NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.

Ghosh K, Voth GA.

DOI: 10.1021/acs.jctc.6c00741
Published: 2026-08-01
Container: J Chem Theory Comput
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c00741,
  title = {NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.},
  author = {Ghosh K and  Voth GA.},
  year = {2026},
  journal = {J Chem Theory Comput},
  doi = {10.1021/acs.jctc.6c00741},
  url = {https://doi.org/10.1021/acs.jctc.6c00741}
}

RIS

TY  - JOUR
TI  - NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.
AU  - Ghosh K
AU  -  Voth GA.
PY  - 2026
JO  - J Chem Theory Comput
DO  - 10.1021/acs.jctc.6c00741
UR  - https://doi.org/10.1021/acs.jctc.6c00741
ER  -

APA

K, G., & GA., V. (2026). NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.. J Chem Theory Comput. https://doi.org/10.1021/acs.jctc.6c00741

Source records

europe-pmc · retrieved 2026-09-26T21:46:50.920Z