3229 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.
By demonstrating that the activation energies for both dielectric relaxation and DC transport are nearly identical, this study suggests that the colossal permittivity in is a bulk phenomenon driven by the same microscopic forces that govern charge mobility in a system nearing metallicity.
This study uses Boltzmann transport theory to demonstrate that Coulombic scattering mechanisms—specifically ionized impurity scattering and polar optical phonon scattering—are the dominant factors determining the thermoelectric power factors of n-type and p-type half-Heusler alloys.
Through extensive 3D dislocation dynamics simulations of FCC Cu, this study resolves inconsistencies in avalanche statistics by demonstrating that the power-law exponent is invariant to dislocation density and loading direction, while the truncation scale is strictly controlled by density, thereby providing robust scaling laws for predictive plasticity modeling.
This study demonstrates that applying explainable machine learning techniques to prune irrelevant and correlated features from a support vector regression model yields a simplified, five-feature predictor for material band gaps that maintains high accuracy while significantly improving generalization and interpretability for materials discovery.
This paper introduces a composition-only machine learning framework based on heterogeneous Rashomon set ensembles to identify design principles for quantum defect host materials, successfully screening 45,000 compounds to predict 122 high-confidence candidates—including TiO and layered chalcogenides—that are validated by density functional perturbation theory calculations.
Through extensive computational investigations, this study identifies new thermodynamically stable carbon defect configurations in MoS2 and refutes recent claims that these impurities cause electrical doping, demonstrating instead that they act as deep carrier traps.
This study demonstrates that in combined extensional-shear Jeffery-Hamel flows, the flow birefringence of a cellulose nanocrystal suspension follows a root-sum-square relationship of shear and extensional contributions, thereby validating the extension of stress-birefringence analysis to complex, multi-mode deformation fields.
This paper develops a symmetry-controlled theory predicting that temperature gradients in insulating altermagnets generate anisotropic thermomagnonic torques, leading to unique phenomena such as reduced domain-wall velocities and anisotropic skyrmion motion with suppressed transverse deflection.
This paper provides a concise yet comprehensive overview of the fundamental principles and methodologies of X-ray photoelectron spectroscopy (XPS), aiming to bridge the gap between easy data acquisition and reliable interpretation by clarifying essential concepts such as photoemission processes, chemical shifts, charge referencing, peak fitting, and quantification strategies for newcomers to the field.
This study demonstrates that K-ion intercalation in electrodeposited Prussian blue analogs drives reversible nanoscale resistive switching, establishing a low-cost, scalable, and ultrafast memristive platform suitable for neuromorphic and memory applications.