Rapid prediction of NMR spectral properties with quantified uncertainty

Date

2019-08-06

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

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 1H and 13C nuclei which exceeds DFT-accessible accuracy for 13C and 1H 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.

Description

open access article

Keywords

NMR, Machine learning, DFT

Citation

Jonas, E. and Kuhn, S. (2019) Rapid prediction of NMR spectral properties with quantified uncertainty. Journal of Cheminformatics 11, 50

Rights

Research Institute

Cyber Technology Institute (CTI)