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 extends the dynamical Hubbard functional formulation to spin-polarized and multi-site cases, demonstrating that this efficient and transparent approach accurately captures electronic correlations in transition-metal monoxides (MnO, FeO, CoO, and NiO) with results comparable to or better than state-of-the-art theories.
This high-throughput first-principles study investigates the magnetic properties of lead-free halide double perovskites, revealing that while most exhibit antiferromagnetism, specific alloyed compounds like and show promise as high-temperature ferromagnetic half-metals for spintronics applications.
This paper presents a thermodynamically consistent finite element scheme derived from the thermodynamic extremal principle that unifies Cahn-Hilliard phase-field models and CALPHAD-based reactive diffusion models into a single framework capable of handling both regularized interfaces and degenerate Stefan-type problems on fixed meshes while ensuring exact mass conservation and discrete energy dissipation.
This paper employs a nonlinear modes framework and efficient sampling algorithms to derive and quantitatively confirm analytic predictions for the asymptotic distributions of activation barriers and plasticity-onset forces in an anharmonic mean-field glass model, with results expected to apply to realistic finite-dimensional glasses.
This paper presents an extended deep energy method that utilizes two neural networks and a sharp polyline representation to solve coupled thermo-mechanical crack propagation problems, achieving high accuracy in stress intensity factor extraction and crack path prediction across various loading conditions without requiring a regularization length.
This paper introduces an equivariant graph neural network that rapidly and accurately predicts the frequency-dependent dynamic polarizability tensors of organic molecules, enabling efficient large-scale screening for optoelectronic applications as a computationally cheaper alternative to traditional TD-DFT methods.
This paper presents a novel experimental setup combining an ex-situ He neutron spin filter and a two-dimensional detector to overcome the limitations of traditional triple-axis spectrometers, successfully enabling the analysis of out-of-plane, noncollinear incommensurate magnetic orders in complex materials like MnWO.
By combining XAS and RIXS to study PrNiO, researchers reveal that the material's multilayer architecture, rather than rare-earth substitution, is the primary determinant of its magnetic interactions and multiorbital electronic structure.
This paper introduces a novel three-dimensional metamaterial design inspired by rope kinematics that achieves acoustic properties indistinguishable from water, effectively realizing the long-sought "metal water" with a high bulk modulus and low shear modulus.
By combining first-principles calculations with device modeling, this study reveals that the coexistence of distinct crystal facets in low-dimensional pnictogen chalcohalides causes significant band-edge misalignment that drastically reduces open-circuit voltage, identifying surface orientation as a critical design parameter to bridge the gap between theoretical and experimental solar cell efficiencies.