DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929707652481024 |
|---|---|
| author | Yan, Sen Goffinet, Etienne Gabellieri, Fabrizio Young, Ryan Gkoura, Lydia Jennings, Laurence Castiglione, Filippo Launey, Thomas |
| author_facet | Yan, Sen Goffinet, Etienne Gabellieri, Fabrizio Young, Ryan Gkoura, Lydia Jennings, Laurence Castiglione, Filippo Launey, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits Yan, Sen Goffinet, Etienne Gabellieri, Fabrizio Young, Ryan Gkoura, Lydia Jennings, Laurence Castiglione, Filippo Launey, Thomas Instrumentation and Detectors Artificial Intelligence Machine Learning 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. |
| title | DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits |
| topic | Instrumentation and Detectors Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.06845 |