Rapid prediction of NMR spectral properties with quantified uncertainty
| dc.cclicence | CC-BY | en |
| dc.contributor.author | Kuhn, Stefan | |
| dc.contributor.author | Jonas, Eric | |
| dc.date.acceptance | 2018-07-29 | |
| dc.date.accessioned | 2019-08-15T09:23:26Z | |
| dc.date.available | 2019-08-15T09:23:26Z | |
| dc.date.issued | 2019-08-06 | |
| dc.description | open access article | en |
| dc.description.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. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | Jonas, E. and Kuhn, S. (2019) Rapid prediction of NMR spectral properties with quantified uncertainty. Journal of Cheminformatics 11, 50 | en |
| dc.identifier.doi | https://doi.org/10.1186/s13321-019-0374-3 | |
| dc.identifier.uri | https://www.dora.dmu.ac.uk/handle/2086/18337 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Springer | en |
| dc.researchinstitute | Cyber Technology Institute (CTI) | en |
| dc.subject | NMR | en |
| dc.subject | Machine learning | en |
| dc.subject | DFT | en |
| dc.title | Rapid prediction of NMR spectral properties with quantified uncertainty | en |
| dc.type | Article | en |