Rapid Prediction of NMR Spectral Properties With Quantified Uncertainty
Journal Article - Open Access
University of Chicago Chicago United States
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Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation.Here we report the development of a novel machine learning technique for accurately predicting chemical shiftsof both 1H and 13C nuclei which exceeds DFT-accessible accuracy for 13C and 1H for a subset of nuclei, while beingorders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confidentpredictions, and suggests future avenues for improved performance.
- Physical Chemistry
- Atomic and Molecular Physics and Spectroscopy