cond-mat.mtrl-sci

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.

Velocity-field characteristics and device performance in nanoscale amorphous oxide Thin-Film-Transistors

This paper presents a physics-based model validated by experimental data on short-channel IGZO thin-film transistors to characterize electron velocity-field behavior, accounting for trapping effects, scattering mechanisms, and thermal phenomena to achieve carrier velocities exceeding 4×10⁶ cm/s in the band, thereby providing a crucial framework for designing next-generation nanoscale oxide electronics.

Electrically switchable vacancy state revealed by in-operando positron experiments

Using in-operando positron annihilation spectroscopy on copper, this study demonstrates that electrically driven solids exhibit a reversible, non-equilibrium vacancy population driven by current-induced Frenkel-pair production rather than Joule heating alone, providing a defect-mediated mechanism for flash state phenomena.

Room-temperature third-order nonlinear anomalous Hall effect in ferromagnetic metal Fe3GaTe2

This study reports the observation of a room-temperature third-order nonlinear anomalous Hall effect in the ferromagnetic metal Fe3GaTe2, which persists up to its Curie temperature (~350 K) and is attributed to the Berry curvature quadrupole, offering new avenues for nonlinear electronic devices.