| Autor: |
Nicholas Hindley, Stephen J. DeVience, Ella Zhang, Leo L. Cheng, Matthew S. Rosen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Magnetic Resonance Open, Vol 19, Iss , Pp 100151- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-4410 |
| DOI: |
10.1016/j.jmro.2024.100151 |
| Popis: |
The discovery of novel experimental techniques often lags behind contemporary theoretical understanding. In particular, it can be difficult to establish appropriate measurement protocols without analytic descriptions of the underlying system-of-interest. Here we propose a statistical learning framework that avoids the need for such descriptions for ergodic systems. We validate this framework by using Monte Carlo simulation and deep neural networks to learn a mapping between nuclear magnetic resonance spectra acquired on a novel low-field instrument and proton exchange rates in ethanol-water mixtures. We found that trained networks exhibited normalized-root-mean-square errors of less than 1 % for exchange rates under 150 s−1 but performed poorly for rates above this range. This differential performance occurred because low-field measurements are indistinguishable from one another for fast exchange. Nonetheless, where a discoverable relationship between indirect measurements and emergent dynamics exists, we demonstrate the possibility of approximating it in an efficient, data-driven manner. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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