2025/02/06 by Étienne Goffinet, Yan, Sen, Goffinet, Etienne +12 · 2 citations
Chemistry · Medicine · Physics and Astronomy · #Advanced MRI Techniques and Applications #Advanced NMR Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Machine Learning (cs.LG) #NMR spectroscopy and applications
paper · pdf · doi:10.48550/arxiv.2502.06845
openalex publication_date 2025/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial analytical technique used for molecular structure elucidation, with applications spanning chemistry, biology, materials science, and medicine. However, the frequency resolution of NMR spectra is limited by the "field strength" of the instrument. High-field NMR instruments provide high-resolution spectra but are prohibitively expensive, whereas lower-field instruments offer more accessible, but lower-resolution, results. This paper introduces an AI-driven approach that not only enhances the frequency resolution of NMR spectra through super-resolution techniques but also provides multi-scale functionality. By leveraging a diffusion model, our method can reconstruct high-field spectra from low-field NMR data, offering flexibility in generating spectra at varying magnetic field strengths. These reconstructions are comparable to those obtained from high-field instruments, enabling finer spectral details and improving molecular characterization. To date, our approach is one of the first to overcome the limitations of instrument field strength, achieving NMR super-resolution through AI. This cost-effective solution makes high-resolution analysis accessible to more researchers and industries, without the need for multimillion-dollar equipment.