Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities.
- DOI
- 10.1063/5.0218379
- Published
- 2024 Jul 14
- Container
- The Journal of chemical physics
- Publisher
- arXiv
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1063/5.0218379,
title = {Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities.},
author = {Grisafi A and Salanne M},
year = {2024},
journal = {The Journal of chemical physics},
doi = {10.1063/5.0218379},
url = {https://doi.org/10.1063/5.0218379}
}RIS
TY - JOUR TI - Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities. AU - Grisafi A AU - Salanne M PY - 2024 JO - The Journal of chemical physics DO - 10.1063/5.0218379 UR - https://doi.org/10.1063/5.0218379 ER -
APA
A, G., & M, S. (2024). Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities.. The Journal of chemical physics. https://doi.org/10.1063/5.0218379
Source records
- pubmed · retrieved 2026-09-27T13:12:53.283Z
- europe-pmc · retrieved 2026-09-27T13:12:53.301Z
- datacite · retrieved 2026-09-27T13:12:53.275Z
- hal · retrieved 2026-09-27T13:12:53.322Z