Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks.
- DOI
- 10.1021/acs.jctc.1c00360
- Published
- 2021 Jul 13
- 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.1c00360,
title = {Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks.},
author = {Di Pasquale N and Elliott JD and Hadjidoukas P and Carbone P},
year = {2021},
journal = {Journal of chemical theory and computation},
doi = {10.1021/acs.jctc.1c00360},
url = {https://doi.org/10.1021/acs.jctc.1c00360}
}RIS
TY - JOUR TI - Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks. AU - Di Pasquale N AU - Elliott JD AU - Hadjidoukas P AU - Carbone P PY - 2021 JO - Journal of chemical theory and computation DO - 10.1021/acs.jctc.1c00360 UR - https://doi.org/10.1021/acs.jctc.1c00360 ER -
APA
N, D. P., JD, E., P, H., & P, C. (2021). Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.1c00360
Source records
- pubmed · retrieved 2026-09-25T07:52:13.201Z