2772 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 demonstrates that FePt/FeRh bilayers significantly enhance heat-assisted magnetic recording efficiency by leveraging the FeRh phase transition to reduce FePt coercivity through interfacial exchange coupling and improved domain wall mobility, rather than by softening intrinsic anisotropy.
VibroML is an open-source Python toolkit that leverages machine-learned potentials and genetic algorithms to automate the remediation of dynamical instabilities, validate finite-temperature stability, and systematically explore compositional spaces, thereby transforming high-throughput materials screening from mere stability verification into a comprehensive workflow for generating physically viable crystalline structures.
This study demonstrates that a fine-tuned large language model, CrystaLLM-, can effectively utilize conditioning vectors to target and generate stable, novel MAX phase structures, thereby accelerating the discovery of precursors for MXenes through efficient computational screening.
This paper proposes a mechanical model demonstrating that modulation phasons in Ni-Mn-Ga five-layer modulated martensite act as a source of anomalous macroscopic shear compliance, effectively relaxing external shear loads in commensurate and weakly incommensurate states while explaining key lattice properties like spontaneous monoclinic distortion and twin formation.
This study extends a geometric model of incomplete solid-state phase transitions to three dimensions, demonstrating that a purely geometric memory effect—where prior transformation history alters subsequent plate-size distributions—is robust across dimensions but significantly stronger in two-dimensional systems than in three-dimensional ones.
This study demonstrates that reducing the thickness of LaNiO3 in LaNiO3/CaMnO3 superlattices drives a metal-insulator transition and orbital reconstruction that suppresses interfacial charge transfer and the Mn magnetic moment, thereby establishing a direct, tunable coupling between electronic confinement and emergent interfacial magnetism.
This study demonstrates that moderate cobalt substitution transforms the narrow-gap semiconductor FeSb into a room-temperature metallic altermagnet, with optical and theoretical evidence confirming that the resulting low-energy interband transitions and phonon anomalies arise from non-relativistic spin-split bands and enhanced electron-phonon coupling while preserving altermagnetic symmetry.
By combining machine learning classifiers with metadynamics simulations, this study reveals that the selection of specific polymorphs in Zn(imidazolate) metal-organic frameworks is determined as early as the pre-nucleation cluster stage, challenging the assumption that polymorph selection occurs later in the synthesis process.
This paper reports the first realization of strong coupling between quantized magnon modes in a sub-10-micron YIG microplatelet and microwave photons in a superconducting resonator, achieved through focused ion beam fabrication and enabling efficient on-chip studies at ultra-low input powers.
NIMS-OS is an open-source Python library and GUI application that enables fully automated, closed-loop materials research by integrating diverse AI algorithms with robotic experimental systems such as NAREE to autonomously discover new materials like electrolytes.