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 crystalline materials, have emerged as promising H2 storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H2 adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H2 uptake. This offers engineers and researchers a practical means of screening potential MOFs for H2 storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts.
R. Taylor, Shahin Alipour Bonab, M. Yazdani-Asrami· Algorithms· 0 citations
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.