4962 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 demonstrates that nonlinear phonon dispersion in disordered solids arises from a disorder-induced mesoscopic lengthscale and, through analysis and simulations, reveals that both this nonlinear softening and non-phononic vibrations significantly contribute to non-Debye anomalies like the boson peak, with their relative importance depending on the material's disorder strength.
This study demonstrates that polar dielectric materials exhibit thermoreflectance coefficients significantly superior to traditional metal transducers, establishing them as highly effective candidates for next-generation optical thermal metrology through the introduction of a new design-oriented figure of merit and experimental validation on SiO2 films.
This study demonstrates that sol-gel-derived NiO/ZnO heterostructure thin films, enhanced by NaCl doping and optimized stacking order, achieve superior specific capacitance (1.627 Fg⁻¹) compared to single-layer counterparts, highlighting their potential as cost-effective electrode materials for supercapacitors.
This paper proposes a physics-informed machine learning framework that predicts the Euler characteristic of input images by training neural networks to generate unit vector fields (interpreted as spin configurations) and computing their skyrmion number, utilizing a magnetic Hamiltonian as a loss function to constrain degrees of freedom without requiring large pre-existing datasets.
This paper proposes a theoretical framework to induce tunable odd-parity spin splittings in abundant collinear altermagnets by coupling them to a static, symmetry-equivalent -odd loop-current order generated via two-color linearly polarized light or intrinsic order, thereby enabling controllable mixed-parity spin textures for advanced spintronic applications.
This paper establishes a unified framework based on the Cooper pair quadrupole moment to define superconducting pair size, revealing that in flat-band systems with broken time-reversal symmetry, Berry curvature provides a crucial geometric contribution that, together with the quantum metric, sets a fundamental lower bound on the coherence length.
This study demonstrates that targeted composition design in chemically disordered spinel-type high-entropy oxides can induce coupled structural phase transitions through a "cooperation via competition" mechanism among local lattice distortions, challenging the notion that extreme disorder precludes such emergent phenomena.
Using molecular dynamics simulations with a machine-learned potential, this study reveals that the fracture energy of silica glass increases by up to 33% below the branching threshold due to a combination of rising intrinsic surface energy density and nanoscale roughening, demonstrating that dynamic fracture creates a fundamentally different surface structure rather than merely increasing apparent surface area.
Using first-principles calculations, this study demonstrates that coherent chiral lattice motion in metals induces significant orbital accumulation and a smaller spin accumulation, revealing that the response is primarily governed by orbital character and electron-phonon coupling rather than spin-orbit coupling alone, thereby identifying light transition metals as promising platforms for chiral-phonon-driven orbitronics.
This study demonstrates that optimal input representations for materials property prediction depend on LLM scale, with compact formats suiting smaller models and detailed descriptions benefiting larger ones, while establishing mean negative log-likelihood as an effective, training-free confidence metric for fine-tuned models.