cond-mat.mtrl-sci

Element-deletion-enhanced digital image correlation for automated crack detection and tracking in lattice materials

This paper presents a global digital image correlation framework that solves the correlation problem directly on the lattice mesh with automatic element deletion and data-driven damage detection, enabling robust, high-resolution tracking of crack initiation and propagation in architected materials where traditional continuum-based optical methods fail.

Giant spontaneous Kerr effect reveals the defect origin of macroscopic time-reversal symmetry breaking in altermagnetic MnTe

This study demonstrates that giant spontaneous Kerr rotations observed in bulk $\alpha$MnTe at telecommunication wavelengths arise from carrier self-doping rather than ideal altermagnetic order, as evidenced by the absence of such signals in stoichiometric thin films.

Evolution of the Saddle Point in Antimony Telluride Homologous Superlattices

This study utilizes scanning tunneling spectroscopy and angle-resolved photoemission spectroscopy on antimony telluride homologous superlattices with two to four antimonene layers to experimentally confirm the presence of an M-point saddle point and van Hove singularity, revealing that Sb and Te $p_z$ orbital hybridization is the key mechanism driving this feature toward the Fermi level.

Expanding the extreme-k dielectric materials space through physics-validated generative reasoning

The paper introduces DielecMIND, an AI framework that combines large-language-model hypothesis generation with physics-validated first-principles calculations to successfully discover and validate five new extreme-kappa dielectric materials, thereby expanding this rare materials class by 35% and establishing a new paradigm for overcoming data scarcity in functional materials discovery.

Accelerating point defect simulations using data-driven and machine learning approaches

This paper reviews data-driven and machine learning approaches, particularly descriptor-based models and interatomic potentials trained on DFT data, that accelerate point defect simulations in solid-state materials by enabling rapid, quantum-mechanically accurate predictions of properties like formation energies and vibrational free energies for high-throughput screening and experimental integration.

Generative Discovery of Magnetic Insulators under Competing Physical Constraints

This paper introduces MagMatLLM, a constraint-guided generative framework that successfully identifies twelve previously unknown, dynamically stable magnetic insulators by integrating language-model-based crystal generation with evolutionary selection and first-principles validation to navigate the challenging, data-scarce regime of competing physical constraints.

Healing of topological defects while crystallizing nanocrystals

The two-level systems in cryogenic solids, or how to avoid stressful memories

Nanoscale Fluorescence Thermometry: Probes, Recent Advances and Emerging Directions

This review comprehensively examines fluorescence nanothermometry by detailing its fundamental mechanisms, material platforms, and recent advances while critically evaluating current challenges and outlining future strategies to enable robust, real-time temperature measurements at the nanoscale.

Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass

By combining neutron and X-ray diffraction with large-scale molecular dynamics simulations, this study demonstrates that while explicit long-range interactions improve liquid structure predictions, they remain insufficient for accurately modeling the medium-range order of silica glass, as both short-range and long-range machine learning potentials fail to capture the necessary network flexibility and ring statistics during the liquid-to-glass transition.