Large language models for applied superconductivity: Towards physics-aware reasoning and integrated multi-modal generative AI
Applied superconductivity research spans materials science, physics, cryogenics, and large-scale device and system engineering, generating highly fragmented data, heterogeneous models, and disconnected domain-specific knowledge representations. This fragmentation limits systematic cross-disciplinary reasoning (across experimental, computational, and operational domains), slows innovation, and hinders the translation of experimental insights into deployable technologies. Compared with traditional analytical and data-driven methods, large language models (LLMs) exhibit complementary strengths in cross-domain knowledge integration, contextual reasoning, and multimodal information fusion. This perspective examines how these capabilities may be combined with superconducting-physics constraints, symbolic representations, and structured experimental and simulation data. We propose a conceptual framework 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 models and specialised machine-learning algorithms. Potential applications include LLM-assisted superconducting materials discovery, automated fault and quench diagnostics of experimental systems and superconducting devices, smart manufacturing and intelligent quality control, and technical documentation and support. We further discuss the practical limitations of these systems, including data quality, multimodal alignment, inference latency, uncertainty, hallucination, privacy, and the need for human oversight. The paper concludes by outlining a roadmap for the trustworthy integration of LLMs into superconductivity research and engineering.