Rapid prediction of NMR spectral properties with quantified uncertainty.

ABSTRACT: 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 shifts of both [Formula: see text]   and [Formula: see text] nuclei which exceeds DFT-accessible accuracy for [Formula: see text] and [Formula: see text] for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.

SUBMITTER: Jonas E 

PROVIDER: S-EPMC6684566 | biostudies-literature |

REPOSITORIES: biostudies-literature