high-stress silicon nitride membranes with distributed Bragg reflectors using dry processing techniques, achieving self-aligned optomechanical cavities with high optical finesse and mechanical quality factors while eliminating the alignment and stability bottlenecks of conventional methods.
This study demonstrates that embedding a single GaAs quantum dot in a dielectric metasurface enables simultaneous anti-bunched and super-bunched photon emission from neutral and charged exciton complexes, respectively, by enhancing weak charged exciton transitions to achieve comparable count rates.
Addressing the breakdown of Bloch's theorem in spatially varying composites, this paper establishes a foundational ab initio quantum theoretical framework for functionally graded materials that derives effective field equations for modulated Bloch states, revealing non-tensorial electromagnetic properties and enabling the predictive design of optimized electronic devices such as graded p-n junctions.
This paper predicts the existence of previously unobserved Bloch-type and twisted-Néel-type photonic skyrmions in chiral multilayered structures by exploiting the quantum spin Hall effect of plasmonic optical vortices, thereby expanding the photonic skyrmion family and offering new degrees of freedom for chiral sensing and information technologies.
This study utilizes optical pump-terahertz probe spectroscopy to reveal that ultrafast photoconductivity in epitaxial graphene nanoribbons exhibits a non-monotonic fluence dependence driven by an interplay between negative-sign conductivity from secondary hot carriers at low fluences and positive-sign conductivity from excess carriers at high fluences, resulting in complex variations in mobility and plasmonic resonance.
This study demonstrates that resonant mid-IR pulses can suppress quantum fluctuations in to induce a metastable ferroelectric state, providing a first-principles explanation for light-driven control of quantum phase transitions in oxide perovskites.
This study employs machine-learned interatomic potentials to simulate Ruddlesden-Popper chalcogenides, revealing diverse new polymorphs, unique structural behaviors like negative thermal expansion and ascending symmetry breaking, and layer-dependent tilt patterns driven by the competition between octahedral rotations and interface rumpling.
Although YbCuSe lacks magnetic order, significant structural disorder prevents a quantum spin liquid ground state, leading instead to a universal 2D random singlet phase characterized by a distribution of singlet formations.
This study validates the accuracy of the UMA universal machine learning interatomic potential for 25 different MoS2 dopants and demonstrates its ability to efficiently simulate complex phenomena like dopant clustering and layer fracturing, thereby enabling high-throughput computational design of doped MoS2 for targeted applications.