Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29510Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Schmid, Nicolas | - |
| dc.contributor.author | Fischetti, Giulia | - |
| dc.contributor.author | Henrici, Andreas | - |
| dc.contributor.author | Wilhelm, Dirk | - |
| dc.contributor.author | Wanner, Marc | - |
| dc.contributor.author | Meshkian, Mohsen | - |
| dc.contributor.author | Bruderer, Simon | - |
| dc.contributor.author | Wegner, Jan-Dirk | - |
| dc.contributor.author | Sigel, Roland K. O. | - |
| dc.contributor.author | Heitmann, Bjoern | - |
| dc.contributor.author | Konukoglu, Ender | - |
| dc.date.accessioned | 2024-01-04T14:58:39Z | - |
| dc.date.available | 2024-01-04T14:58:39Z | - |
| dc.date.issued | 2023-07 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29510 | - |
| dc.description.abstract | Accurate extraction of multiplet parameters, such as J-couplings and chemical shifts, play a vital role in small molecule analysis using nuclear magnetic resonance (NMR) spectroscopy. These parameters provide essential quantitative information about molecular structures, interatomic interactions, and chemical environments, enabling precise characterization of small organic compounds. This poster presents an innovative omputational approach that utilizes state-of-the-art deep learning techniques, specifically detection transformers, to automate and optimize the extraction of multiplet parameters from 1D NMR spectra of small molecules. By incorporating these advanced computational methods, experimenters can achieve improved efficiency, accuracy, and speed in analyzing and characterizing small organic compounds using NMR spectroscopy. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | NMR spectroscopy | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject | Deep learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 530: Physik | de_CH |
| dc.title | Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning | de_CH |
| dc.type | Konferenz: Poster | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Angewandte Mathematik und Physik (IAMP) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-29510 | - |
| zhaw.conference.details | European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.start | 291 | de_CH |
| zhaw.parentwork.editor | Prisner, Thomas | - |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Abstract) | de_CH |
| zhaw.title.proceedings | Euromar 2022 Abstractbook | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.funding.zhaw | Maschinelles Lernen für NMR-Spektroskopie | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Schmid-etal_Transforming-NMR-Spectroscopy_EUROMAR.pdf | 774.99 kB | Adobe PDF |  View/Open |
Show simple item record
Schmid, N., Fischetti, G., Henrici, A., Wilhelm, D., Wanner, M., Meshkian, M., Bruderer, S., Wegner, J.-D., Sigel, R. K. O., Heitmann, B., & Konukoglu, E. (2023). Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning [Conference poster]. In T. Prisner (Ed.), Euromar 2022 Abstractbook (p. 291). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29510
Schmid, N. et al. (2023) ‘Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning’, in T. Prisner (ed.) Euromar 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 291. Available at: https://doi.org/10.21256/zhaw-29510.
N. Schmid et al., “Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning,” in Euromar 2022 Abstractbook, Jul. 2023, p. 291. doi: 10.21256/zhaw-29510.
SCHMID, Nicolas, Giulia FISCHETTI, Andreas HENRICI, Dirk WILHELM, Marc WANNER, Mohsen MESHKIAN, Simon BRUDERER, Jan-Dirk WEGNER, Roland K. O. SIGEL, Bjoern HEITMANN und Ender KONUKOGLU, 2023. Transforming NMR spectroscopy : extraction of multiplet parameters with deep learning. In: Thomas PRISNER (Hrsg.), Euromar 2022 Abstractbook. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Juli 2023. S. 291
Schmid, Nicolas, Giulia Fischetti, Andreas Henrici, Dirk Wilhelm, Marc Wanner, Mohsen Meshkian, Simon Bruderer, et al. 2023. “Transforming NMR Spectroscopy : Extraction of Multiplet Parameters with Deep Learning.” Conference poster. In Euromar 2022 Abstractbook, edited by Thomas Prisner, 291. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29510.
Schmid, Nicolas, et al. “Transforming NMR Spectroscopy : Extraction of Multiplet Parameters with Deep Learning.” Euromar 2022 Abstractbook, edited by Thomas Prisner, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, p. 291, https://doi.org/10.21256/zhaw-29510.
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