4820 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 review bridges quantum phonon dynamics and mesoscopic hydrodynamics by rigorously deriving viscous heat equations from first principles to explain and predict exotic non-diffusive thermal transport phenomena, such as thermal backflow and vortices, in dielectric crystals.
This paper demonstrates a monolithic, all-optical switching mechanism for nonlinear structured light in ultrathin, engineered 3R-MoS van der Waals crystals, enabling the dynamic generation of second-harmonic vortex beams with switchable topological charges to advance integrated nanophotonic technologies.
This study reveals that mechanical strain induces distinct topological phase transitions in monolayer and bilayer ZrTe, with bilayers exhibiting a particularly rich phase diagram that makes them promising candidates for tunable topological devices using moderate strain.
The paper introduces DREAMS, a hierarchical multi-agent framework for density functional theory simulations that employs a multi-tier safety guard to verify every step and trace data provenance, thereby achieving high-accuracy, trustworthy, and near-autonomous materials research with significantly reduced error rates compared to unguarded systems.
This study integrates density functional theory with a transformer-based artificial intelligence model to predict the stability and core-shell versus surface-segregated configurations of 13-atom bimetallic nanoclusters, achieving accurate energy predictions and interpretable insights into key chemical descriptors while adhering to FAIR/TRUE principles for reproducible materials discovery.
This study evaluates the half-metallicity of Co- and Ni-based Heusler compounds lattice-matched to InAs by comparing standard DFT functionals with quasiparticle self-consistent calculations and a machine-learned DFT+ approach, ultimately identifying CoTiSn and CoZrAl as the most promising half-metallic candidates while highlighting the significant method dependence of predicted spin polarization.
Using real-time time-dependent density functional theory, this study demonstrates that circularly polarized light induces ultrafast, helicity-dependent transient magnetization in the non-magnetic insulator SrTiO through purely electronic orbital angular momentum transfer, effectively disentangling the inverse Faraday effect from optical orientation without requiring ionic motion.
This paper introduces a climate-native computational framework that combines molecular dynamics, equivariant graph neural networks, and sequential deep learning to predict the annual temperature-driven power conversion efficiency profiles of organic photovoltaic materials in tropical regions, demonstrating that modeling thermal conformational dynamics significantly outperforms static efficiency assessments for identifying suitable candidates for real-world deployment.
This paper demonstrates that transforming crystallographic data into chemically meaningful text enables fine-tuned large language models to serve as accurate, interpretable validators for metal-organic frameworks, effectively identifying structural errors and providing diagnostic rationales comparable to specialized graph-based models.
Using diffusion Quantum Monte Carlo, this study corrects hybrid DFT's erroneous prediction of a split oxygen-hole state in NaMnO by demonstrating that the localized oxygen polaron is the true ground state, thereby exposing a critical failure mode of hybrid functionals in correlated oxides that cannot be resolved by conventional X-ray absorption spectroscopy.