2856 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 proposes that moire excitons in two-dimensional superlattices exhibit cooperative quantum optical phenomena, such as tunable superradiant and subradiant states and extreme optical switching, driven by their real-space lattice structure and controllable via electric fields, strain, or twist angle adjustments.
By synthesizing high-quality rutile OsO2 single crystals, researchers discovered that while the material is initially a paramagnetic metal, applying high pressure (44 GPa) induces a metal-insulator transition and drives a phase change into an altermagnetic state, demonstrating that external pressure can effectively tune its magnetic ground state.
This study reports the successful synthesis of bulk OsO2 single crystals that outperform commercial RuO2 nanopowder in oxygen evolution reaction efficiency and stability, challenging the universal applicability of nanoscaling by demonstrating that crystal integrity is a critical descriptor for robust electrocatalysis.
This study demonstrates that biaxial strain can dynamically tune the topological order and piezoelectric functionality of Tc-adsorbed penta-hexa silicene monolayers by engineering momentum-space orbital hybridization, thereby transforming static materials into versatile, strain-responsive quantum platforms.
This study investigates how layer structure and strain influence the electronic properties and surface morphology of InAs/InGaAs quantum wells on InP (001), revealing that layer design dictates mobility anisotropy, excessive thickness triggers quantum well collapse, and quantum confinement significantly affects band nonparabolicity.
This paper demonstrates that robust defect detection in fluctuating magnetic systems, such as Ni80Fe20, can be achieved by training U-Net models on statistical descriptors like temporal mean, standard deviation, and latent entropy derived from micromagnetic simulations, provided the training data accurately reflects the expected noise statistics.
This paper presents an AI-driven workflow combining a Gaussian mixture variational autoencoder with Pearson correlation coefficients to analyze sparsely sampled X-ray hyperspectral data, enabling the generation of nanometer-resolution multiphase maps that reveal complex phase heterogeneity and transition zones in individual Na-ion cathode particles during electrochemical cycling.
High-quality single-crystalline DyNiSb exhibits two distinct magnetic transitions and metallic conductivity, contrasting with previous polycrystalline reports, while displaying complex magnetotransport behavior including weak antilocalization and a field-induced Fermi surface reconstruction.
This study characterizes the thermodynamic and magnetotransport properties of single-crystalline ErPdSb, revealing its antiferromagnetic ordering at 1.2 K, semimetallic behavior with a resistivity hump near 70 K, a transition from weak antilocalization to negative magnetoresistance in magnetic fields, and a sizable anomalous Hall effect at low temperatures indicative of Fermi surface reconstruction.
This paper benchmarks three machine learning frameworks (MODNet, CrabNet, and a Magpie-based Random Forest) for predicting battery electrode properties using the Materials Project dataset, demonstrating that CrabNet consistently outperforms the others across rigorous statistical validation while highlighting both the potential and practical limitations of ML-driven materials discovery.