4820 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 establishes through theory, simulation, and experimental refinement that while anomalous absorption effects in 3D electron diffraction generally cause negligible errors for routine structure determination, they become significant and necessary to include for high-atomic-number materials at thicknesses approaching the extinction distance.
This paper utilizes point group representations to classify four distinct types of spin-orbit coupling-induced spin splittings (Rashba, Dresselhaus, Weyl, and Ising) in noncentrosymmetric non-magnetic crystals, deriving their energy expressions, minimal tight-binding models, and nodal features while identifying specific material realizations to provide a foundational framework for studying related collective electronic phenomena.
This study demonstrates that diamond particles embedded in a cubic boron nitride matrix undergo a shock-induced phase transformation into graphite during hypersonic impact at Mach 8.45, a process that serves as the primary mechanism for energy absorption and fracture in the composite.
This paper introduces a framework of optimally embedded tight-binding models that treats orbital positions as tunable parameters to quantitatively reproduce geometry-dependent observables, such as non-linear optical responses, without compromising band structure accuracy.
This paper introduces a continuous-wave quantum relaxometry protocol that overcomes the temporal limitations of conventional pulsed methods by extracting spin-lattice relaxation times () from frequency-domain responses, thereby enabling efficient, high-throughput sensing across a broad range of timescales and temperatures, including in nanodiamonds.
This study employs ab initio calculations to demonstrate that metal-encapsulated biphenylene bilayers serve as efficient bifunctional catalysts for oxygen reduction and evolution reactions, with specific metals like Mn and Fe identified as optimal for ORR and OER respectively, driven primarily by the d-orbital charge population of the intercalated metal.
This paper demonstrates that a printable hexagonal boron nitride ionogel enables efficient low-voltage gating of epitaxial Bi (111) films, revealing a non-rigid band response with tunable Rashba spin-orbit coupling and multiband transport effects that cannot be explained by conventional Fermi level shifts.
This paper proposes a machine-learning charge density (MLCD) approach that, by optimizing training sets with mixed-size small supercells, achieves high-accuracy prediction of large-supercell defect formation energies with significantly greater data efficiency and lower error rates compared to traditional machine-learning interatomic potentials.
This study utilizes a mass-selected ion beam apparatus to measure the etching yields of Si, SiO2, and Si3N4 under irradiation by various silicon, halogen, and silicon halide ions, revealing that silicon tri-halide ions exhibit superior etching performance at high energies while mono-halide ions tend to deposit silicon at low energies, thereby providing critical data to enhance the precision of etching process simulations.
This study proposes a multimodal deep learning framework that integrates volume fractions, persistent diagrams, and spatial correlations derived from 3D microstructure images to accurately predict local strain localization in Mg/LPSO two-phase alloys, revealing that high strain concentrates in regions where the hard LPSO phase is elongated at a 45-degree angle to the loading direction.