FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials

Thomas Plé, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal

Open source

DOI: 10.1063/5.0217688
Published: 2024-07-25
Container: The Journal of Chemical Physics
Publisher: AIP Publishing
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1063/5.0217688,
  title = {FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials},
  author = {Thomas Plé and Olivier Adjoua and Louis Lagardère and Jean-Philip Piquemal},
  year = {2024},
  journal = {The Journal of Chemical Physics},
  doi = {10.1063/5.0217688},
  url = {https://doi.org/10.1063/5.0217688}
}

RIS

TY  - JOUR
TI  - FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials
AU  - Thomas Plé
AU  - Olivier Adjoua
AU  - Louis Lagardère
AU  - Jean-Philip Piquemal
PY  - 2024
JO  - The Journal of Chemical Physics
DO  - 10.1063/5.0217688
UR  - https://doi.org/10.1063/5.0217688
ER  -

APA

Plé, T., Adjoua, O., Lagardère, L., & Piquemal, J. (2024). FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials. The Journal of Chemical Physics. https://doi.org/10.1063/5.0217688

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crossref · retrieved 2026-09-25T10:13:36.656Z