3454 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 first-principles study demonstrates that twisting a -bismuthene monolayer by 30 onto a planar bismuthene layer on a SiC substrate induces unique orbital hybridization and Rashba spin-splitting, resulting in a robust and tunable Quantum Spin Hall phase with enhanced topological responses.
This study demonstrates that integrating natural language embeddings of synthesis and testing conditions with structural descriptors significantly enhances the accuracy and generalizability of machine learning models for predicting glass dissolution rates, thereby accelerating the discovery of durable nuclear waste immobilization materials.
This perspective outlines a generative design framework for inorganic materials that integrates foundation AI models, multi-modal learning, and high-throughput experimental validation to enable efficient data-driven inverse design for next-generation technologies.
This paper introduces "distributional inverse homogenization," a noninvasive methodology that leverages macroscopic mechanical property measurements to statistically infer global microstructural information, bridging probability and homogenization theory to solve a challenging class of inverse problems in material science.
This paper proposes a numerically exact, system-size-independent diagrammatic quantum Monte Carlo method that unifies dynamic and static disorder to efficiently calculate transport properties, such as carrier mobility, in realistic disordered semiconductors within the thermodynamic limit.
This paper demonstrates that focused surface acoustic waves, generated via tailored interdigitated transducer designs, enable the exploration of the high-power nonlinear regime of magnon-phonon coupling and spin wave resonance, with experimental findings on symmetry tuning and power-dependent transmission validated by both analytical and micromagnetic simulations.
This study demonstrates that inserting an interlayer (Al, Cr, or Ta) in CoFeB/Pt trilayers suppresses the magnetic proximity effect-induced ferromagnetism in the Pt layer, thereby significantly reducing the total damping and highlighting the critical role of the proximity effect in spin transport parameter extraction.
This study demonstrates that incorporating high entropy alloys into bicontinuous nanoporous structures overcomes inherent macroscopic brittleness through strain hardening mechanisms like dislocation starvation and sluggish motion, resulting in materials with specific strengths 5 to 10 times higher than single-element counterparts and enhanced thermal resilience.
This study demonstrates that ferromagnetic [Co/Ni] and [Co/Pt] multilayers with perpendicular magnetic anisotropy can effectively generate spin-orbit torques comparable to platinum to drive magnetization dynamics in CoFeB layers, while also revealing a significant correlation between torque strength and the CoFeB layer thickness through inductive detection.
The paper introduces Diffusion-based Crystal Omni (DAO), a pretrain-finetune framework utilizing Siamese foundation models that significantly outperforms conventional methods in predicting crystal structures, achieving high accuracy on real-world superconductors while operating over 2,000 times faster than DFT-based approaches.