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 bachelor thesis employs density functional theory calculations to investigate the intrinsic and magnetic properties of Nd-substituted ThMn-type compounds with Zr and Ti and to identify promising Nd-poor quaternary materials as potential alternatives to rare-earth magnets.
This paper introduces aLLoyM, a fine-tuned large language model trained on alloy phase diagram data that significantly improves prediction accuracy for multiple-choice questions and demonstrates the novel capability to generate phase diagrams from component descriptions, thereby accelerating materials discovery.
This paper presents a systematic study establishing new and generalized bound relations between various electronic properties—such as electron density, effective mass, orbital magnetization, and Chern invariants—in both metallic and insulating magnetic crystals, illustrated through model systems and analyzed via optical absorption spectra.
This study establishes Hexa-graphyne (HXGY) as a stable, transparent, semimetallic 2D carbon allotrope with unique mechanical softness and distinct optical properties, highlighting its significant potential for nanoelectronic and optoelectronic applications.
This study establishes that ultra-clean RuO single crystals exhibit a weakly-correlated 3D Fermi-liquid state with a characteristic temperature-dependent magnetic susceptibility driven by enhanced orbital contributions from lattice expansion, resolving ongoing debates about its magnetic nature.
This paper presents a comprehensive benchmark comparing a GPU-accelerated finite-difference phase-field code (GPU-PF) and a CPU-parallelized finite-element adaptive-mesh code (PRISMS-PF) for simulating directional solidification of Al-Cu and SCN-camphor alloys under experimentally relevant conditions, validating their accuracy in predicting dendritic morphology and tip dynamics while evaluating their computational performance to support integrated computational materials engineering workflows.
This contribution presents a physics-guided Bayesian active learning framework that integrates a Langmuir adsorption model with a two-stage parameter estimation strategy to autonomously and efficiently optimize pulse durations in atomic layer deposition, thereby achieving faster convergence, higher prediction accuracy, and significantly reduced precursor consumption compared to conventional data-driven approaches.
This paper presents an integrated, FAIR-aligned workflow that combines version control, automated testing, structured logging, and standardized post-processing to establish a complete data provenance chain ensuring reproducibility from code development to published figures in numerical physics simulations.
This paper demonstrates that non-Abelian multigap band topology, characterized by nontrivial Euler class invariants, induces observable magnetononlinear Hall effects in tunable two-dimensional kagome metal-organic frameworks, offering a pathway to experimentally detect this uncharted topological phase through controllable magnetotransport measurements.
This paper presents a scalable, GPU-centric finite-element projector augmented-wave (PAW-FE) method that leverages algorithmic innovations like mixed-precision arithmetic and Chebyshev filtered subspace iteration to achieve significant speedups and exascale-ready performance for large-scale, chemically accurate density functional theory simulations.