Can DFT-Trained Neural Network Potentials Reproduce Structure, Solvation, and Water-Exchange Properties in Aqueous Magnesium Solutions?
- DOI
- 10.1021/acs.jctc.6c01270
- Published
- 2026 Nov 10
- Container
- Journal of chemical theory and computation
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c01270,
title = {Can DFT-Trained Neural Network Potentials Reproduce Structure, Solvation, and Water-Exchange Properties in Aqueous Magnesium Solutions?},
author = {Falkner S and Montero de Hijes P and Dellago C and Schwierz N},
year = {2026},
journal = {Journal of chemical theory and computation},
doi = {10.1021/acs.jctc.6c01270},
url = {https://doi.org/10.1021/acs.jctc.6c01270}
}RIS
TY - JOUR TI - Can DFT-Trained Neural Network Potentials Reproduce Structure, Solvation, and Water-Exchange Properties in Aqueous Magnesium Solutions? AU - Falkner S AU - Montero de Hijes P AU - Dellago C AU - Schwierz N PY - 2026 JO - Journal of chemical theory and computation DO - 10.1021/acs.jctc.6c01270 UR - https://doi.org/10.1021/acs.jctc.6c01270 ER -
APA
S, F., P, M. D. H., C, D., & N, S. (2026). Can DFT-Trained Neural Network Potentials Reproduce Structure, Solvation, and Water-Exchange Properties in Aqueous Magnesium Solutions?. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01270
Source records
- pubmed · retrieved 2026-11-11T02:06:30.115Z