3501 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 study reports the synthesis of single-crystalline quasi-two-dimensional Kondo lattice CeSnSb and demonstrates a rare inverse magnetic melting effect where low in-plane magnetic fields restore translational and rotational symmetries by transforming a fragile antiferromagnetic order and cluster glass ground state into a polarized paramagnetic phase.
This study investigates how electric-field history significantly influences the dynamics of field-induced ferroelectric phase transitions in PMN-PT compositions near the morphotropic phase boundary, revealing distinct behaviors, memory effects, and kinetic acceleration compared to compositions far from the boundary.
This study demonstrates that engineering tunable dislocation densities in KTaO3 perovskite oxides enables a novel brittle-ductile-brittle transition and allows for the simultaneous optimization of mechanical durability and functional properties, thereby overcoming traditional tradeoffs in ceramic materials.
This paper presents a framework that integrates domain-specialized small language models with AI measurement tools to enable deterministic, atomic-resolution control of scanning probe microscopy experiments, achieving high command accuracy and reliability on consumer-grade hardware while outperforming general-purpose models.
This paper introduces an atom-by-atom inverse design framework that integrates Nano-Topology Optimization with conditional diffusion models to overcome continuum limitations by explicitly accounting for crystal symmetry and surface physics, thereby enabling the discovery of high-performance metallic nanostructures like aluminum nanocantilevers and nanopillars.
This paper demonstrates how large language models and AI agents can lower the programming barrier for laboratory automation by enabling researchers to easily create custom control scripts and develop autonomous systems for operating complex scientific instruments.
This paper introduces a transfer-learning framework utilizing generative molecular language models, pretrained on extensive chemical data and fine-tuned with curated energetic materials datasets, to overcome data scarcity and accelerate the discovery of next-generation energetic materials through fragment-based encodings.
This study reports the successful synthesis and characterization of noncentrosymmetric YPtSi single crystals, revealing a weak-coupling, two-gap superconducting state with an enhanced Kadowaki-Woods ratio and the absence of charge density wave transitions, which are further supported by first-principles calculations.
This study demonstrates that despite fundamentally different interaction origins—metallic-like isotropic potentials versus entropic forces from hard polyhedra—two distinct particle systems exhibit identical complex crystallization pathways and local structural evolution because their interactions are effectively similar when mapped to an equivalent pairwise potential.
This paper demonstrates coherent, broadband microwave-to-optical transduction in the layered antiferromagnet CrSBr by leveraging strong magnon-exciton coupling at excitonic resonances, offering a scalable and efficient alternative to existing transducer technologies that often sacrifice performance for noise reduction or integrability.