3493 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 presents a comprehensive, data-driven framework that integrates machine learning, a novel SISSO-derived tolerance factor, and sustainability metrics to screen and rank stable, experimentally feasible chalcogenide perovskites for next-generation solar energy applications.
This paper proposes a constructive, observation-centered framework for quantum mechanics that prioritizes signal analysis over traditional wave functions to address the mismatch between infinite-dimensional formalism and finite computational accuracy, ultimately aiming to provide rigorous effective descriptions for complex quantum systems.
This review examines the application of generative AI to the inverse design of inorganic compounds, analyzing how data-representation-model pipelines address unique challenges like symmetry and electronic structure across diverse systems while outlining future directions for benchmarking and synthesizability.
This study reveals that wavelength-dependent photo-creep in halide perovskite single crystals arises from a competition between ion migration, which promotes creep, and carrier trapping, which suppresses it, leading to distinct mechanical responses under different illumination conditions that challenge conventional semiconductor photo-plasticity models.
This study combines surface-enhanced Raman scattering (SERS) experiments and density functional theory (DFT) calculations to characterize selected-lanthanide-citrate complexes (Tb through Lu), revealing systematic trends in relative peak intensities that are attributed to variations in lanthanide-oxygen interaction strength and local electronic distribution.
Using time- and angle-resolved photoemission spectroscopy (TR-ARPES) to independently track exciton and carrier populations in bulk ReSe, this study identifies exciton photoionization as the dominant microscopic mechanism for exciton dissociation, establishing a population-resolved strategy for resolving conversion pathways in strongly excitonic materials.
This paper introduces A-Lab GPSS, an agentic AI-driven robotic platform capable of autonomously synthesizing and characterizing air-sensitive lithium halide spinel conductors under strict air-free conditions, successfully optimizing discovery strategies through abductive and inductive reasoning to significantly increase the yield of high-purity, conductive materials.
This study demonstrates that atomically thin tsumoite (BiTe) exhibits exceptional third-order nonlinear optical properties, enabling the design of advanced all-photonic devices such as isolators, information converters, and logic gates for integrated signal processing.
This paper proposes a unified framework for strongly correlated systems that redefines Mott physics through five pioneering discoveries, including golden-ratio scaling of quantum metrics, Fibonacci-sequenced fractional Chern insulators, a Provability Hierarchy Theorem linking strange metals to QMA-hard complexity, geometric-phase-induced nonlinear Hall oscillations, and the quantum geometric tensor as a unified descriptor of band geometry and topology.
The paper introduces XANE(3), an E(3)-equivariant graph neural network that accurately predicts X-ray absorption near-edge structure (XANES) spectra directly from atomic structures by combining tensor-product message passing with a derivative-aware training objective, achieving high fidelity in reproducing spectral features on a large iron oxide dataset.