2898 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 point-like defects significantly stabilize square-triangle quasicrystals in soft-matter systems by providing a substantial entropy gain through both individual contributions and combinatorial mixing, thereby explaining the high defect concentrations observed in these materials.
This study utilizes multi-scale simulations combining molecular dynamics and phase-field modeling to demonstrate that the discrepancy between surface and fracture energies in amorphous silica arises primarily from damage diffusion over a 16–23 Å range around the crack path, rather than plastic deformation.
This study reveals that conventional antiferromagnets with a nonzero propagation vector exhibit macroscopic symmetry breaking and hidden altermagnetic spin splitting, demonstrated through first-principles calculations on multiferroic MnS2 to propose new avenues for spintronic material design.
This study reveals that in the prototypical altermagnet -MnTe, spin-orbit coupling induces a weak ferromagnetic state dominated by a robust net orbital magnetization that significantly outweighs the spin contribution and remains stable against carrier density variations, highlighting the critical role of orbital effects in altermagnetic phenomena.
This study demonstrates the successful synthesis of a NiFe2O4/γ-Al2O3/SrTiO3 heterostructure that preserves the high-mobility two-dimensional electron gas at the interface while exhibiting robust magneto-electronic rectification and Kondo-like scattering, marking a significant step toward all-oxide spintronic applications.
By analyzing Discrete Dislocation Dynamics simulations of fcc Cu, this study reveals that dislocation link lengths on active slip systems follow a double-exponential distribution due to stress-induced bowing, whereas inactive systems exhibit a single-exponential distribution, a distinction explained by modeling the network as a one-dimensional Poisson process with super-linear growth rates for long links.
By directly applying three types of shear strain to Sr2RuO4 single crystals and observing negligible changes in the superconducting transition temperature, this study provides evidence that shear strain does not couple to superconductivity, supporting a one-component order parameter model while highlighting its inability to fully explain other experimental anomalies.
This paper presents a non-destructive, contactless frequency domain laser ultrasound method utilizing zero-group velocity guided elastic wave resonances to accurately measure the wall thickness of millimeter-sized inertial confinement fusion capsules, with results that align excellently with infrared interferometry references.
This study employs first-principles calculations and device simulations to demonstrate that lead-free () perovskites possess the necessary dynamical stability, tunable optoelectronic properties, and suitable band gaps to serve as promising candidates for stable thin-film PIN photodiode applications.
This paper proposes a robust, end-to-end differentiable fine-tuning method for pretrained machine-learning interatomic potentials that optimizes predicted structures by unrolling relaxation trajectories and backpropagating gradients, resulting in a consistent ~32% reduction in prediction error across various models and hyperparameter settings.