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.
Using ultrafast electron diffraction, the study reveals element-specific atomic vibrations in monolayer WSe₂ to map nonthermal lattice evolution and identify long-lasting optical phonon overpopulation as a key energy transfer mechanism.
This paper introduces the Standard Solid-State Protocols (SSSP), a rigorous methodology and open-source toolkit that automates the selection of optimal DFT simulation parameters to balance numerical precision and computational efficiency for high-throughput materials discovery.
This paper introduces charge-equilibrated TensorNet (QET), a linear-scaling, charge-aware foundation potential that overcomes the limitations of existing models by accurately capturing electrostatic interactions to predict complex ionic structures and reactive processes in energy storage and catalysis systems.
This paper derives an irreversibility condition for brittle fracture in one-dimensional elastic bars using the inverse-deformation approach and the second law of thermodynamics, proving that fixing crack locations in the reference configuration ensures local stability for all broken equilibria under hard loading.
This paper reports the discovery of a stable, metallic solid-state hydrogen storage crystal (H9@C20) formed by chemically precompressing hydrogen atoms within C20 fullerene cages under ambient conditions, achieving a hydrogen density that surpasses solid hydrogen and offering a promising pathway for high-density hydrogen storage.
Through high-throughput synthesis and computational modeling, this study demonstrates that reducing the size of ternary halide perovskite nanocrystals extends the solubility limits of Cl:Br:I mixtures, thereby stabilizing solid solutions, suppressing defects, and preventing halide segregation.
This study demonstrates that increasing LiTFSI concentration in DMC electrolytes restructures the solvation shell to promote anion-rich contact ion pairs, which accelerates early SEI nucleation and yields a denser, inorganic-rich interphase compared to conventional electrolytes.
This paper proposes BDIP-Net, a machine-learning framework that combines a cost-effective MatterSim-D3 structural optimization workflow with a dual-interaction graph neural network to efficiently generate DFT-quality bilayer structures and accurately predict their properties by explicitly modeling both intra-layer and inter-layer interactions.
This paper introduces NanoEQL, a fully interpretable white-box neural network that deciphers nanocrystal size determination mechanisms by modeling synthesis outcomes as a linear equation of crystallization, growth, and external potential capabilities, thereby overcoming the black-box limitations of traditional deep learning in chemical reaction analysis.
This paper demonstrates that physics-informed symbolic regression, utilizing the Rigid Unit Packing Fraction descriptor, successfully derives an interpretable closed-form model for predicting the glass transition temperature of alkali borate glasses with high accuracy while explicitly capturing the physical interplay between structural rigidity, dissociation energy, and network packing.