M. Yazdani-Asrami

Explainable Machine Learning for Surrogate Modeling Toward the Optimization of Multipulse Field Magnetization in HTS Bulks

High-temperature superconducting (HTS) bulks can be magnetized into trapped-field magnets (TFMs) capable of generating multitesla magnetic fields. Pulsed-field magnetization (PFM), although low-cost, suffers from heat generation and complex flux dynamics. Previous studies have highlighted the strong dependence of multi...

Black-Box and Interpretable Arti fi cial Intelligence Models for Hydrogen Uptake Across Various Metal–Organic Frameworks

Black-Box and Interpretable Artificial Intelligence Models for Hydrogen Uptake Across Various Metal–Organic Frameworks

Hydrogen (H2) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H2 remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystall...

Large language models for applied superconductivity: Towards physics-aware reasoning and integrated multi-modal generative AI

A conceptual framework is proposed in which LLMs act as high-level reasoning and knowledge-integration layers that connect experimental data, simulation outputs, literature, and expert knowledge across modalities, while numerical computation and real-time protection remain the responsibility of validated physics-based...