at Google ScholarTitle data
Gönnheimer, Nils ; Reuter, Karsten ; Margraf, Johannes T.:
Beyond Numerical Hessians : Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation.
In: Journal of Chemical Theory and Computation.
Vol. 21
(2025)
Issue 9
.
- pp. 4742-4752.
ISSN 1549-9626
DOI: https://doi.org/10.1021/acs.jctc.4c01790
Official URL: 
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Additional notes: | PMID: 40275478 |
| Institutions of the University: | Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Research Institutions > Central research institutes > Research Center for AI in Science and Society |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 10 Nov 2025 08:56 |
| Last Modified: | 10 Nov 2025 08:56 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/95158 |