GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture.

Picha AK, Karwounopoulos J, Erhard LC, Boresch S, Heid E

DOI: 10.1021/acs.jctc.6c01169
Published: 2026 Sep 22
Container: Journal of chemical theory and computation
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c01169,
  title = {GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture.},
  author = {Picha AK and Karwounopoulos J and Erhard LC and Boresch S and Heid E},
  year = {2026},
  journal = {Journal of chemical theory and computation},
  doi = {10.1021/acs.jctc.6c01169},
  url = {https://doi.org/10.1021/acs.jctc.6c01169}
}

RIS

TY  - JOUR
TI  - GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture.
AU  - Picha AK
AU  - Karwounopoulos J
AU  - Erhard LC
AU  - Boresch S
AU  - Heid E
PY  - 2026
JO  - Journal of chemical theory and computation
DO  - 10.1021/acs.jctc.6c01169
UR  - https://doi.org/10.1021/acs.jctc.6c01169
ER  -

APA

AK, P., J, K., LC, E., S, B., & E, H. (2026). GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01169

Source records

pubmed · retrieved 2026-11-10T11:35:04.515Z
europe-pmc · retrieved 2026-11-10T11:35:04.522Z