Efficient Machine Learning Prediction of Solvent‐Dependent 1H NMR Chemical Shifts in Zinc Complexes
NMR shifts in transition metal complexes, offering prediction times that are orders of magnitude faster while maintaining accuracy comparable to DFT methods, potentially accelerating the characterization and design of organometallic compounds.
Authors
- Michael Ringleb (ORCID: https://orcid.org/0000-0002-7320-8529)
- Alexander Croy (ORCID: https://orcid.org/0000-0001-9296-9350)
- Stefanie Gräfe (ORCID: https://orcid.org/0000-0002-1747-5809)
- Jyothika R. Pillay
- Ulrich S. Schubert
- Stefan Zechel
Institutions
- Helmholtz Institute Jena (DE)
- Helmholtz-Zentrum Berlin für Materialien und Energie (DE)
- Fraunhofer Institute for Applied Optics and Precision Engineering (DE)
- Friedrich Schiller University Jena (DE)
Publication Details
- Journal
- Journal of Computational Chemistry
- Published
- 2026-04-12
- DOI
- https://doi.org/10.1002/jcc.70368
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Thüringer Aufbaubank
- European Regional Development Fund