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.
This paper investigates how different types of impurity scattering, particularly polarized magnetic impurities, modulate the nonlinear Hall effect in Weyl semimetals by deriving conductivity tensors within a semiclassical Boltzmann framework and revealing that while magnetic disorder introduces a helicity-dependent anisotropic correction, the isotropic response from scalar disorder remains dominant.
This study demonstrates that interleaving dielectric layers in Hf0.5Zr0.5O2 ferroelectric films enhances the memory window by engineering interfacial oxygen vacancies to create bidirectional internal bias fields, thereby redefining defects from detrimental factors to tunable design elements for optimizing ferroelectric memory devices.
This study demonstrates the successful growth of high-quality antiferromagnetic SrCrO thin films and reveals that while the material maintains a metallic ground state under low strain, increasing both tensile and compressive strain induces a metal-to-insulator transition driven by strain-dependent structural distortions, all while the magnetic transition temperature remains constant.
This paper proposes truncated automatic sparse differentiation to efficiently compute higher-order derivatives for machine learning interatomic potentials by exploiting physical locality to discard negligible long-range interactions, achieving order-of-magnitude speedups with minimal impact on predicted observables.
This study employs multiconfigurational relativistic embedded-cluster calculations to demonstrate that the suppression of altermagnetism in bulk arises from the preservation of -orbital quasi-degeneracy under near-tetragonal symmetry, which inhibits the local quadrupolar order required for spin splitting.
This paper demonstrates that site-specific Al/Li ordering in spinel ferrites creates a ferrimagnetic insulating state with quenching of the Fermi-level density of states, while *ab initio* calculations reveal that the intersection of low-frequency magnon and acoustic phonon modes drives strong hybridization, offering a microscopic pathway to minimize magnetic damping for low-loss magnonic technologies.
This paper establishes a first-principles diagrammatic framework demonstrating that nonlinear one-electron-two-phonon interactions, unlike linear couplings, renormalize phonon frequencies across the entire Brillouin zone with distinct temperature dependence, a phenomenon quantified in polar semiconductors like LiF and KTaO and applicable to materials with strong lattice fluctuations.
This paper introduces a closed-loop active learning pipeline that iteratively retrains on quantum chemical simulation data to overcome the generalization limitations of static models, successfully generating synthesizable molecules with properties that significantly exceed the original training distribution.
This paper introduces BOOM, the first chemically-informed open-source benchmark for systematically evaluating out-of-distribution (OOD) generalization in molecular property prediction, revealing that current machine learning models struggle to extrapolate beyond their training data and highlighting the need for new approaches to achieve robust OOD performance.
This paper introduces machine-learning classifiers trained on experimentally validated MAGNDATA data that achieve over 92% accuracy in identifying magnetic ground states, effectively correcting the systematic ferromagnetic bias found in large-scale Materials Project databases and enabling more reliable high-throughput screening of magnetic materials.