4907 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 employs multiscale modeling with machine-learning potentials to reveal how grain boundaries and anion size govern ion transport in argyrodite solid electrolytes, offering critical insights for the microstructural design of high-performance all-solid-state batteries.
This paper presents a quantum-path electron interferometer based on time- and angle-resolved photoemission spectroscopy that successfully reconstructs the previously inaccessible phase of electronic wavefunctions, demonstrated by resolving the phase jumps and helicity of Dirac states in a topological insulator.
This study demonstrates that high-flux soft X-ray synchrotron radiation can partially restore the stoichiometric chemical and electronic properties of degraded, hygroscopic VI3 crystals by reversing surface contamination and vanadate formation.
This study demonstrates that direct-write laser annealing can pattern phase gradients in tungsten thin films to create spin-orbit torque channels capable of switching CoFeB magnetization without external magnetic fields, offering a new strategy for designing efficient spintronic devices.
This paper investigates the growth kinetics and optical properties of fractional-monolayer GaN/AlN quantum wells grown by plasma-activated molecular beam epitaxy, proposing a phenomenological model that links their emission characteristics to specific growth mechanisms and demonstrating their potential as powerful ultraviolet-C emitters.
This paper introduces QHFlow2, a state-of-the-art machine learning Hamiltonian model that significantly outperforms existing methods in energy and force prediction accuracy by directly evaluating predicted Hamiltonians, achieving NequIP-level force precision and up to 20-fold energy error reductions on standard benchmarks.
This study demonstrates that entropy-stabilized (MnFeCoNiCu)N high-entropy nitride nanoparticles serve as highly efficient, stable, and durable visible-light photocatalysts for the rapid degradation and detoxification of antibiotics in complex aqueous environments, with their performance and molecular-level mechanisms validated through in situ interfacial visualization and computational simulations.
This study employs quantum mechanical calculations to demonstrate that chalcogen vacancies in transition metal dichalcogenide monolayers enhance their optoelectronic and photocatalytic performance by reducing band gaps and facilitating charge separation, thereby establishing these defective structures as viable materials for hydrogen evolution, CO reduction, and optoelectronic applications rather than flawed products.
This study demonstrates that the interplay between twisted bilayer graphene and graphene/h-BN moiré lattices, governed by global twist configurations and rotational relaxation, drives a multi-scale "moiré-moiré reconstruction" that creates diverse commensurate double-moiré phases, offering a general framework for engineering structural and electronic properties in multilayer van der Waals heterostructures.
This paper introduces MatPhaseBench, a high-quality, human-validated benchmark derived from 3,681 materials science papers to evaluate the capabilities of Vision-Language Models in understanding complex materials phase diagrams, revealing that current models significantly lag behind expert-level reasoning due to their reliance on surface visual perception rather than deep thermodynamic mechanism analysis.