2732 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 predicts that hole-doping LaNiO to a concentration of induces nearly perfect Fermi surface nesting, which strongly enhances antiferromagnetic spin fluctuations to enable bulk superconductivity at ambient pressure without the need for external pressure or strain.
This paper demonstrates that crystallographic symmetry alone does not uniquely determine electronic charge equivalence, as pressure-induced charge transfer can either break charge equivalence between crystallographically identical sites or preserve it between distinct sites via emergent hidden symmetries, thereby challenging the conventional reliance on Wyckoff positions to predict electronic behavior.
This study introduces a hybrid 1D-2D finite element framework to demonstrate that bending piezo-flexomagnetic nanobeams generates significant external magnetic flux in the surrounding air, a critical factor for designing non-contact nanoscale sensing systems that is often overlooked in existing theoretical models.
This paper demonstrates that a high-throughput Bayesian optimization framework effectively accelerates the discovery of cost-effective cement-salt hydrate composites for seasonal thermochemical energy storage, identifying Pareto-optimal formulations that significantly improve specific energy and cost-performance balance compared to previous cement-based materials.
This study demonstrates that increasing the lattice constant of coinage metal surfaces induces a phase transition in TCNQ monolayers by significantly altering adsorbate-substrate interactions and shifting adsorbate-adsorbate forces from repulsive to attractive, thereby favoring tightly packed polymorphs.
This paper presents a @HF and Bethe-Salpeter equation framework for periodic systems using Gaussian orbitals and density fitting, which corrects Hartree-Fock overestimations of band gaps and valence band widths in semiconductors and oxides by employing an exact RPA screening without plasmon pole approximations and a hybrid convergence strategy for virtual states.
This study develops a data-efficient, accurate machine learning potential based on a fine-tuned MACE foundation model and active learning to enable large-scale molecular dynamics simulations that directly predict lithium self-diffusion coefficients in NMC811 cathode materials, overcoming the time and length scale limitations of traditional density functional theory.
This paper proposes a regional data-centric ecosystem for the Great Plains to overcome barriers in materials innovation by integrating distributed experimental assets with FAIR metadata, uncertainty-aware modeling, and cross-trained workforces, using a high-purity germanium pilot to demonstrate how trustworthy data practices and interoperable infrastructure can drive processing-rich materials discovery.
This paper introduces nb-UiO-FF, a novel partially reactive force field that accurately models the structural, mechanical, and defect properties of the UiO-66 metal-organic framework and enables molecular dynamics simulations of its solvothermal synthesis and self-assembly mechanisms.
This paper proposes an adaptive Density Functional Tight Binding (DFTB) method that dynamically adjusts Slater-Koster parameters based on local atomic environments and oxidation states using machine learning, achieving high accuracy in modeling electronic structures across diverse systems like oxidized nickel surfaces and lithium-intercalated graphite.