2692 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.
Through extensive cryogenic experiments and theoretical modeling, this study elucidates the conduction mechanisms in high-performance carbon nanotube fibers, demonstrating that heterogeneous fluctuation-induced tunneling and field-dependent transport enable them to surpass traditional metals in ultimate conductivity.
The paper introduces \texttt{MACE-Field}, an -equivariant interatomic potential that integrates a uniform electric field into the MACE backbone to accurately predict diverse inorganic materials' dielectric, ferroelectric, and spectroscopic properties through exact differentiation of a learned electric enthalpy functional.
This study reveals that Janus transition metal dichalcogenide nanotubes achieve minimum energy and exhibit anomalous optical phonon frequency peaks when their extrinsic radius matches the monolayer's intrinsic bending radius, a phenomenon driven by soft phonon modes resulting from curvature deviation.
This study utilizes machine learning and diffusion-based structure prediction to reveal that amorphous lithium difluorophosphate (\ce{LiPO2F2}), a key solid-electrolyte interphase component, exhibits high ionic conductivity due to structural disorder and abundant interstitial defects, suggesting that amorphous mixed-anion phases are the primary fast-ion pathways in Li-ion batteries.
This paper proposes and validates "data-model coevolution" as a foundational architectural principle for AI-native materials databases, demonstrating through a Li-P-S ternary prototype that endogenous generation-evaluation-refinement cycles can autonomously discover novel stable phases and achieve high-precision predictive modeling with minimal first-principles cost.
This paper proposes a new "Constraint-Modulated Viscosity Law" based on the Continuous Present Actualization premise, which outperforms standard models like VFT and MYEGA in fitting broad-window glass-forming systems by accounting for the continuous narrowing of configurational access as liquids cool.
This study combines momentum-resolved photoemission spectroscopy and DFT+U calculations to experimentally characterize the electronic band structure of the layered antiferromagnet CrPS, revealing a ligand-to-metal charge-transfer gap and distinct orbital hybridization patterns that govern its magnetic and optical properties.
This paper introduces a large-scale, aligned Nanocrystal Synthesis-Property database constructed using the LLM-enhanced NanoExtractor tool, which enables the generative inverse design of viable nanocrystal synthesis routes through the NanoDesigner model, successfully validated by experimental confirmation of both established and novel nanocrystal formulations.
The paper introduces MOF-LLM, a novel framework that enhances the spatial reasoning capabilities of a Qwen-3 8B language model through spatial-aware continual pre-training, supervised fine-tuning, and reinforcement learning to achieve state-of-the-art, high-efficiency block-level 3D structure prediction for Metal-Organic Frameworks.
MatMind is a unified generative foundation model for crystal materials science that integrates structure-activity knowledge and physics-informed feedback to surpass specialized narrow architectures in both property prediction and crystal generation tasks.