4797 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.
ZStar is an open-source Python toolkit that automates the calculation of polarization, Born effective charges, dielectric response, and infrared and Raman spectra for materials of all dimensionalities through a unified, symmetry-adapted workflow that enhances computational efficiency and supports both conventional and agent-assisted usage.
This paper introduces a probabilistic deep learning framework that unifies image compression, temporal prediction, and microstructure forecasting to accurately predict the evolution of Widmanstätten ferrite in S235 steel under arbitrary thermal histories using in situ high-temperature microscopy data.
This paper applies a physics-based prediction framework to the unsynthesized iron phosphide NaFeP, revealing that while structural rules suggest nodal superconductivity, the electronic structure predicts an s± state with a near-nodal inner hole sheet, thereby demonstrating the height-dependent transition between nodal and nodeless pairing within a single compound.
The paper introduces MEOWN, a physics-informed machine learning framework that rapidly and accurately predicts muon stopping sites in crystalline materials by combining electrostatic modeling with symmetry-driven optimization, offering a computationally efficient alternative to traditional density functional theory methods for studying quantum magnetism.
This paper introduces the emergent ternary stabilization energy () as a metric to quantify the unique thermodynamic stability of inorganic ternary compounds beyond binary limits, revealing that polyanion compounds and those with high cation electronegativity contrast gain the most significant stabilization from compositional complexity.
This paper demonstrates that an AI agent workflow utilizing GPT-6 Astra can successfully optimize the thermal design of 2D CFET inverters by proposing and validating structural modifications that significantly reduce peak temperatures while quantifying associated electrical trade-offs.
This study establishes that the high-pressure optical response of alkali chlorides and alkaline-earth oxides is governed by anion polarizability, which is predictably modulated by coordination number and bond length, revealing distinct transferability behaviors for oxide versus chloride anions across B1 and B2 crystal structures.
This paper introduces basis-adaptive texture tomography, a method that replaces uniform orientation grids with candidate orientations from peak indexing to achieve higher angular resolution and more accurate mapping of complex grain and subgrain structures in polycrystalline materials exhibiting spotty diffraction rings.
This study characterizes a synthesized nitrogen-doped porous armchair graphene nanoribbon, revealing that while periodic perforation primarily dictates its mechanical and thermal properties, nitrogen substitution is the dominant factor controlling its electronic and optical behavior, including bandgap tuning and exciton binding.
This study demonstrates that combining pre-alloyed master-alloy powders with a large layer thickness, remelting, and heat treatment in laser powder bed fusion effectively produces chemically homogeneous, single-phase Ti-Nb-Fe-Sn alloys with tunable mechanical properties and low elastic modulus, overcoming the chemical heterogeneity typically associated with elemental powder blending.