2732 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 presents a dataset-aware, entropy-maximized active learning framework that combines local entropy-driven molecular dynamics with global information filtering to efficiently generate high-quality training data for machine-learned interatomic potentials, achieving significantly lower energy errors than random sampling across diverse chemical systems with minimal DFT-labeled structures.
This paper demonstrates the successful growth of high-quality, air-stable superconducting PdTe thin films with bulk-like properties via molecular beam epitaxy using a topotactic transformation from a PdTe₂ buffer layer, establishing a promising platform for realizing topological superconductivity and Majorana zero modes.
This paper demonstrates that an extreme ultra-violet transient grating, formed by interfering free-electron laser pulses, can overcome momentum mismatch to enable the ultrafast excitation of Bloch plasmon polaritons in hyperbolic metamaterials, offering a dynamic alternative to permanent nanostructured gratings for controlling optical modes.
TriForces is a model-agnostic, three-stream framework that combines self-supervised learning with separated composition and structure representations to significantly enhance the transferability and data efficiency of atomistic graph neural networks for machine learning interatomic potentials.
This paper utilizes first-principles calculations to demonstrate that the coupled spin-optical signals in hexagonal boron nitride originate from interacting donor-acceptor pairs rather than isolated defects, revealing how their separation and charge states govern key quantum properties and offering a unified framework for designing room-temperature quantum emitters.
Using nano-ARPES, researchers demonstrate that while the momentum positioning of valence band maxima in twisted bilayer WSe2 remains fixed, the twist angle can be used to tune the energetic separation between hole bands at the K and Γ points by over 100 meV, offering a pathway to control band gaps and spin-dependent electron-phonon coupling in 2D devices.
Through an extensive study of over 7,000 ground-state structures in a 2D binary packing model, the paper reveals that while non-periodic structures dominate, approximately 35% of them exhibit "structural selectivity," a property that serves as a signature of underlying order extending well beyond the diversity limits of periodic crystals.
This paper presents an enhanced generalized phase diagram for graphene CVD growth on copper that incorporates thermal-expansion-induced strain and chemical desorption effects to predict and guide the rational synthesis of high-quality bilayer graphene by linking macroscopic growth parameters to microscopic layer-selection mechanisms.
This study utilizes a machine learning force field to reveal that SbS exhibits anisotropic crystallization driven by its quasi-1D ribbon structure, with interface-controlled growth kinetics characterized by a significantly lower activation energy than diffusion, offering key insights for optimizing its performance in data storage and photonics applications.
This study demonstrates that transferable 3D Convolutional Neural Networks, specifically the DenseNet-201 architecture, significantly outperform traditional descriptor-based models in predicting the elastic constants of nanoporous metals, achieving high accuracy () and enabling the identification of Pareto optimal designs through transfer learning and large-scale stochastic evaluation.