2832 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 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.
This study utilizes a custom-built cryogenic magneto-THz scattering-type scanning near-field optical microscopy platform to visualize the nanoscale evolution of colossal magnetoresistance in , revealing that magnetic-field-induced spin switching initiates as 1–2 nm isolated sites that coalesce into ~15 nm conducting regions during the transition from an antiferromagnetic insulator to a ferromagnetic metal.
This study utilizes ab initio molecular dynamics simulations to reveal that while Ge atoms initiate the structural collapse of crystalline Cr2Ge2Te6 upon heating, the robust Cr[Te6] octahedra persist through the melting and supercooled liquid phases, a structural stability that underpins the material's low resistance drift and ultrafast nanosecond-scale crystallization for phase-change memory applications.
This paper demonstrates that the quantum metric serves as a sensitive geometric probe for persistent spin helices by revealing a divergence caused by a hidden line degeneracy at the balanced spin-orbit coupling condition, which is subsequently regularized by higher-order cubic interactions.
Using Vicinal Cellular Automata modeling, this paper demonstrates that the geometric shape of nanopillars—ranging from crystal-symmetry-following structures to universal circular forms—is determined by the spatial distribution of growth potential (local vs. global) and can be effectively manipulated by adjusting temperature and external particle flux.