Overcoming Sampling Limitations Using Machine-Learned Interatomic Potentials: The Case of Water-in-Salt Electrolytes
- DOI
- 10.1021/acs.jctc.6c00579
- Published
- 2026-09-21
- Container
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c00579,
title = {Overcoming Sampling Limitations Using Machine-Learned
Interatomic Potentials: The Case of Water-in-Salt Electrolytes},
author = {Luca Brugnoli and Mathieu Salanne and A. Marco Saitta and Alessandra Serva and Arthur France-Lanord},
year = {2026},
journal = {Journal of Chemical Theory and Computation},
doi = {10.1021/acs.jctc.6c00579},
url = {https://doi.org/10.1021/acs.jctc.6c00579}
}RIS
TY - JOUR TI - Overcoming Sampling Limitations Using Machine-Learned Interatomic Potentials: The Case of Water-in-Salt Electrolytes AU - Luca Brugnoli AU - Mathieu Salanne AU - A. Marco Saitta AU - Alessandra Serva AU - Arthur France-Lanord PY - 2026 JO - Journal of Chemical Theory and Computation DO - 10.1021/acs.jctc.6c00579 UR - https://doi.org/10.1021/acs.jctc.6c00579 ER -
APA
Brugnoli, L., Salanne, M., Saitta, A. M., Serva, A., & France-Lanord, A. (2026). Overcoming Sampling Limitations Using Machine-Learned Interatomic Potentials: The Case of Water-in-Salt Electrolytes. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c00579
Source records
- crossref · retrieved 2026-11-10T19:14:03.769Z